






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()








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__	)
