



import torch
from torch import nn

class       _SCREAMING_SNAKE_CASE	(nn.Module ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Union[str, Any]=1     ,  UpperCamelCase :							Any=False				)->Union[str, Any]:
      super().__init__()

      __SCREAMING_SNAKE_CASE       :    int       =	n_token
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	d_embed
      __SCREAMING_SNAKE_CASE       :    str       =	d_proj

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	cutoffs + [n_token]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	[0] + self.cutoffs
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	div_val

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.cutoffs[0]
      __SCREAMING_SNAKE_CASE       :    int       =	len(self.cutoffs				) - 1
      __SCREAMING_SNAKE_CASE       :    int       =	self.shortlist_size + self.n_clusters

      if self.n_clusters > 0:
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	nn.Parameter(torch.zeros(self.n_clusters     ,  self.d_embed				)				)
       __SCREAMING_SNAKE_CASE       :    int       =	nn.Parameter(torch.zeros(self.n_clusters				)				)

      __SCREAMING_SNAKE_CASE       :    Any       =	nn.ModuleList()
      __SCREAMING_SNAKE_CASE       :    Any       =	nn.ParameterList()

      if div_val == 1:
       for i in range(len(self.cutoffs				)				):
        if d_proj != d_embed:
         self.out_projs.append(nn.Parameter(torch.FloatTensor(__UpperCamelCase     ,  __UpperCamelCase				)				)				)
        else:
         self.out_projs.append(__UpperCamelCase				)

       self.out_layers.append(nn.Linear(__UpperCamelCase     ,  __UpperCamelCase				)				)
      else:
       for i in range(len(self.cutoffs				)				):
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    str       =	self.cutoff_ends[i], self.cutoff_ends[i + 1]
        __SCREAMING_SNAKE_CASE       :    Tuple       =	d_embed // (div_val**i)

        self.out_projs.append(nn.Parameter(torch.FloatTensor(__UpperCamelCase     ,  __UpperCamelCase				)				)				)

        self.out_layers.append(nn.Linear(__UpperCamelCase     ,  r_idx - l_idx				)				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	keep_order





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							List[Any]				)->Tuple:
      if proj is None:
       __SCREAMING_SNAKE_CASE       :    str       =	nn.functional.linear(__UpperCamelCase     ,  __UpperCamelCase     ,  bias=__UpperCamelCase				)
      else:
       # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
       __SCREAMING_SNAKE_CASE       :    Dict       =	nn.functional.linear(__UpperCamelCase     ,  proj.t().contiguous()				)
       __SCREAMING_SNAKE_CASE       :    Tuple       =	nn.functional.linear(__UpperCamelCase     ,  __UpperCamelCase     ,  bias=__UpperCamelCase				)
       # else:
       #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
       #     if bias is not None:
       #         logit = logit + bias

      return logit





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							List[str]=None     ,  UpperCamelCase :							Union[str, Any]=False				)->Dict:

      if labels is not None:
       # Shift so that tokens < n predict n
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	hidden[..., :-1, :].contiguous()
       __SCREAMING_SNAKE_CASE       :    List[str]       =	labels[..., 1:].contiguous()
       __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden.view(-1     ,  hidden.size(-1				)				)
       __SCREAMING_SNAKE_CASE       :    Tuple       =	labels.view(-1				)
       if hidden.size(0				) != labels.size(0				):
        raise RuntimeError("Input and labels should have the same size in the batch dimension."				)
      else:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden.view(-1     ,  hidden.size(-1				)				)

      if self.n_clusters == 0:
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self._compute_logit(__UpperCamelCase     ,  self.out_layers[0].weight     ,  self.out_layers[0].bias     ,  self.out_projs[0]				)
       if labels is not None:
        __SCREAMING_SNAKE_CASE       :    List[str]       =	labels != -1_0_0
        __SCREAMING_SNAKE_CASE       :    str       =	torch.zeros_like(__UpperCamelCase     ,  dtype=hidden.dtype     ,  device=hidden.device				)
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	(
            -nn.functional.log_softmax(__UpperCamelCase     ,  dim=-1				)[mask].gather(1     ,  labels[mask].unsqueeze(1				)				).squeeze(1				)
        )
       else:
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	nn.functional.log_softmax(__UpperCamelCase     ,  dim=-1				)
      else:
       # construct weights and biases
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	[], []
       for i in range(len(self.cutoffs				)				):
        if self.div_val == 1:
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.cutoff_ends[i], self.cutoff_ends[i + 1]
         __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.out_layers[0].weight[l_idx:r_idx]
         __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.out_layers[0].bias[l_idx:r_idx]
        else:
         __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.out_layers[i].weight
         __SCREAMING_SNAKE_CASE       :    str       =	self.out_layers[i].bias

        if i == 0:
         __SCREAMING_SNAKE_CASE       :    int       =	torch.cat([weight_i, self.cluster_weight]     ,  dim=0				)
         __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.cat([bias_i, self.cluster_bias]     ,  dim=0				)

        weights.append(__UpperCamelCase				)
        biases.append(__UpperCamelCase				)

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	weights[0], biases[0], self.out_projs[0]

       __SCREAMING_SNAKE_CASE       :    str       =	self._compute_logit(__UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	nn.functional.log_softmax(__UpperCamelCase     ,  dim=1				)

       if labels is None:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden.new_empty((head_logit.size(0				), self.n_token)				)
       else:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	torch.zeros_like(__UpperCamelCase     ,  dtype=hidden.dtype     ,  device=hidden.device				)

       __SCREAMING_SNAKE_CASE       :    Tuple       =	0
       __SCREAMING_SNAKE_CASE       :    str       =	[0] + self.cutoffs
       for i in range(len(__UpperCamelCase				) - 1				):
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	cutoff_values[i], cutoff_values[i + 1]

        if labels is not None:
         __SCREAMING_SNAKE_CASE       :    List[Any]       =	(labels >= l_idx) & (labels < r_idx)
         __SCREAMING_SNAKE_CASE       :    List[Any]       =	mask_i.nonzero().squeeze()

         if indices_i.numel() == 0:
          continue

         __SCREAMING_SNAKE_CASE       :    Any       =	labels.index_select(0     ,  __UpperCamelCase				) - l_idx
         __SCREAMING_SNAKE_CASE       :    Any       =	head_logprob.index_select(0     ,  __UpperCamelCase				)
         __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden.index_select(0     ,  __UpperCamelCase				)
        else:
         __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	hidden

        if i == 0:
         if labels is not None:
          __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	head_logprob_i.gather(1     ,  target_i[:, None]				).squeeze(1				)
         else:
          __SCREAMING_SNAKE_CASE       :    Any       =	head_logprob[:, : self.cutoffs[0]]
        else:
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	weights[i], biases[i], self.out_projs[i]

         __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self._compute_logit(__UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase				)
         __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	nn.functional.log_softmax(__UpperCamelCase     ,  dim=1				)
         __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.cutoffs[0] + i - 1  # No probability for the head cluster
         if labels is not None:
          __SCREAMING_SNAKE_CASE       :    int       =	head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
              1     ,  target_i[:, None]				).squeeze(1				)
         else:
          __SCREAMING_SNAKE_CASE       :    int       =	head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
          __SCREAMING_SNAKE_CASE       :    str       =	logprob_i

        if labels is not None:
         if (hasattr(self     ,  "keep_order"				) and self.keep_order) or keep_order:
          out.index_copy_(0     ,  __UpperCamelCase     ,  -logprob_i				)
         else:
          out[offset : offset + logprob_i.size(0				)].copy_(-logprob_i				)
         offset += logprob_i.size(0				)

      return out






     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							int				)->int:
      if self.n_clusters == 0:
       __SCREAMING_SNAKE_CASE       :    int       =	self._compute_logit(__UpperCamelCase     ,  self.out_layers[0].weight     ,  self.out_layers[0].bias     ,  self.out_projs[0]				)
       return nn.functional.log_softmax(__UpperCamelCase     ,  dim=-1				)
      else:
       # construct weights and biases
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[], []
       for i in range(len(self.cutoffs				)				):
        if self.div_val == 1:
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.cutoff_ends[i], self.cutoff_ends[i + 1]
         __SCREAMING_SNAKE_CASE       :    str       =	self.out_layers[0].weight[l_idx:r_idx]
         __SCREAMING_SNAKE_CASE       :    str       =	self.out_layers[0].bias[l_idx:r_idx]
        else:
         __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.out_layers[i].weight
         __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.out_layers[i].bias

        if i == 0:
         __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.cat([weight_i, self.cluster_weight]     ,  dim=0				)
         __SCREAMING_SNAKE_CASE       :    int       =	torch.cat([bias_i, self.cluster_bias]     ,  dim=0				)

        weights.append(__UpperCamelCase				)
        biases.append(__UpperCamelCase				)

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    str       =	weights[0], biases[0], self.out_projs[0]
       __SCREAMING_SNAKE_CASE       :    Dict       =	self._compute_logit(__UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase				)

       __SCREAMING_SNAKE_CASE       :    Dict       =	hidden.new_empty((head_logit.size(0				), self.n_token)				)
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	nn.functional.log_softmax(__UpperCamelCase     ,  dim=1				)

       __SCREAMING_SNAKE_CASE       :    List[Any]       =	[0] + self.cutoffs
       for i in range(len(__UpperCamelCase				) - 1				):
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	cutoff_values[i], cutoff_values[i + 1]

        if i == 0:
         __SCREAMING_SNAKE_CASE       :    int       =	head_logprob[:, : self.cutoffs[0]]
        else:
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	weights[i], biases[i], self.out_projs[i]

         __SCREAMING_SNAKE_CASE       :    Tuple       =	self._compute_logit(__UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase     ,  __UpperCamelCase				)
         __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	nn.functional.log_softmax(__UpperCamelCase     ,  dim=1				)

         __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	head_logprob[:, -i] + tail_logprob_i
         __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	logprob_i

       return out











import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor

class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							int = 3_2     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Union[int, float] = 1 / 2_5_5     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict=7     ,  UpperCamelCase :							Dict=3_0     ,  UpperCamelCase :							Tuple=4_0_0     ,  UpperCamelCase :							Union[str, Any]=3     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	parent
      __SCREAMING_SNAKE_CASE       :    str       =	do_resize
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	size if size is not None else {"shortest_edge": 2_8_8}
      __SCREAMING_SNAKE_CASE       :    List[str]       =	size_divisor
      __SCREAMING_SNAKE_CASE       :    str       =	do_rescale
      __SCREAMING_SNAKE_CASE       :    int       =	rescale_factor
      __SCREAMING_SNAKE_CASE       :    Tuple       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_center_crop
      __SCREAMING_SNAKE_CASE       :    List[str]       =	image_mean
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_std
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	do_pad
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	batch_size
      __SCREAMING_SNAKE_CASE       :    str       =	num_channels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	min_resolution
      __SCREAMING_SNAKE_CASE       :    Tuple       =	max_resolution





     def 	__snake_case    (       self :							Optional[Any]				)->List[str]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }






     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Dict=False				)->Optional[Any]:
      if not batched:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.size["shortest_edge"]
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_inputs[0]
       if isinstance(UpperCamelCase     ,  Image.Image				):
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	image.size
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	image.shape[1], image.shape[2]
       __SCREAMING_SNAKE_CASE       :    Tuple       =	size / min(UpperCamelCase     ,  UpperCamelCase				)
       if h < w:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	size, scale * w
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	scale * h, size

       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	int((1_3_3_3 / 8_0_0) * size				)
       if max(UpperCamelCase     ,  UpperCamelCase				) > max_size:
        __SCREAMING_SNAKE_CASE       :    Any       =	max_size / max(UpperCamelCase     ,  UpperCamelCase				)
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	newh * scale
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	neww * scale

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	int(newh + 0.5				), int(neww + 0.5				)
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[]
       for image in image_inputs:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_expected_values([image]				)
        expected_values.append((expected_height, expected_width)				)
       __SCREAMING_SNAKE_CASE       :    int       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[0]				)[0]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[1]				)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			BridgeTowerImageProcessor if is_vision_available() else None





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    str       =	BridgeTowerImageProcessingTester(self				)





     @property
     def 	__snake_case    (       self :							str				)->Any:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__snake_case    (       self :							Optional[int]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_mean"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_std"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_normalize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_resize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size_divisor"				)				)





     def 	__snake_case    (       self :							Optional[int]				)->int:
      pass





     def 	__snake_case    (       self :							str				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Any       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  Image.Image				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    Any       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )





     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  numpify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  np.ndarray				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE       :    List[str]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  torchify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  torch.Tensor				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Any       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






