



"""simple docstring"""
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_UpperCAmelCase   =	logging.get_logger("""transformers.models.speecht5""")

_UpperCAmelCase   =	{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_UpperCAmelCase   =	{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_UpperCAmelCase   =	{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_UpperCAmelCase   =	{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_UpperCAmelCase   =	{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_UpperCAmelCase   =	{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_UpperCAmelCase   =	{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_UpperCAmelCase   =	{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_UpperCAmelCase   =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase   =	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase   =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase   =	[]
_UpperCAmelCase   =	[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
_UpperCAmelCase   =	IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_UpperCAmelCase   =	IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_UpperCAmelCase   =	IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]





def    __magic_name__ (							lowercase							,	lowercase							,	lowercase							,	lowercase							,	lowercase       ):


   for attribute in key.split("""."""       ):
      SCREAMING_SNAKE_CASE_:       List[str]   	=getattr(_A							,	_A       )

   if weight_type is not None:
      SCREAMING_SNAKE_CASE_:       int   	=getattr(_A							,	_A       ).shape
   else:
      SCREAMING_SNAKE_CASE_:       Tuple   	=hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          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":
      SCREAMING_SNAKE_CASE_:       str   	=value
   elif weight_type == "weight_g":
      SCREAMING_SNAKE_CASE_:       Optional[int]   	=value
   elif weight_type == "weight_v":
      SCREAMING_SNAKE_CASE_:       Any   	=value
   elif weight_type == "bias":
      SCREAMING_SNAKE_CASE_:       str   	=value
   elif weight_type == "running_mean":
      SCREAMING_SNAKE_CASE_:       Tuple   	=value
   elif weight_type == "running_var":
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=value
   elif weight_type == "num_batches_tracked":
      SCREAMING_SNAKE_CASE_:       Tuple   	=value
   else:
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=value

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





def    __magic_name__ (							lowercase							,	lowercase       ):


   for key in ignore_keys:
      if key.endswith(""".*"""       ):
         if name.startswith(key[:-1]       ):
            return True
      elif ".*." in key:
         SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Any   	=key.split(""".*."""       )
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False





def    __magic_name__ (							lowercase							,	lowercase							,	lowercase       ):
   SCREAMING_SNAKE_CASE_:       int   	=[]

   if task == "s2t":
      SCREAMING_SNAKE_CASE_:       int   	=hf_model.speechta.encoder.prenet.feature_encoder
      SCREAMING_SNAKE_CASE_:       Optional[int]   	=MAPPING_S2T
      SCREAMING_SNAKE_CASE_:       Any   	=IGNORE_KEYS_S2T
   elif task == "t2s":
      SCREAMING_SNAKE_CASE_:       Dict   	=None
      SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=MAPPING_T2S
      SCREAMING_SNAKE_CASE_:       Any   	=IGNORE_KEYS_T2S
   elif task == "s2s":
      SCREAMING_SNAKE_CASE_:       Optional[int]   	=hf_model.speechta.encoder.prenet.feature_encoder
      SCREAMING_SNAKE_CASE_:       Optional[int]   	=MAPPING_S2S
      SCREAMING_SNAKE_CASE_:       int   	=IGNORE_KEYS_S2S
   else:
      raise ValueError(f'''Unsupported task: {task}'''       )

   for name, value in fairseq_dict.items():
      if should_ignore(_A							,	_A       ):
         logger.info(f'''{name} was ignored'''       )
         continue

      SCREAMING_SNAKE_CASE_:       List[str]   	=False
      if "conv_layers" in name:
         load_conv_layer(
             _A							,	_A							,	_A							,	_A							,	hf_model.config.feat_extract_norm == """group"""							,	)
         SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=True
      else:
         for key, mapped_key in MAPPING.items():
            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

            if "*" in key:
               SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Optional[Any]   	=key.split(""".*."""       )
               if prefix in name and suffix in name:
                  SCREAMING_SNAKE_CASE_:       Dict   	=suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
            if key in name:
               SCREAMING_SNAKE_CASE_:       Tuple   	=True
               if "*" in mapped_key:
                  SCREAMING_SNAKE_CASE_:       int   	=name.split(_A       )[0].split("""."""       )[-2]
                  SCREAMING_SNAKE_CASE_:       Dict   	=mapped_key.replace("""*"""							,	_A       )
               if "weight_g" in name:
                  SCREAMING_SNAKE_CASE_:       str   	="""weight_g"""
               elif "weight_v" in name:
                  SCREAMING_SNAKE_CASE_:       Optional[Any]   	="""weight_v"""
               elif "bias" in name:
                  SCREAMING_SNAKE_CASE_:       Any   	="""bias"""
               elif "weight" in name:
                  SCREAMING_SNAKE_CASE_:       Tuple   	="""weight"""
               elif "running_mean" in name:
                  SCREAMING_SNAKE_CASE_:       Optional[int]   	="""running_mean"""
               elif "running_var" in name:
                  SCREAMING_SNAKE_CASE_:       List[Any]   	="""running_var"""
               elif "num_batches_tracked" in name:
                  SCREAMING_SNAKE_CASE_:       str   	="""num_batches_tracked"""
               else:
                  SCREAMING_SNAKE_CASE_:       Tuple   	=None
               set_recursively(_A							,	_A							,	_A							,	_A							,	_A       )
            continue
      if not is_used:
         unused_weights.append(_A       )

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





def    __magic_name__ (							lowercase							,	lowercase							,	lowercase							,	lowercase							,	lowercase       ):
   SCREAMING_SNAKE_CASE_:       Any   	=full_name.split("""conv_layers."""       )[-1]
   SCREAMING_SNAKE_CASE_:       List[str]   	=name.split("""."""       )
   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=int(items[0]       )
   SCREAMING_SNAKE_CASE_:       List[str]   	=int(items[1]       )

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''       )
         SCREAMING_SNAKE_CASE_:       Optional[int]   	=value
         logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''       )
         SCREAMING_SNAKE_CASE_:       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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''       )
         SCREAMING_SNAKE_CASE_:       List[Any]   	=value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''       )
         SCREAMING_SNAKE_CASE_:       List[str]   	=value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
   else:
      unused_weights.append(_A       )





@torch.no_grad()
def    __magic_name__ (							lowercase							,	lowercase							,	lowercase							,	lowercase=None							,	lowercase=None							,	lowercase=None							,	):


   if config_path is not None:
      SCREAMING_SNAKE_CASE_:       Optional[int]   	=SpeechTaConfig.from_pretrained(_A       )
   else:
      SCREAMING_SNAKE_CASE_:       List[str]   	=SpeechTaConfig()

   if task == "s2t":
      SCREAMING_SNAKE_CASE_:       Tuple   	=config.max_text_positions
      SCREAMING_SNAKE_CASE_:       Dict   	=SpeechTaForSpeechToText(_A       )
   elif task == "t2s":
      SCREAMING_SNAKE_CASE_:       int   	=1876
      SCREAMING_SNAKE_CASE_:       Dict   	=600
      SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=config.max_speech_positions
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=SpeechTaForTextToSpeech(_A       )
   elif task == "s2s":
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=1876
      SCREAMING_SNAKE_CASE_:       str   	=config.max_speech_positions
      SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=SpeechTaForSpeechToSpeech(_A       )
   else:
      raise ValueError(f'''Unknown task name: {task}'''       )

   if vocab_path:
      SCREAMING_SNAKE_CASE_:       Any   	=SpeechTaTokenizer(_A							,	model_max_length=config.max_text_positions       )

      # Mask token behaves like a normal word, i.e. include the space before it
      SCREAMING_SNAKE_CASE_:       Dict   	=AddedToken("""<mask>"""							,	lstrip=_A							,	rstrip=_A       )
      SCREAMING_SNAKE_CASE_:       Optional[int]   	=mask_token
      tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
      tokenizer.add_tokens(["""<ctc_blank>"""]       )

   SCREAMING_SNAKE_CASE_:       Optional[int]   	=SpeechTaFeatureExtractor()
   SCREAMING_SNAKE_CASE_:       Optional[int]   	=SpeechTaProcessor(tokenizer=_A							,	feature_extractor=_A       )
   processor.save_pretrained(_A       )

   SCREAMING_SNAKE_CASE_:       str   	=torch.load(_A       )
   recursively_load_weights(fairseq_checkpoint["""model"""]							,	_A							,	_A       )

   model.save_pretrained(_A       )

   if repo_id:
      print("""Pushing to the hub..."""       )
      processor.push_to_hub(_A       )
      model.push_to_hub(_A       )


if __name__ == "__main__":
   _UpperCAmelCase   =	argparse.ArgumentParser()
   parser.add_argument(
       """--task""",
       default="""s2t""",
       type=str,
       help="""Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.""",
   )
   parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
   parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
   )

   _UpperCAmelCase   =	parser.parse_args()
   convert_speechta_checkpoint(
       args.task,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.vocab_path,
       args.push_to_hub,
   )




'''simple docstring'''




import unittest

import numpy as np

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

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 DPTImageProcessor






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=18       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  size if size is not None else {"""height""": 18, """width""": 18}
					lowerCAmelCase   					=  parent
					lowerCAmelCase   					=  batch_size
					lowerCAmelCase   					=  num_channels
					lowerCAmelCase   					=  image_size
					lowerCAmelCase   					=  min_resolution
					lowerCAmelCase   					=  max_resolution
					lowerCAmelCase   					=  do_resize
					lowerCAmelCase   					=  size
					lowerCAmelCase   					=  do_normalize
					lowerCAmelCase   					=  image_mean
					lowerCAmelCase   					=  image_std







			def 							a_       (   self):




					"""simple docstring"""


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







@require_torch
@require_vision
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       DPTImageProcessor if is_vision_available() else None



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  DPTImageProcessingTester(self)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  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"""))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size       ,    {"""height""": 18, """width""": 18})

					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42)
					self.assertEqual(image_processor.size       ,    {"""height""": 42, """width""": 42})



			def 							a_       (   self):




					"""simple docstring"""


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

					# Test not batched input
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase   					=  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
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase   					=  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
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )




