a d(@sjddlmZddlmZmZmZmZmZmZm Z m Z ddl Z ddl m Z ddl mZmZddlmZeeddd Zeedd d Ze e ee e ffe jd d dZe e ee e ffe jd ddZe e d ddZe e d ddZe eddddZe ee dddZd9e ee jfeedfddddZe eeed d!d"Zd:e ee jfe j j!e d$d%d&Z"d;e ee jfe j j!e d$d'd(Z#deeefeeeefeeeeefd6d7d8Z+dS)?)wraps)AnyCallableDictListOptionalTupleUnioncastN)Tensor)BetaUniform)_extract_device_dtype)freturncsttdfdd }|S)zValidate the 2D input of the wrapped function. Args: f: a function that takes the first argument as tensor. Returns: the wrapped function after input is validated. inputcsXt|stdt|t|jddt|tjtjtj gd|g|Ri|S)N Input type is not a Tensor. Got ZBCHW)required_shapesaccepted_dtypes) torch is_tensor TypeErrortype_validate_shapeshape_validate_input_dtypefloat16float32float64)rargskwargsrj/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/augmentation/utils/helpers.pywrappers  z _validate_input..wrapperrr rr'r%r$r&_validate_input s  r*csttdfdd }|S)zValidate the 3D input of the wrapped function. Args: f: a function that takes the first argument as tensor. Returns: the wrapped function after input is validated. rcslt|stdt|t|j}|dkr>td|dt|tjtj tj gd|g|Ri|S)Nrz"Expect input of 5 dimensions, got z insteadr) rrrrlenrAssertionErrorrrr r!)rr"r#Z input_shaper$r%r&r',s  z"_validate_input3d..wrapperr(r)r%r$r&_validate_input3d"s  r.)rrcCs&t|trt|d}nt|}|jSzXInfer input shape. Input may be either (tensor,) or (tensor, transform_matrix) r) isinstancetuple_transform_inputrrZtensorr%r%r&_infer_batch_shape;s r4cCs&t|trt|d}nt|}|jSr/)r0r1_transform_input3drr3r%r%r&_infer_batch_shape3dGs r6cCsnt|stdt|t|jdvr:td|jt|jdkrR|d}t|jdkrj|d}|S)zReshape an input tensor to be (*, C, H, W). Accept either (H, W), (C, H, W) or (*, C, H, W). Args: input: Tensor Returns: Tensor r)zNInput size must have a shape of either (H, W), (C, H, W) or (*, C, H, W). Got r7rr8rrrrr,r ValueErrorZ unsqueezerr%r%r&r2Ss   r2cCsnt|stdt|t|jdvr:td|jt|jdkrR|d}t|jdkrj|d}|S)zReshape an input tensor to be (*, C, D, H, W). Accept either (D, H, W), (C, D, H, W) or (*, C, D, H, W). Args: input: Tensor Returns: Tensor r)r8r9r+zWInput size must have a shape of either (D, H, W), (C, D, H, W) or (*, C, D, H, W). Got r8rr9r:rr%r%r&r5js    r5)rrrcCs$|j|vr td|d|jdS)zCheck if the dtype of the input tensor is in the range of accepted_dtypes Args: input: Tensor accepted_dtypes: List. e.g. [torch.float32, torch.float64] zExpected input of . Got N)dtyper)rrr%r%r&rs r)outputrrcCs\tt|}tt|jt|D]6}|jddkrLtd|d|jd|d}q |S)zCollapse the broadcasted batch dimensions an input tensor to be the specified shape. Args: input: Tensor shape: List/tuple of int Returns: Tensor rz Dimension z# of input is expected to be 1, got )r r ranger,rr-Zsqueeze)r>rZ out_tensorZdimr%r%r&_transform_output_shapes  rAr.)rrrcCsDd}|D]}t|t|krd}q&q|s@td|d|ddS)zCheck if the dtype of the input tensor is in the range of accepted_dtypes Args: shape: tensor shape required_shapes: List. e.g. ["BCHW", "BCDHW"] FTzExpected input shape in r<.N)r,r)rrZpassedZrequired_shaper%r%r&rsr)r channel_indexnumberrcCs|j||kS)aRValidate if an input has the right shape. e.g. to check if an input is channel first. If channel first, the second channel of an RGB input shall be fixed to 3. To verify using: _validate_input_shape(input, 1, 3) Args: input: Tensor channel_index: int number: int Returns: bool )r)rrCrDr%r%r&_validate_input_shapes rEF)rdistrcCsH|r>|dg|ddRj|dgdgt|dRS||S)zThe uniform reparameterized sampling function that accepts 'same_on_batch'. If same_on_batch is True, all values generated will be exactly same given a batch_size (shape[0]). By default, same_on_batch is set to False. r?Nr)Zrsamplerepeatr,rrF same_on_batchr%r%r&_adapted_rsamplings:rJcCsH|r>|dg|ddRj|dgdgt|dRS||S)zThe uniform sampling function that accepts 'same_on_batch'. If same_on_batch is True, all values generated will be exactly same given a batch_size (shape[0]). By default, same_on_batch is set to False. r?Nr)samplerGr,rHr%r%r&_adapted_samplings:rL)rlowhighrIrcCsftt|tr|ndt|tr |ndg\}}tj|||d}tj|||d}t||dd}t|||S)ayThe uniform sampling function that accepts 'same_on_batch'. If same_on_batch is True, all values generated will be exactly same given a batch_size (shape[0]). By default, same_on_batch is set to False. By default, sampling happens on the default device and dtype. If low/high is a tensor, sampling will happen in the same device/dtype as low/high tensor. Ndevicer=FZ validate_args)rr0r r as_tensorr rJ)rrMrNrIrPr=rFr%r%r&_adapted_uniforms"rS)rabrIrcCsftt|tr|ndt|tr |ndg\}}tj|||d}tj|||d}t||dd}t|||S)alThe beta sampling function that accepts 'same_on_batch'. If same_on_batch is True, all values generated will be exactly same given a batch_size (shape[0]). By default, same_on_batch is set to False. By default, sampling happens on the default device and dtype. If a/b is a tensor, sampling will happen in the same device/dtype as a/b tensor. NrOFrQ)rr0r rrRr rJ)rrTrUrIrPr=rFr%r%r& _adapted_betas ,rV)paramrnamercCs0|jt|kr,td|d|d|jdS)NzInvalid shape for z . Expected r<)rrSizer-)rWrrXr%r%r&_shape_validationsrZ)paramsrcCsFi}|D]4\}}t|tr2|||iq |||iq |S)zEPerform deep copy on any dict. Support tensor copying here. )itemsr0r updateclone)r[outkvr%r%r& deepcopy_dict s  rbignore)r[params_override if_none_existin_placercCs~|dur |S|r|nt|}|D]T\}}||vr>|||<q$|dkrHq$|dkrhtd|d|dq$td|dq$|S) aTOverride params dict w.r.t params_override. Args: params: source parameters. params_override: key-values to override the source parameters. if_none_exist: behaviour if the key in `params_override` does not exist in `params`. 'raise' | 'ignore'. in_place: if to override in-place or not. NrcraisezParam `z` not existed in `z`.`z` is not a valid option.)rbr\ RuntimeErrorr;)r[rdrerfr_r`rar%r%r&override_parameterss rj)r)F)F)F)F)NrcF), functoolsrtypingrrrrrrr r rr Ztorch.distributionsr r Z kornia.utilsrr*r.rYr4r6r2r5rrAstrrintboolrE distributions DistributionrJrLfloatrSrVr1listrZrbrjr%r%r%r&sj (  " "  &