"""simple docstring"""







import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Tuple   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	int						)  ->							Tuple:
   __lowerCamelCase    		=					tf.image.resize(lowercase_      , (346, 346)						)
   __lowerCamelCase    		=					tf.image.crop_to_bounding_box(lowercase_      , (346 - 289) // 2      , (346 - 289) // 2      , 289      , 289						)
   return image



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
   __lowerCamelCase    		=					289
   __lowerCamelCase    		=					640
   __lowerCamelCase    		=					{'0': 'LABEL_0', '1': 'LABEL_1'}
   __lowerCamelCase    		=					{'LABEL_0': 0, 'LABEL_1': 1}
   __lowerCamelCase    		=					[]

   __lowerCamelCase    		=					BertConfig()
   __lowerCamelCase    		=					AlignConfig.from_text_vision_configs(
       text_config=lowercase_      , vision_config=lowercase_      , projection_dim=640						)
   return config



def       A__    (	)  ->							Optional[int]:
   __lowerCamelCase    		=					'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCamelCase    		=					Image.open(requests.get(lowercase_      , stream=lowercase_						).raw						)
   return im



def       A__    (	)  ->							List[Any]:
   __lowerCamelCase    		=					EfficientNetImageProcessor(
       do_center_crop=lowercase_      , rescale_factor=1 / 1_2_7.5      , rescale_offset=lowercase_      , do_normalize=lowercase_      , include_top=lowercase_      , resample=Image.BILINEAR      , )
   __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-uncased'						)
   __lowerCamelCase    		=					64
   __lowerCamelCase    		=					AlignProcessor(image_processor=lowercase_      , tokenizer=lowercase_						)
   return processor



def       A__    (	lowercase_     :	Union[str, Any]						)  ->							List[Any]:
   # EfficientNet image encoder
   __lowerCamelCase    		=					[v.split('_'						)[0].split('block'						)[1] for v in original_param_names if v.startswith('block'						)]
   __lowerCamelCase    		=					list(set(lowercase_						)						)
   __lowerCamelCase    		=					sorted(lowercase_						)
   __lowerCamelCase    		=					len(lowercase_						)
   __lowerCamelCase    		=					{b: str(lowercase_						) for b, i in zip(lowercase_      , range(lowercase_						)						)}

   __lowerCamelCase    		=					[]
   rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')						)
   rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')						)
   rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')						)
   rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')						)
   rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')						)

   for b in block_names:
      __lowerCamelCase    		=					block_name_mapping[b]
      rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')						)
      rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')						)
      rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')						)
      rename_keys.append(
          (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')						)
      rename_keys.append(
          (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')						)
      rename_keys.append(
          (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')						)
      rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')						)
      rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')						)
      rename_keys.append(
          (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')						)
      rename_keys.append(
          (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')						)

      rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')						)
      rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')						)
      rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')						)
      rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')						)
      rename_keys.append(
          (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')						)
      rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')						)
      rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')						)
      rename_keys.append(
          (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')						)
      rename_keys.append(
          (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')						)

   __lowerCamelCase    		=					{}
   for item in rename_keys:
      if item[0] in original_param_names:
         __lowerCamelCase    		=					'vision_model.' + item[1]

    # BERT text encoder
   __lowerCamelCase    		=					[]
   __lowerCamelCase    		=					'tf_bert_model/bert'
   __lowerCamelCase    		=					'text_model'
   for i in range(12						):
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
              F'''{new}.encoder.layer.{i}.attention.self.query.weight''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
              F'''{new}.encoder.layer.{i}.attention.self.query.bias''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
              F'''{new}.encoder.layer.{i}.attention.self.key.weight''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
              F'''{new}.encoder.layer.{i}.attention.self.key.bias''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
              F'''{new}.encoder.layer.{i}.attention.self.value.weight''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
              F'''{new}.encoder.layer.{i}.attention.self.value.bias''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
              F'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
              F'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
              F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
              F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
              F'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
          )						)
      rename_keys.append(
          (
              F'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
              F'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
          )						)
      rename_keys.append(
          (F'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', F'''{new}.encoder.layer.{i}.output.dense.weight''')						)
      rename_keys.append(
          (F'''{old}/encoder/layer_._{i}/output/dense/bias:0''', F'''{new}.encoder.layer.{i}.output.dense.bias''')						)
      rename_keys.append(
          (F'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')						)
      rename_keys.append(
          (F'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')						)

   rename_keys.append((F'''{old}/embeddings/word_embeddings/weight:0''', F'''{new}.embeddings.word_embeddings.weight''')						)
   rename_keys.append(
       (F'''{old}/embeddings/position_embeddings/embeddings:0''', F'''{new}.embeddings.position_embeddings.weight''')						)
   rename_keys.append(
       (F'''{old}/embeddings/token_type_embeddings/embeddings:0''', F'''{new}.embeddings.token_type_embeddings.weight''')						)
   rename_keys.append((F'''{old}/embeddings/LayerNorm/gamma:0''', F'''{new}.embeddings.LayerNorm.weight''')						)
   rename_keys.append((F'''{old}/embeddings/LayerNorm/beta:0''', F'''{new}.embeddings.LayerNorm.bias''')						)

   rename_keys.append((F'''{old}/pooler/dense/kernel:0''', F'''{new}.pooler.dense.weight''')						)
   rename_keys.append((F'''{old}/pooler/dense/bias:0''', F'''{new}.pooler.dense.bias''')						)
   rename_keys.append(('dense/kernel:0', 'text_projection.weight')						)
   rename_keys.append(('dense/bias:0', 'text_projection.bias')						)
   rename_keys.append(('dense/bias:0', 'text_projection.bias')						)
   rename_keys.append(('temperature:0', 'temperature')						)

   for item in rename_keys:
      if item[0] in original_param_names:
         __lowerCamelCase    		=					item[1]
   return key_mapping



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Optional[int]      , lowercase_     :	Union[str, Any]						)  ->							List[str]:
   list(hf_params.keys()						)

   for key, value in tf_params.items():
      if key not in key_mapping:
         continue

      __lowerCamelCase    		=					key_mapping[key]
      if "_conv" in key and "kernel" in key:
         __lowerCamelCase    		=					torch.from_numpy(lowercase_						).permute(3      , 2      , 0      , 1						)
      elif "embeddings" in key:
         __lowerCamelCase    		=					torch.from_numpy(lowercase_						)
      elif "depthwise_kernel" in key:
         __lowerCamelCase    		=					torch.from_numpy(lowercase_						).permute(2      , 3      , 0      , 1						)
      elif "kernel" in key:
         __lowerCamelCase    		=					torch.from_numpy(np.transpose(lowercase_						)						)
      elif "temperature" in key:
         __lowerCamelCase    		=					value
      elif "bn/gamma" or "bn/beta" in key:
         __lowerCamelCase    		=					torch.from_numpy(np.transpose(lowercase_						)						).squeeze()
      else:
         __lowerCamelCase    		=					torch.from_numpy(lowercase_						)

      # Replace HF parameters with original TF model parameters
      hf_params[hf_key].copy_(lowercase_						)



@torch.no_grad()
def       A__    (	lowercase_     :	Any      , lowercase_     :	List[Any]      , lowercase_     :	List[str]      , lowercase_     :	Dict						)  ->							int:
   __lowerCamelCase    		=					64
   __lowerCamelCase    		=					Tokenizer(lowercase_						)
   __lowerCamelCase    		=					align.Align('efficientnet-b7'      , 'bert-base'      , 640      , lowercase_      , tok.get_vocab_size()						)
   original_model.compile()
   original_model.load_weights(lowercase_						)

   __lowerCamelCase    		=					original_model.trainable_variables
   __lowerCamelCase    		=					original_model.non_trainable_variables
   __lowerCamelCase    		=					{param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      __lowerCamelCase    		=					param.numpy()
   __lowerCamelCase    		=					list(tf_params.keys()						)

   # Load HuggingFace model
   __lowerCamelCase    		=					get_align_config()
   __lowerCamelCase    		=					AlignModel(lowercase_						).eval()
   __lowerCamelCase    		=					hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('Converting parameters...'						)
   __lowerCamelCase    		=					rename_keys(lowercase_						)
   replace_params(lowercase_      , lowercase_      , lowercase_						)

   # Initialize processor
   __lowerCamelCase    		=					get_processor()
   __lowerCamelCase    		=					processor(
       images=prepare_img()      , text='A picture of a cat'      , padding='max_length'      , max_length=64      , return_tensors='pt'						)

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      __lowerCamelCase    		=					hf_model(**lowercase_						)

   __lowerCamelCase    		=					outputs.image_embeds.detach().numpy()
   __lowerCamelCase    		=					outputs.text_embeds.detach().numpy()

   # Original model inference
   __lowerCamelCase    		=					False
   __lowerCamelCase    		=					EfficientNetImageProcessor(
       do_center_crop=lowercase_      , do_rescale=lowercase_      , do_normalize=lowercase_      , include_top=lowercase_      , resample=Image.BILINEAR      , )
   __lowerCamelCase    		=					tf_image_processor(images=prepare_img()      , return_tensors='tf'      , data_format='channels_last'						)['pixel_values']
   __lowerCamelCase    		=					tok(tf.constant(['A picture of a cat']						)						)

   __lowerCamelCase    		=					original_model.image_encoder(lowercase_      , training=lowercase_						)
   __lowerCamelCase    		=					original_model.text_encoder(lowercase_      , training=lowercase_						)

   __lowerCamelCase    		=					tf.nn.la_normalize(lowercase_      , axis=-1						)
   __lowerCamelCase    		=					tf.nn.la_normalize(lowercase_      , axis=-1						)

   # Check whether original and HF model outputs match  -> np.allclose
   if not np.allclose(lowercase_      , lowercase_      , atol=1e-3						):
      raise ValueError('The predicted image features are not the same.'						)
   if not np.allclose(lowercase_      , lowercase_      , atol=1e-3						):
      raise ValueError('The predicted text features are not the same.'						)
   print('Model outputs match!'						)

   if save_model:
      # Create folder to save model
      if not os.path.isdir(lowercase_						):
         os.mkdir(lowercase_						)
      # Save converted model and image processor
      hf_model.save_pretrained(lowercase_						)
      processor.save_pretrained(lowercase_						)

   if push_to_hub:
      # Push model and image processor to hub
      print('Pushing converted ALIGN to the hub...'						)
      processor.push_to_hub('align-base'						)
      hf_model.push_to_hub('align-base'						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		List[Any]   =   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_path''',
         default='''./weights/model-weights''',
         type=str,
         help='''Path to the pretrained TF ALIGN checkpoint.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''hf_model''',
         type=str,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
     parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








import unittest

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 DeiTImageProcessor






class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=18  ,						SCREAMING_SNAKE_CASE_=30  ,						SCREAMING_SNAKE_CASE_=400  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						)-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      size if size is not None else {'''height''': 20, '''width''': 20}
													__UpperCamelCase  	=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      min_resolution
													__UpperCamelCase  	=      max_resolution
													__UpperCamelCase  	=      do_resize
													__UpperCamelCase  	=      size
													__UpperCamelCase  	=      do_center_crop
													__UpperCamelCase  	=      crop_size
													__UpperCamelCase  	=      do_normalize
													__UpperCamelCase  	=      image_mean
													__UpperCamelCase  	=      image_std



							def      A__		(	self      )-> int:





													'''simple docstring'''






													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													}






@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	DeiTImageProcessor if is_vision_available() else None
							_snake_case  		=	True



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      DeiTImageProcessingTester(self      )



							@property
							def      A__		(	self      )-> Any:





													'''simple docstring'''






													return self.image_processor_tester.prepare_image_processor_dict()



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_resize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''size'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_normalize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_mean'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_std'''      )      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class.from_dict(self.image_processor_dict      )
													self.assertEqual(image_processor.size  ,						{'''height''': 20, '''width''': 20}      )
													self.assertEqual(image_processor.crop_size  ,						{'''height''': 18, '''width''': 18}      )

													__UpperCamelCase  	=      self.image_processing_class.from_dict(self.image_processor_dict  ,						size=42  ,						crop_size=84      )
													self.assertEqual(image_processor.size  ,						{'''height''': 42, '''width''': 42}      )
													self.assertEqual(image_processor.crop_size  ,						{'''height''': 84, '''width''': 84}      )



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													__UpperCamelCase  	=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=SCREAMING_SNAKE_CASE_      )
													for image in image_inputs:
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						Image.Image      )

													# Test not batched input
													__UpperCamelCase  	=      image_processing(image_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													__UpperCamelCase  	=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=SCREAMING_SNAKE_CASE_  ,						numpify=SCREAMING_SNAKE_CASE_      )
													for image in image_inputs:
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						np.ndarray      )

													# Test not batched input
													__UpperCamelCase  	=      image_processing(image_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													__UpperCamelCase  	=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=SCREAMING_SNAKE_CASE_  ,						torchify=SCREAMING_SNAKE_CASE_      )
													for image in image_inputs:
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						torch.Tensor      )

													# Test not batched input
													__UpperCamelCase  	=      image_processing(image_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)




