


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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase=None	,	__UpperCamelCase=None	,	__UpperCamelCase=None	,	__UpperCamelCase=None	,	__UpperCamelCase=None	,	__UpperCamelCase=None	,	):
     if attention_mask is None:
          a_     :  List[Any]				      =     np.where(input_ids != config.pad_token_id	,	1	,	0			)
     if decoder_attention_mask is None:
          a_     :  Dict				      =     np.where(decoder_input_ids != config.pad_token_id	,	1	,	0			)
     if head_mask is None:
          a_     :  Optional[Any]				      =     np.ones((config.encoder_layers, config.encoder_attention_heads)			)
     if decoder_head_mask is None:
          a_     :  Optional[Any]				      =     np.ones((config.decoder_layers, config.decoder_attention_heads)			)
     if cross_attn_head_mask is None:
          a_     :  Union[str, Any]				      =     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 	a__						:

       def __init__( self   :  Dict   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[int]=1_3   ,				lowerCamelCase_   :  List[str]=7   ,				lowerCamelCase_   :  Optional[Any]=True   ,				lowerCamelCase_   :  str=False   ,				lowerCamelCase_   :  str=9_9   ,				lowerCamelCase_   :  Optional[int]=1_6   ,				lowerCamelCase_   :  List[Any]=2   ,				lowerCamelCase_   :  Union[str, Any]=4   ,				lowerCamelCase_   :  str=4   ,				lowerCamelCase_   :  int="gelu"   ,				lowerCamelCase_   :  Optional[int]=0.1   ,				lowerCamelCase_   :  Dict=0.1   ,				lowerCamelCase_   :  Tuple=3_2   ,				lowerCamelCase_   :  List[Any]=2   ,				lowerCamelCase_   :  Any=1   ,				lowerCamelCase_   :  List[str]=0   ,				lowerCamelCase_   :  str=0.0_2   ,				):
            a_     :  Tuple				      =     parent
            a_     :  List[Any]				      =     batch_size
            a_     :  Optional[int]				      =     seq_length
            a_     :  Any				      =     is_training
            a_     :  Dict				      =     use_labels
            a_     :  Any				      =     vocab_size
            a_     :  List[str]				      =     hidden_size
            a_     :  List[Any]				      =     num_hidden_layers
            a_     :  int				      =     num_attention_heads
            a_     :  List[str]				      =     intermediate_size
            a_     :  str				      =     hidden_act
            a_     :  Tuple				      =     hidden_dropout_prob
            a_     :  int				      =     attention_probs_dropout_prob
            a_     :  Optional[int]				      =     max_position_embeddings
            a_     :  List[str]				      =     eos_token_id
            a_     :  Optional[Any]				      =     pad_token_id
            a_     :  Optional[int]				      =     bos_token_id
            a_     :  Optional[int]				      =     initializer_range

       def     UpperCAmelCase( self   :  str     ):
            a_     :  str				      =     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size     )   ,				3   ,				self.vocab_size     )
            a_     :  List[Any]				      =     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,				dtype=np.intaa     ))   ,				-1     )

            a_     :  Any				      =     shift_tokens_right(lowercase_   ,				1   ,				2     )

            a_     :  int				      =     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=lowercase_   ,				)
            a_     :  Union[str, Any]				      =     prepare_marian_inputs_dict(lowercase_   ,				lowercase_   ,				lowercase_     )
            return config, inputs_dict

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  Tuple				      =     self.prepare_config_and_inputs()
            return config, inputs_dict

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Tuple     ):
            a_     :  Dict				      =     2_0
            a_     :  Optional[int]				      =     model_class_name(lowercase_     )

            a_     :  str				      =     model.encode(inputs_dict["""input_ids"""]     )

            a_     :  Any				      =     (
                inputs_dict["""decoder_input_ids"""],
                inputs_dict["""decoder_attention_mask"""],
            )

            a_     :  Dict				      =     model.init_cache(decoder_input_ids.shape[0]   ,				lowercase_   ,				lowercase_     )
            a_     :  Dict				      =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,				dtype="""i4"""     )

            a_     :  Union[str, Any]				      =     jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]   ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,				)
            a_     :  List[Any]				      =     model.decode(
                decoder_input_ids[:, :-1]   ,				lowercase_   ,				decoder_attention_mask=lowercase_   ,				past_key_values=lowercase_   ,				decoder_position_ids=lowercase_   ,				)

            a_     :  Tuple				      =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,				dtype="""i4"""     )
            a_     :  List[str]				      =     model.decode(
                decoder_input_ids[:, -1:]   ,				lowercase_   ,				decoder_attention_mask=lowercase_   ,				past_key_values=outputs_cache.past_key_values   ,				decoder_position_ids=lowercase_   ,				)

            a_     :  Optional[int]				      =     model.decode(lowercase_   ,				lowercase_     )

            a_     :  List[str]				      =     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     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  List[Any]     ):
            a_     :  str				      =     2_0
            a_     :  int				      =     model_class_name(lowercase_     )

            a_     :  str				      =     model.encode(inputs_dict["""input_ids"""]     )

            a_     :  Any				      =     (
                inputs_dict["""decoder_input_ids"""],
                inputs_dict["""decoder_attention_mask"""],
            )

            a_     :  Tuple				      =     jnp.concatenate(
                [
                    decoder_attention_mask,
                    jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
                ]   ,				axis=-1   ,				)

            a_     :  int				      =     model.init_cache(decoder_input_ids.shape[0]   ,				lowercase_   ,				lowercase_     )
            a_     :  Dict				      =     jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]   ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,				)

            a_     :  List[Any]				      =     model.decode(
                decoder_input_ids[:, :-1]   ,				lowercase_   ,				decoder_attention_mask=lowercase_   ,				past_key_values=lowercase_   ,				decoder_position_ids=lowercase_   ,				)
            a_     :  Any				      =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,				dtype="""i4"""     )
            a_     :  Dict				      =     model.decode(
                decoder_input_ids[:, -1:]   ,				lowercase_   ,				past_key_values=outputs_cache.past_key_values   ,				decoder_attention_mask=lowercase_   ,				decoder_position_ids=lowercase_   ,				)

            a_     :  str				      =     model.decode(lowercase_   ,				lowercase_   ,				decoder_attention_mask=lowercase_     )

            a_     :  Optional[int]				      =     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 	a__						(    _UpperCAmelCase    ,							unittest.TestCase    ,							_UpperCAmelCase     ):
       lowerCamelCase__:  List[str]				     =  True
       lowerCamelCase__:  int				     =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
       lowerCamelCase__:  Optional[int]				     =  (FlaxMarianMTModel,) if is_flax_available() else ()

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            a_     :  List[Any]				      =     FlaxMarianModelTester(self     )

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Optional[int]				      =     self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowercase_   ,				lowercase_   ,				lowercase_     )

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            a_     :  int				      =     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(lowercase_   ,				lowercase_   ,				lowercase_     )

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  Tuple				      =     self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__     ):
                      a_     :  Optional[int]				      =     self._prepare_for_class(lowercase_   ,				lowercase_     )
                      a_     :  Optional[int]				      =     model_class(lowercase_     )

                      @jax.jit
                      def encode_jitted(lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Union[str, Any]=None   ,				**lowerCamelCase_   :  Tuple     ):
                           return model.encode(input_ids=lowercase_   ,				attention_mask=lowercase_     )

                      with self.subTest("""JIT Enabled"""     ):
                           a_     :  Optional[int]				      =     encode_jitted(**lowercase_     ).to_tuple()

                      with self.subTest("""JIT Disabled"""     ):
                           with jax.disable_jit():
                                a_     :  Dict				      =     encode_jitted(**lowercase_     ).to_tuple()

                      self.assertEqual(len(lowercase_     )   ,				len(lowercase_     )     )
                      for jitted_output, output in zip(lowercase_   ,				lowercase_     ):
                           self.assertEqual(jitted_output.shape   ,				output.shape     )

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  Any				      =     self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__     ):
                      a_     :  str				      =     model_class(lowercase_     )
                      a_     :  str				      =     model.encode(inputs_dict["""input_ids"""]   ,				inputs_dict["""attention_mask"""]     )

                      a_     :  str				      =     {
                          """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(lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Any     ):
                           return model.decode(
                               decoder_input_ids=lowercase_   ,				decoder_attention_mask=lowercase_   ,				encoder_outputs=lowercase_   ,				)

                      with self.subTest("""JIT Enabled"""     ):
                           a_     :  Any				      =     decode_jitted(**lowercase_     ).to_tuple()

                      with self.subTest("""JIT Disabled"""     ):
                           with jax.disable_jit():
                                a_     :  Optional[Any]				      =     decode_jitted(**lowercase_     ).to_tuple()

                      self.assertEqual(len(lowercase_     )   ,				len(lowercase_     )     )
                      for jitted_output, output in zip(lowercase_   ,				lowercase_     ):
                           self.assertEqual(jitted_output.shape   ,				output.shape     )

       @slow
       def     UpperCAmelCase( self   :  Optional[Any]     ):
            for model_class_name in self.all_model_classes:
                 a_     :  Optional[int]				      =     model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""     )
                 # FlaxMarianForSequenceClassification expects eos token in input_ids
                 a_     :  Any				      =     np.ones((1, 1)     ) * model.config.eos_token_id
                 a_     :  int				      =     model(lowercase_     )
                 self.assertIsNotNone(lowercase_     )


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    unittest.TestCase     ):
       lowerCamelCase__:  Tuple				     =  None
       lowerCamelCase__:  Dict				     =  None

       @classmethod
       def     UpperCAmelCase( cls   :  Optional[Any]     ):
            a_     :  str				      =     F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
            return cls

       @cached_property
       def     UpperCAmelCase( self   :  int     ):
            return MarianTokenizer.from_pretrained(self.model_name     )

       @property
       def     UpperCAmelCase( self   :  List[Any]     ):
            return self.tokenizer.eos_token_id

       @cached_property
       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  FlaxMarianMTModel				      =     FlaxMarianMTModel.from_pretrained(self.model_name     )
            self.assertEqual(model.config.decoder_start_token_id   ,				model.config.pad_token_id     )
            return model

       def     UpperCAmelCase( self   :  str   ,				**lowerCamelCase_   :  Union[str, Any]     ):
            a_     :  Tuple				      =     self.translate_src_text(**lowercase_     )
            self.assertListEqual(self.expected_text   ,				lowercase_     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				**lowerCamelCase_   :  List[str]     ):
            a_     :  str				      =     self.tokenizer(self.src_text   ,				padding=lowercase_   ,				return_tensors="""np"""   ,				**lowercase_     )
            a_     :  Tuple				      =     self.model.generate(
                model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				max_length=1_2_8   ,				).sequences
            a_     :  Any				      =     self.tokenizer.batch_decode(lowercase_   ,				skip_special_tokens=lowercase_     )
            return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  List[str]				     =  """en"""
       lowerCamelCase__:  Any				     =  """fr"""
       lowerCamelCase__:  Any				     =  [
           """I am a small frog.""",
           """Now I can forget the 100 words of german that I know.""",
       ]
       lowerCamelCase__:  int				     =  [
           """Je suis une petite grenouille.""",
           """Maintenant, je peux oublier les 100 mots d\'allemand que je connais.""",
       ]

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  Optional[Any]				     =  """fr"""
       lowerCamelCase__:  Tuple				     =  """en"""
       lowerCamelCase__:  Any				     =  [
           """Donnez moi le micro.""",
           """Tom et Mary étaient assis à une table.""",  # Accents
       ]
       lowerCamelCase__:  Any				     =  [
           """Give me the microphone.""",
           """Tom and Mary were sitting at a table.""",
       ]

       @slow
       def     UpperCAmelCase( self   :  List[Any]     ):
            self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  Optional[int]				     =  """mt"""
       lowerCamelCase__:  str				     =  """en"""
       lowerCamelCase__:  Optional[Any]				     =  ["""Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
       lowerCamelCase__:  Any				     =  ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

       @slow
       def     UpperCAmelCase( self   :  Optional[int]     ):
            self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  Dict				     =  """en"""
       lowerCamelCase__:  Optional[Any]				     =  """de"""
       lowerCamelCase__:  Union[str, Any]				     =  [
           """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__:  int				     =  [
           """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     UpperCAmelCase( self   :  str     ):
            self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  Optional[Any]				     =  """en"""
       lowerCamelCase__:  Optional[int]				     =  """zh"""
       lowerCamelCase__:  Optional[Any]				     =  ["""My name is Wolfgang and I live in Berlin"""]
       lowerCamelCase__:  Optional[Any]				     =  ["""我叫沃尔夫冈 我住在柏林"""]

       @slow
       def     UpperCAmelCase( self   :  int     ):
            self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  Optional[Any]				     =  """ru"""
       lowerCamelCase__:  List[str]				     =  """fr"""
       lowerCamelCase__:  List[str]				     =  ["""Он показал мне рукопись своей новой пьесы."""]
       lowerCamelCase__:  Any				     =  ["""Il m\'a montré le manuscrit de sa nouvelle pièce."""]

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  List[Any]				     =  """en"""
       lowerCamelCase__:  List[Any]				     =  """ROMANCE"""
       lowerCamelCase__:  Tuple				     =  [
           """>>fr<< Don\'t spend so much time watching TV.""",
           """>>pt<< Your message has been sent.""",
           """>>es<< He\'s two years older than me.""",
       ]
       lowerCamelCase__:  Any				     =  [
           """Ne passez pas autant de temps à regarder la télé.""",
           """A sua mensagem foi enviada.""",
           """Es dos años más viejo que yo.""",
       ]

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            self._assert_generated_batch_equal_expected()




'''simple docstring'''




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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowercase		:					Optional[int]													=		logging.get_logger(__name__)


if is_vision_available():
						import PIL





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			['''pixel_values''']







			def __init__(      self							:    Any							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    bool = True							,     lowercase_							:    Union[int, float] = 1 / 255							,     lowercase_							:    bool = True							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    bool = True							,     **lowercase_							:    Optional[Any]							,     ):
										super().__init__(**lowercase_       )
										lowercase_   :     List[Any]									=			size if size is not None else {"""shortest_edge""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_       )

										lowercase_   :     Tuple									=			do_resize
										lowercase_   :     int									=			size
										lowercase_   :     List[Any]									=			resample
										lowercase_   :     str									=			do_center_crop
										lowercase_   :     Optional[int]									=			crop_size
										lowercase_   :     str									=			do_rescale
										lowercase_   :     List[Any]									=			rescale_factor
										lowercase_   :     Dict									=			do_normalize
										lowercase_   :     List[str]									=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowercase_   :     Any									=			image_std if image_std is not None else OPENAI_CLIP_STD
										lowercase_   :     List[str]									=			do_convert_rgb







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Dict							,     ):
										lowercase_   :     Tuple									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			get_resize_output_image_size(
										    lowercase_							,     size=(size["""height"""], size["""width"""])							,     default_to_square=lowercase_       )
										return resize(lowercase_							,     size=lowercase_							,     resample=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Any							,     ):
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										return center_crop(lowercase_							,     size=(size["""height"""], size["""width"""])							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[int, float]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    List[Any]							,     ):
										return rescale(lowercase_							,     scale=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Optional[Any]							,     ):
										return normalize(lowercase_							,     mean=lowercase_							,     std=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    ImageInput							,     lowercase_							:    bool = None							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    PILImageResampling = None							,     lowercase_							:    bool = None							,     lowercase_							:    int = None							,     lowercase_							:    bool = None							,     lowercase_							:    float = None							,     lowercase_							:    bool = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    bool = None							,     lowercase_							:    Optional[Union[str, TensorType]] = None							,     lowercase_							:    Optional[ChannelDimension] = ChannelDimension.FIRST							,     **lowercase_							:    Optional[int]							,     ):
										lowercase_   :     Optional[Any]									=			do_resize if do_resize is not None else self.do_resize
										lowercase_   :     Union[str, Any]									=			size if size is not None else self.size
										lowercase_   :     Optional[int]									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     int									=			resample if resample is not None else self.resample
										lowercase_   :     str									=			do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_   :     int									=			crop_size if crop_size is not None else self.crop_size
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										lowercase_   :     Dict									=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_   :     List[str]									=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_   :     Optional[Any]									=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_   :     List[Any]									=			image_mean if image_mean is not None else self.image_mean
										lowercase_   :     str									=			image_std if image_std is not None else self.image_std
										lowercase_   :     Any									=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowercase_   :     int									=			make_list_of_images(lowercase_       )

										if not valid_images(lowercase_       ):
																	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:
																	raise ValueError("""Size 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."""       )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																	lowercase_   :     Dict									=			[convert_to_rgb(lowercase_       ) for image in images]

										# All transformations expect numpy arrays.
										lowercase_   :     Any									=			[to_numpy_array(lowercase_       ) for image in images]

										if do_resize:
																	lowercase_   :     Union[str, Any]									=			[self.resize(image=lowercase_							,     size=lowercase_							,     resample=lowercase_       ) for image in images]

										if do_center_crop:
																	lowercase_   :     List[str]									=			[self.center_crop(image=lowercase_							,     size=lowercase_       ) for image in images]

										if do_rescale:
																	lowercase_   :     int									=			[self.rescale(image=lowercase_							,     scale=lowercase_       ) for image in images]

										if do_normalize:
																	lowercase_   :     List[Any]									=			[self.normalize(image=lowercase_							,     mean=lowercase_							,     std=lowercase_       ) for image in images]

										lowercase_   :     List[Any]									=			[to_channel_dimension_format(lowercase_							,     lowercase_       ) for image in images]

										lowercase_   :     Any									=			{"""pixel_values""": images}
										return BatchFeature(data=lowercase_							,     tensor_type=lowercase_       )





