




'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A_ 			=		logging.get_logger(__name__)

A_ 			=		{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      for attribute in key.split('.'		):
            snake_case__					:      List[Any]  			=						getattr(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

      if weight_type is not None:
            snake_case__					:      Optional[int]  			=						getattr(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		).shape
      else:
            snake_case__					:      str  			=						hf_pointer.shape

      assert hf_shape == value.shape, (
          f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
          f" {value.shape} for {full_name}"
      )

      if weight_type == "weight":
            snake_case__					:      str  			=						value
      elif weight_type == "weight_g":
            snake_case__					:      int  			=						value
      elif weight_type == "weight_v":
            snake_case__					:      Tuple  			=						value
      elif weight_type == "bias":
            snake_case__					:      List[str]  			=						value
      else:
            snake_case__					:      Tuple  			=						value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
      snake_case__					:      str  			=						[]
      snake_case__					:      List[Any]  			=						fairseq_model.state_dict()

      snake_case__					:      Optional[int]  			=						hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case__					:      int  			=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			hf_model.config.feat_extract_norm == 'group'							,			)
                  snake_case__					:      List[str]  			=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case__					:      int  			=						'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                        if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
                              snake_case__					:      Union[str, Any]  			=						True
                              if "*" in mapped_key:
                                    snake_case__					:      Optional[int]  			=						name.split(__SCREAMING_SNAKE_CASE		)[0].split('.'		)[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    snake_case__					:      List[Any]  			=						mapped_key.replace('*'							,			__SCREAMING_SNAKE_CASE		)
                              if "weight_g" in name:
                                    snake_case__					:      Optional[Any]  			=						'weight_g'
                              elif "weight_v" in name:
                                    snake_case__					:      Optional[Any]  			=						'weight_v'
                              elif "weight" in name:
                                    snake_case__					:      Optional[Any]  			=						'weight'
                              elif "bias" in name:
                                    snake_case__					:      int  			=						'bias'
                              else:
                                    snake_case__					:      Any  			=						None
                              set_recursively(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
                        continue
            if not is_used:
                  unused_weights.append(__SCREAMING_SNAKE_CASE		)

      logger.warning(f"Unused weights: {unused_weights}"		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[str]:
      snake_case__					:      Dict  			=						full_name.split('conv_layers.'		)[-1]
      snake_case__					:      Tuple  			=						name.split('.'		)
      snake_case__					:      str  			=						int(items[0]		)
      snake_case__					:      List[Any]  			=						int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  snake_case__					:      str  			=						value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  snake_case__					:      List[str]  			=						value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  snake_case__					:      str  			=						value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  snake_case__					:      Any  			=						value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[str]:
      snake_case__					:      Tuple  			=						SEWDConfig()
      if is_finetuned:
            snake_case__					:      Optional[Any]  			=						model.wav_encoder.wav_model.cfg
      else:
            snake_case__					:      Dict  			=						model.cfg

      snake_case__					:      List[str]  			=						fs_config.conv_bias
      snake_case__					:      List[str]  			=						eval(fs_config.conv_feature_layers		)
      snake_case__					:      Union[str, Any]  			=						[x[0] for x in conv_layers]
      snake_case__					:      Any  			=						[x[1] for x in conv_layers]
      snake_case__					:      int  			=						[x[2] for x in conv_layers]
      snake_case__					:      List[Any]  			=						'gelu'
      snake_case__					:      Optional[Any]  			=						'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
      snake_case__					:      Optional[int]  			=						0.0
      snake_case__					:      Optional[Any]  			=						fs_config.activation_fn.name
      snake_case__					:      Union[str, Any]  			=						fs_config.encoder_embed_dim
      snake_case__					:      Union[str, Any]  			=						0.02
      snake_case__					:      List[Any]  			=						fs_config.encoder_ffn_embed_dim
      snake_case__					:      Optional[Any]  			=						1E-5
      snake_case__					:      Any  			=						fs_config.encoder_layerdrop
      snake_case__					:      Tuple  			=						fs_config.encoder_attention_heads
      snake_case__					:      Optional[int]  			=						fs_config.conv_pos_groups
      snake_case__					:      Optional[int]  			=						fs_config.conv_pos
      snake_case__					:      Tuple  			=						len(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      int  			=						fs_config.encoder_layers
      snake_case__					:      str  			=						fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      snake_case__					:      Any  			=						fs_config.max_position_embeddings
      snake_case__					:      Dict  			=						fs_config.position_buckets
      snake_case__					:      Dict  			=						fs_config.share_att_key
      snake_case__					:      List[str]  			=						fs_config.relative_attention
      snake_case__					:      Dict  			=						fs_config.position_biased_input
      snake_case__					:      List[str]  			=						tuple(fs_config.pos_att_type.split('|'		)		)
      snake_case__					:      str  			=						fs_config.norm_rel_ebd

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            snake_case__					:      List[str]  			=						model.cfg
            snake_case__					:      Tuple  			=						fs_config.final_dropout
            snake_case__					:      Optional[int]  			=						fs_config.layerdrop
      snake_case__					:      Optional[int]  			=						fs_config.activation_dropout
      snake_case__					:      List[str]  			=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      snake_case__					:      Union[str, Any]  			=						fs_config.attention_dropout
      snake_case__					:      Dict  			=						fs_config.dropout_input
      snake_case__					:      List[str]  			=						fs_config.dropout
      snake_case__					:      Optional[int]  			=						fs_config.mask_channel_length
      snake_case__					:      Optional[Any]  			=						fs_config.mask_channel_prob
      snake_case__					:      Tuple  			=						fs_config.mask_length
      snake_case__					:      Tuple  			=						fs_config.mask_prob

      snake_case__					:      str  			=						'Wav2Vec2FeatureExtractor'
      snake_case__					:      Union[str, Any]  			=						'Wav2Vec2CTCTokenizer'

      return config






@torch.no_grad()
def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=True		)						->      Any:

      if is_finetuned:
            snake_case__					:      Tuple  			=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,			arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
      else:
            snake_case__					:      List[Any]  			=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

      if config_path is not None:
            snake_case__					:      Optional[int]  			=						SEWDConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__					:      str  			=						convert_config(model[0]							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[Any]  			=						model[0].eval()

      snake_case__					:      Dict  			=						True if config.feat_extract_norm == 'layer' else False
      snake_case__					:      Any  			=						WavaVecaFeatureExtractor(
          feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__SCREAMING_SNAKE_CASE							,			return_attention_mask=__SCREAMING_SNAKE_CASE							,			)

      if is_finetuned:
            if dict_path:
                  snake_case__					:      Dict  			=						Dictionary.load(__SCREAMING_SNAKE_CASE		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  snake_case__					:      int  			=						target_dict.pad_index
                  snake_case__					:      Any  			=						target_dict.bos_index
                  snake_case__					:      Tuple  			=						target_dict.pad_index
                  snake_case__					:      Tuple  			=						target_dict.bos_index
                  snake_case__					:      List[str]  			=						target_dict.eos_index
                  snake_case__					:      Optional[int]  			=						len(target_dict.symbols		)
                  snake_case__					:      Optional[int]  			=						os.path.join(__SCREAMING_SNAKE_CASE							,			'vocab.json'		)
                  if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
                        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE		)		)
                        return
                  os.makedirs(__SCREAMING_SNAKE_CASE							,			exist_ok=__SCREAMING_SNAKE_CASE		)
                  with open(__SCREAMING_SNAKE_CASE							,			'w'							,			encoding='utf-8'		) as vocab_handle:
                        json.dump(target_dict.indices							,			__SCREAMING_SNAKE_CASE		)
                  snake_case__					:      List[Any]  			=						WavaVecaCTCTokenizer(
                      __SCREAMING_SNAKE_CASE							,			unk_token=target_dict.unk_word							,			pad_token=target_dict.pad_word							,			bos_token=target_dict.bos_word							,			eos_token=target_dict.eos_word							,			word_delimiter_token='|'							,			do_lower_case=__SCREAMING_SNAKE_CASE							,			)
                  snake_case__					:      List[Any]  			=						WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE							,			tokenizer=__SCREAMING_SNAKE_CASE		)
                  processor.save_pretrained(__SCREAMING_SNAKE_CASE		)

            snake_case__					:      Union[str, Any]  			=						SEWDForCTC(__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__					:      Tuple  			=						SEWDModel(__SCREAMING_SNAKE_CASE		)
            feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE		)

      recursively_load_weights(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

      hf_model.save_pretrained(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      A_ 			=		argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument(
          "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
      )
      A_ 			=		parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )






'''simple docstring'''






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 ViltImageProcessor






class 					lowercase_			(	unittest.TestCase       ):





     def __init__(    self    :				Optional[Any]    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Optional[Any]=7    ,       __lowerCamelCase    :				Optional[Any]=3    ,       __lowerCamelCase    :				int=18    ,       __lowerCamelCase    :				List[str]=30    ,       __lowerCamelCase    :				List[str]=400    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				List[str]=2    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				List[Any]=[0.5, 0.5, 0.5]    ,       __lowerCamelCase    :				Any=[0.5, 0.5, 0.5]    ,       ):
           snake_case__					:      Dict  			=						size if size is not None else {'shortest_edge': 30}
           snake_case__					:      Any  			=						parent
           snake_case__					:      int  			=						batch_size
           snake_case__					:      List[str]  			=						num_channels
           snake_case__					:      str  			=						image_size
           snake_case__					:      List[Any]  			=						min_resolution
           snake_case__					:      Dict  			=						max_resolution
           snake_case__					:      str  			=						do_resize
           snake_case__					:      str  			=						size
           snake_case__					:      List[str]  			=						size_divisor
           snake_case__					:      List[Any]  			=						do_normalize
           snake_case__					:      Optional[Any]  			=						image_mean
           snake_case__					:      Union[str, Any]  			=						image_std





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           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  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Union[str, Any]=False ):
           if not batched:
                 snake_case__					:      int  			=						self.size['shortest_edge']
                 snake_case__					:      Union[str, Any]  			=						image_inputs[0]
                 if isinstance(__lowerCamelCase    ,       Image.Image ):
                       snake_case__			,       snake_case__					:      Any  			=						image.size
                 else:
                       snake_case__			,       snake_case__					:      Optional[Any]  			=						image.shape[1], image.shape[2]
                 snake_case__					:      int  			=						size / min(__lowerCamelCase    ,       __lowerCamelCase )
                 if h < w:
                       snake_case__			,       snake_case__					:      Tuple  			=						size, scale * w
                 else:
                       snake_case__			,       snake_case__					:      List[str]  			=						scale * h, size

                 snake_case__					:      List[Any]  			=						int((1333 / 800) * size )
                 if max(__lowerCamelCase    ,       __lowerCamelCase ) > max_size:
                       snake_case__					:      Dict  			=						max_size / max(__lowerCamelCase    ,       __lowerCamelCase )
                       snake_case__					:      List[Any]  			=						newh * scale
                       snake_case__					:      Optional[Any]  			=						neww * scale

                 snake_case__			,       snake_case__					:      str  			=						int(newh + 0.5 ), int(neww + 0.5 )
                 snake_case__			,       snake_case__					:      Any  			=						(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

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

           return expected_height, expected_width






@require_torch
@require_vision
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase       ):
     A_       							= ViltImageProcessor if is_vision_available() else None





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      int  			=						ViltImageProcessingTester(self )





     @property
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           return self.image_processor_tester.prepare_image_processor_dict()





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_mean' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_std' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_normalize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_resize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size_divisor' ) )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      Optional[Any]  			=						self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 30} )

           snake_case__					:      List[str]  			=						self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42 )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 42} )





     def  _lowerCAmelCase			(    self    :				str ):
           pass





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           # Initialize image_processing
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           snake_case__					:      str  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       Image.Image )

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

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           # Initialize image_processing
           snake_case__					:      List[Any]  			=						self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       numpify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       np.ndarray )

           # Test not batched input
           snake_case__					:      List[Any]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

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

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

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





     def  _lowerCAmelCase			(    self    :				str ):
           # Initialize image_processing
           snake_case__					:      str  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       torchify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       torch.Tensor )

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

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

           # Test batched
           snake_case__					:      Optional[int]  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Tuple  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )
