code: 
  
  
  
  
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import MarianConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
  
  
  if is_flax_available():
     import os
  
     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     lowerCamelCase										=  'platform'
  
     import jax
     import jax.numpy as jnp
  
     from transformers import MarianTokenizer
     from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase=None,      _lowercase=None,      _lowercase=None,      _lowercase=None,      _lowercase=None,      _lowercase=None,      ):
  
  
      '''simple docstring'''
  
  
  
      if attention_mask is None:
          _lowercase     =	np.where(input_ids != config.pad_token_id,      1,      0      )
      if decoder_attention_mask is None:
          _lowercase     =	np.where(decoder_input_ids != config.pad_token_id,      1,      0      )
      if head_mask is None:
          _lowercase     =	np.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
          _lowercase     =	np.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
          _lowercase     =	np.ones((config.decoder_layers, config.decoder_attention_heads)      )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }
  
  class 					snake_case_							:
  
     def __init__(    self:	int   ,       snake_case__:	str   ,       snake_case__:	Tuple=13   ,       snake_case__:	List[Any]=7   ,       snake_case__:	Optional[int]=True   ,       snake_case__:	Any=False   ,       snake_case__:	Optional[Any]=99   ,       snake_case__:	Optional[Any]=16   ,       snake_case__:	Dict=2   ,       snake_case__:	int=4   ,       snake_case__:	Optional[Any]=4   ,       snake_case__:	Union[str, Any]="gelu"   ,       snake_case__:	Dict=0.1   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	str=32   ,       snake_case__:	str=2   ,       snake_case__:	Dict=1   ,       snake_case__:	Any=0   ,       snake_case__:	str=0.02   ,       )  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	parent
         _lowercase     =	batch_size
         _lowercase     =	seq_length
         _lowercase     =	is_training
         _lowercase     =	use_labels
         _lowercase     =	vocab_size
         _lowercase     =	hidden_size
         _lowercase     =	num_hidden_layers
         _lowercase     =	num_attention_heads
         _lowercase     =	intermediate_size
         _lowercase     =	hidden_act
         _lowercase     =	hidden_dropout_prob
         _lowercase     =	attention_probs_dropout_prob
         _lowercase     =	max_position_embeddings
         _lowercase     =	eos_token_id
         _lowercase     =	pad_token_id
         _lowercase     =	bos_token_id
         _lowercase     =	initializer_range
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,       self.vocab_size	)   ,       3   ,       self.vocab_size	)
         _lowercase     =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,       dtype=np.intaa	))   ,       -1	)
  
         _lowercase     =	shift_tokens_right(snake_case__   ,       1   ,       2	)
  
         _lowercase     =	MarianConfig(
             vocab_size=self.vocab_size   ,       d_model=self.hidden_size   ,       encoder_layers=self.num_hidden_layers   ,       decoder_layers=self.num_hidden_layers   ,       encoder_attention_heads=self.num_attention_heads   ,       decoder_attention_heads=self.num_attention_heads   ,       encoder_ffn_dim=self.intermediate_size   ,       decoder_ffn_dim=self.intermediate_size   ,       dropout=self.hidden_dropout_prob   ,       attention_dropout=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       eos_token_id=self.eos_token_id   ,       bos_token_id=self.bos_token_id   ,       pad_token_id=self.pad_token_id   ,       initializer_range=self.initializer_range   ,       use_cache=snake_case__   ,       )
         _lowercase     =	prepare_marian_inputs_dict(snake_case__   ,       snake_case__   ,       snake_case__	)
         return config, inputs_dict
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.prepare_config_and_inputs()
         return config, inputs_dict
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]   ,       snake_case__:	Tuple   ,       snake_case__:	str   ,       snake_case__:	Optional[int]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	20
         _lowercase     =	model_class_name(snake_case__	)
  
         _lowercase     =	model.encode(inputs_dict['input_ids']	)
  
         _lowercase ,    _lowercase     =	(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )
  
         _lowercase     =	model.init_cache(decoder_input_ids.shape[0]   ,       snake_case__   ,       snake_case__	)
         _lowercase     =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,       dtype='i4'	)
  
         _lowercase     =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]   ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,       )
         _lowercase     =	model.decode(
             decoder_input_ids[:, :-1]   ,       snake_case__   ,       decoder_attention_mask=snake_case__   ,       past_key_values=snake_case__   ,       decoder_position_ids=snake_case__   ,       )
  
         _lowercase     =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,       dtype='i4'	)
         _lowercase     =	model.decode(
             decoder_input_ids[:, -1:]   ,       snake_case__   ,       decoder_attention_mask=snake_case__   ,       past_key_values=outputs_cache.past_key_values   ,       decoder_position_ids=snake_case__   ,       )
  
         _lowercase     =	model.decode(snake_case__   ,       snake_case__	)
  
         _lowercase     =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
         self.parent.assertTrue(diff < 1E-3   ,       msg=f'''Max diff is {diff}'''	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	List[str]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Optional[int]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	20
         _lowercase     =	model_class_name(snake_case__	)
  
         _lowercase     =	model.encode(inputs_dict['input_ids']	)
  
         _lowercase ,    _lowercase     =	(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )
  
         _lowercase     =	jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
             ]   ,       axis=-1   ,       )
  
         _lowercase     =	model.init_cache(decoder_input_ids.shape[0]   ,       snake_case__   ,       snake_case__	)
         _lowercase     =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]   ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,       )
  
         _lowercase     =	model.decode(
             decoder_input_ids[:, :-1]   ,       snake_case__   ,       decoder_attention_mask=snake_case__   ,       past_key_values=snake_case__   ,       decoder_position_ids=snake_case__   ,       )
         _lowercase     =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,       dtype='i4'	)
         _lowercase     =	model.decode(
             decoder_input_ids[:, -1:]   ,       snake_case__   ,       past_key_values=outputs_cache.past_key_values   ,       decoder_attention_mask=snake_case__   ,       decoder_position_ids=snake_case__   ,       )
  
         _lowercase     =	model.decode(snake_case__   ,       snake_case__   ,       decoder_attention_mask=snake_case__	)
  
         _lowercase     =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
         self.parent.assertTrue(diff < 1E-3   ,       msg=f'''Max diff is {diff}'''	)
  
  
  
  
  
  
  @require_flax
  class 					snake_case_							(       UpperCAmelCase_      ,			unittest.TestCase      ,			UpperCAmelCase_      ):
     __lowerCAmelCase      =						True
     __lowerCAmelCase      =						(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     __lowerCAmelCase      =						(FlaxMarianMTModel,) if is_flax_available() else ()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	FlaxMarianModelTester(self	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(snake_case__   ,       snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(snake_case__   ,       snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__	):
                 _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__	)
                 _lowercase     =	model_class(snake_case__	)
  
                 @jax.jit
                 def encode_jitted(snake_case__:	int   ,       snake_case__:	Union[str, Any]=None   ,       **snake_case__:	Optional[Any]	):
                     return model.encode(input_ids=snake_case__   ,       attention_mask=snake_case__	)
  
                 with self.subTest('JIT Enabled'	):
                     _lowercase     =	encode_jitted(**snake_case__	).to_tuple()
  
                 with self.subTest('JIT Disabled'	):
                     with jax.disable_jit():
                         _lowercase     =	encode_jitted(**snake_case__	).to_tuple()
  
                 self.assertEqual(len(snake_case__	)   ,       len(snake_case__	)	)
                 for jitted_output, output in zip(snake_case__   ,       snake_case__	):
                     self.assertEqual(jitted_output.shape   ,       output.shape	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__	):
                 _lowercase     =	model_class(snake_case__	)
                 _lowercase     =	model.encode(inputs_dict['input_ids']   ,       inputs_dict['attention_mask']	)
  
                 _lowercase     =	{
                     'decoder_input_ids': inputs_dict['decoder_input_ids'],
                     'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                     'encoder_outputs': encoder_outputs,
                 }
  
                 @jax.jit
                 def decode_jitted(snake_case__:	Dict   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Tuple	):
                     return model.decode(
                         decoder_input_ids=snake_case__   ,       decoder_attention_mask=snake_case__   ,       encoder_outputs=snake_case__   ,       )
  
                 with self.subTest('JIT Enabled'	):
                     _lowercase     =	decode_jitted(**snake_case__	).to_tuple()
  
                 with self.subTest('JIT Disabled'	):
                     with jax.disable_jit():
                         _lowercase     =	decode_jitted(**snake_case__	).to_tuple()
  
                 self.assertEqual(len(snake_case__	)   ,       len(snake_case__	)	)
                 for jitted_output, output in zip(snake_case__   ,       snake_case__	):
                     self.assertEqual(jitted_output.shape   ,       output.shape	)
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         for model_class_name in self.all_model_classes:
             _lowercase     =	model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'	)
             # FlaxMarianForSequenceClassification expects eos token in input_ids
             _lowercase     =	np.ones((1, 1)	) * model.config.eos_token_id
             _lowercase     =	model(snake_case__	)
             self.assertIsNotNone(snake_case__	)
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       unittest.TestCase      ):
     __lowerCAmelCase      =						None
     __lowerCAmelCase      =						None
  
     @classmethod
     def 			SCREAMING_SNAKE_CASE__				(    cls:	int	)  ->		None:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
         return cls
  
     @cached_property
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         return MarianTokenizer.from_pretrained(self.model_name	)
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         return self.tokenizer.eos_token_id
  
     @cached_property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	FlaxMarianMTModel.from_pretrained(self.model_name	)
         self.assertEqual(model.config.decoder_start_token_id   ,       model.config.pad_token_id	)
         return model
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       **snake_case__:	Dict	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.translate_src_text(**snake_case__	)
         self.assertListEqual(self.expected_text   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       **snake_case__:	Optional[Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.tokenizer(self.src_text   ,       padding=snake_case__   ,       return_tensors='np'   ,       **snake_case__	)
         _lowercase     =	self.model.generate(
             model_inputs.input_ids   ,       attention_mask=model_inputs.attention_mask   ,       num_beams=2   ,       max_length=128   ,       ).sequences
         _lowercase     =	self.tokenizer.batch_decode(snake_case__   ,       skip_special_tokens=snake_case__	)
         return generated_words
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       UpperCAmelCase_      ):
     __lowerCAmelCase      =						"""en"""
     __lowerCAmelCase      =						"""fr"""
     __lowerCAmelCase      =						[
         """I am a small frog.""",
         """Now I can forget the 100 words of german that I know.""",
     ]
     __lowerCAmelCase      =						[
         """Je suis une petite grenouille.""",
         """Maintenant, je peux oublier les 100 mots d\'allemand que je connais.""",
     ]
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       UpperCAmelCase_      ):
     __lowerCAmelCase      =						"""fr"""
     __lowerCAmelCase      =						"""en"""
     __lowerCAmelCase      =						[
         """Donnez moi le micro.""",
         """Tom et Mary étaient assis à une table.""",  # Accents
     ]
     __lowerCAmelCase      =						[
         """Give me the microphone.""",
         """Tom and Mary were sitting at a table.""",
     ]
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       UpperCAmelCase_      ):
     __lowerCAmelCase      =						"""mt"""
     __lowerCAmelCase      =						"""en"""
     __lowerCAmelCase      =						["""Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
     __lowerCAmelCase      =						["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       UpperCAmelCase_      ):
     __lowerCAmelCase      =						"""en"""
     __lowerCAmelCase      =						"""de"""
     __lowerCAmelCase      =						[
         """I am a small frog.""",
         """Now I can forget the 100 words of german that I know.""",
         """Tom asked his teacher for advice.""",
         """That\'s how I would do it.""",
         """Tom really admired Mary\'s courage.""",
         """Turn around and close your eyes.""",
     ]
     __lowerCAmelCase      =						[
         """Ich bin ein kleiner Frosch.""",
         """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
         """Tom bat seinen Lehrer um Rat.""",
         """So würde ich das machen.""",
         """Tom bewunderte Marias Mut wirklich.""",
         """Drehen Sie sich um und schließen Sie die Augen.""",
     ]
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       UpperCAmelCase_      ):
     __lowerCAmelCase      =						"""en"""
     __lowerCAmelCase      =						"""zh"""
     __lowerCAmelCase      =						["""My name is Wolfgang and I live in Berlin"""]
     __lowerCAmelCase      =						["""我叫沃尔夫冈 我住在柏林"""]
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       UpperCAmelCase_      ):
     __lowerCAmelCase      =						"""ru"""
     __lowerCAmelCase      =						"""fr"""
     __lowerCAmelCase      =						["""Он показал мне рукопись своей новой пьесы."""]
     __lowerCAmelCase      =						["""Il m\'a montré le manuscrit de sa nouvelle pièce."""]
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 					snake_case_							(       UpperCAmelCase_      ):
     __lowerCAmelCase      =						"""en"""
     __lowerCAmelCase      =						"""ROMANCE"""
     __lowerCAmelCase      =						[
         """>>fr<< Don\'t spend so much time watching TV.""",
         """>>pt<< Your message has been sent.""",
         """>>es<< He\'s two years older than me.""",
     ]
     __lowerCAmelCase      =						[
         """Ne passez pas autant de temps à regarder la télé.""",
         """A sua mensagem foi enviada.""",
         """Es dos años más viejo que yo.""",
     ]
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         self._assert_generated_batch_equal_expected()
  
style_context: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
     import PIL
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						["""pixel_values"""]
  
     def __init__(    self:	int   ,       snake_case__:	bool = True   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	bool = False   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[int, float] = 1 / 255   ,       snake_case__:	bool = False   ,       snake_case__:	bool = True   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = True   ,       **snake_case__:	Optional[Any]   ,       )  ->		None:
  
  
         """simple docstring"""
  
  
  
         super().__init__(**snake_case__	)
         _lowercase     =	size if size is not None else {'height': 346, 'width': 346}
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else {'height': 289, 'width': 289}
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	do_resize
         _lowercase     =	size
         _lowercase     =	resample
         _lowercase     =	do_center_crop
         _lowercase     =	crop_size
         _lowercase     =	do_rescale
         _lowercase     =	rescale_factor
         _lowercase     =	rescale_offset
         _lowercase     =	do_normalize
         _lowercase     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _lowercase     =	image_std if image_std is not None else IMAGENET_STANDARD_STD
         _lowercase     =	include_top
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Optional[int]   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return resize(
             snake_case__   ,       size=(size['height'], size['width'])   ,       resample=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	str   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return center_crop(snake_case__   ,       size=(size['height'], size['width'])   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[int, float]   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	List[str]   ,       )  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if offset:
             _lowercase     =	(image - 1_27.5) * scale
             if data_format is not None:
                 _lowercase     =	to_channel_dimension_format(snake_case__   ,       snake_case__	)
             _lowercase     =	rescaled_image.astype(np.floataa	)
         else:
             _lowercase     =	rescale(snake_case__   ,       scale=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
         return rescaled_image
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Any   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         return normalize(snake_case__   ,       mean=snake_case__   ,       std=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	ImageInput   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	bool = None   ,       snake_case__:	float = None   ,       snake_case__:	bool = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[str, TensorType]] = None   ,       snake_case__:	ChannelDimension = ChannelDimension.FIRST   ,       **snake_case__:	int   ,       )  ->		PIL.Image.Image:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	do_resize if do_resize is not None else self.do_resize
         _lowercase     =	resample if resample is not None else self.resample
         _lowercase     =	do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase     =	do_rescale if do_rescale is not None else self.do_rescale
         _lowercase     =	rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase     =	rescale_offset if rescale_offset is not None else self.rescale_offset
         _lowercase     =	do_normalize if do_normalize is not None else self.do_normalize
         _lowercase     =	image_mean if image_mean is not None else self.image_mean
         _lowercase     =	image_std if image_std is not None else self.image_std
         _lowercase     =	include_top if include_top is not None else self.include_top
  
         _lowercase     =	size if size is not None else self.size
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else self.crop_size
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	make_list_of_images(snake_case__	)
  
         if not valid_images(snake_case__	):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'	)
  
         if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'	)
  
         if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'	)
  
         if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'	)
  
         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'	)
  
         # All transformations expect numpy arrays.
         _lowercase     =	[to_numpy_array(snake_case__	) for image in images]
  
         if do_resize:
             _lowercase     =	[self.resize(image=snake_case__   ,       size=snake_case__   ,       resample=snake_case__	) for image in images]
  
         if do_center_crop:
             _lowercase     =	[self.center_crop(image=snake_case__   ,       size=snake_case__	) for image in images]
  
         if do_rescale:
             _lowercase     =	[self.rescale(image=snake_case__   ,       scale=snake_case__   ,       offset=snake_case__	) for image in images]
  
         if do_normalize:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=snake_case__   ,       std=snake_case__	) for image in images]
  
         if include_top:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=[0, 0, 0]   ,       std=snake_case__	) for image in images]
  
         _lowercase     =	[to_channel_dimension_format(snake_case__   ,       snake_case__	) for image in images]
  
         _lowercase     =	{'pixel_values': images}
         return BatchFeature(data=snake_case__   ,       tensor_type=snake_case__	)
  