
'''simple docstring'''






import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
 from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     Dict=None					,						_lowerCAmelCase :     Optional[Any]=None					,						_lowerCAmelCase :     int=None					,						_lowerCAmelCase :     Optional[Any]=None					,						_lowerCAmelCase :     Any=None					,						):



      """simple docstring"""




      if attention_mask is None:
            _lowerCamelCase					: int		    = input_ids.ne(config.pad_token_id				)
      if decoder_attention_mask is None:
            _lowerCamelCase					: str		    = decoder_input_ids.ne(config.pad_token_id				)
      if head_mask is None:
            _lowerCamelCase					: Union[str, Any]		    = torch.ones(config.encoder_layers					,						config.encoder_attention_heads					,						device=_lowerCAmelCase				)
      if decoder_head_mask is None:
            _lowerCamelCase					: Dict		    = torch.ones(config.decoder_layers					,						config.decoder_attention_heads					,						device=_lowerCAmelCase				)
      if cross_attn_head_mask is None:
            _lowerCamelCase					: Dict		    = torch.ones(config.decoder_layers					,						config.decoder_attention_heads					,						device=_lowerCAmelCase				)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }

class 			UpperCAmelCase__    :



    def __init__(						self   :							List[str],__A   :							Dict,__A   :							Tuple=1_3,__A   :							Optional[Any]=7,__A   :							Optional[Any]=True,__A   :							Tuple=False,__A   :							Optional[int]=9_9,__A   :							List[Any]=1_6,__A   :							str=2,__A   :							Optional[Any]=4,__A   :							List[Any]=4,__A   :							Optional[int]="relu",__A   :							Optional[Any]=0.1,__A   :							Any=0.1,__A   :							Optional[Any]=0.0,__A   :							List[Any]=0.0,__A   :							List[Any]=2_0,__A   :							Dict=2,__A   :							Any=1,__A   :							Tuple=0,):
          _lowerCamelCase					: Any		    = parent
          _lowerCamelCase					: Dict		    = batch_size
          _lowerCamelCase					: int		    = seq_length
          _lowerCamelCase					: Tuple		    = is_training
          _lowerCamelCase					: Tuple		    = use_labels
          _lowerCamelCase					: Optional[int]		    = vocab_size
          _lowerCamelCase					: Union[str, Any]		    = hidden_size
          _lowerCamelCase					: Optional[int]		    = num_hidden_layers
          _lowerCamelCase					: Optional[int]		    = num_attention_heads
          _lowerCamelCase					: List[str]		    = intermediate_size
          _lowerCamelCase					: str		    = hidden_act
          _lowerCamelCase					: List[str]		    = hidden_dropout_prob
          _lowerCamelCase					: int		    = attention_probs_dropout_prob
          _lowerCamelCase					: Any		    = encoder_layerdrop
          _lowerCamelCase					: Union[str, Any]		    = decoder_layerdrop
          _lowerCamelCase					: str		    = max_position_embeddings
          _lowerCamelCase					: int		    = eos_token_id
          _lowerCamelCase					: Optional[Any]		    = pad_token_id
          _lowerCamelCase					: List[Any]		    = bos_token_id



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: Optional[Any]		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )
          _lowerCamelCase					: List[str]		    = self.eos_token_id  # Eos Token
          _lowerCamelCase					: Any		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for M2M100 the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          _lowerCamelCase					: Tuple		    = input_ids.clamp(self.pad_token_id + 1  )
          _lowerCamelCase					: Any		    = decoder_input_ids.clamp(self.pad_token_id + 1  )

          _lowerCamelCase					: Any		    = self.get_config()
          _lowerCamelCase					: Tuple		    = prepare_mam_aaa_inputs_dict(__A,__A,__A  )
          return config, inputs_dict



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          return MaMaaaConfig(
              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,encoder_layerdrop=self.encoder_layerdrop,decoder_layerdrop=self.decoder_layerdrop,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,)



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase    ,		_lowerCamelCase					: str		    = self.prepare_config_and_inputs()
          return config, inputs_dict



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							Optional[int],__A   :							int  ):
          _lowerCamelCase					: Union[str, Any]		    = MaMaaaModel(config=__A  ).get_decoder().to(__A  ).eval()
          _lowerCamelCase					: int		    = inputs_dict["input_ids"]
          _lowerCamelCase					: Union[str, Any]		    = inputs_dict["attention_mask"]
          _lowerCamelCase					: List[str]		    = inputs_dict["head_mask"]

          # first forward pass
          _lowerCamelCase					: List[Any]		    = model(__A,attention_mask=__A,head_mask=__A,use_cache=__A  )

          _lowerCamelCase    ,		_lowerCamelCase					: int		    = outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          _lowerCamelCase					: int		    = ids_tensor((self.batch_size, 3),config.vocab_size  )
          _lowerCamelCase					: Union[str, Any]		    = ids_tensor((self.batch_size, 3),2  )

          # append to next input_ids and
          _lowerCamelCase					: str		    = torch.cat([input_ids, next_tokens],dim=-1  )
          _lowerCamelCase					: List[Any]		    = torch.cat([attention_mask, next_attn_mask],dim=-1  )

          _lowerCamelCase					: Tuple		    = model(__A,attention_mask=__A  )["last_hidden_state"]
          _lowerCamelCase					: Any		    = model(__A,attention_mask=__A,past_key_values=__A  )[
              "last_hidden_state"
          ]

          # select random slice
          _lowerCamelCase					: str		    = ids_tensor((1,),output_from_past.shape[-1]  ).item()
          _lowerCamelCase					: Dict		    = output_from_no_past[:, -3:, random_slice_idx].detach()
          _lowerCamelCase					: Union[str, Any]		    = output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(__A,__A,atol=1e-2  )  )



    def 	lowerCamelCase_	(						self   :							Any,__A   :							Union[str, Any],__A   :							Dict  ):
          _lowerCamelCase					: Tuple		    = MaMaaaModel(config=__A  ).to(__A  ).eval()
          _lowerCamelCase					: List[str]		    = model(**__A  )

          _lowerCamelCase					: Dict		    = outputs.encoder_last_hidden_state
          _lowerCamelCase					: Any		    = outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase					: Optional[int]		    = model.get_encoder()
                encoder.save_pretrained(__A  )
                _lowerCamelCase					: Dict		    = MaMaaaEncoder.from_pretrained(__A  ).to(__A  )

          _lowerCamelCase					: Any		    = encoder(inputs_dict["input_ids"],attention_mask=inputs_dict["attention_mask"]  )[
              0
          ]

          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3  )

          with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase					: Tuple		    = model.get_decoder()
                decoder.save_pretrained(__A  )
                _lowerCamelCase					: List[str]		    = MaMaaaDecoder.from_pretrained(__A  ).to(__A  )

          _lowerCamelCase					: Tuple		    = decoder(
              input_ids=inputs_dict["decoder_input_ids"],attention_mask=inputs_dict["decoder_attention_mask"],encoder_hidden_states=__A,encoder_attention_mask=inputs_dict["attention_mask"],)[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3  )


@require_torch
class 			UpperCAmelCase__    (							A    ,						A    ,						A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase_					     =		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    lowerCAmelCase_					     =		(
        {
            'conversational': MaMaaaForConditionalGeneration,
            'feature-extraction': MaMaaaModel,
            'summarization': MaMaaaForConditionalGeneration,
            'text2text-generation': MaMaaaForConditionalGeneration,
            'translation': MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    lowerCAmelCase_					     =		True
    lowerCAmelCase_					     =		True
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False



    def 	lowerCamelCase_	(						self   :							str,__A   :							Any,__A   :							Dict,__A   :							Union[str, Any],__A   :							str,__A   :							str  ):
          if pipeline_test_casse_name == "TranslationPipelineTests":
                # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                return True

          return False



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: str		    = MaMaaaModelTester(self  )
          _lowerCamelCase					: Optional[Any]		    = ConfigTester(self,config_class=__A  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          self.config_tester.run_common_tests()



    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          _lowerCamelCase    ,		_lowerCamelCase					: Optional[Any]		    = self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                _lowerCamelCase					: Optional[int]		    = model_class(__A  )

                with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(__A  )
                      _lowerCamelCase    ,		_lowerCamelCase					: Optional[int]		    = model_class.from_pretrained(__A,output_loading_info=__A  )
                self.assertEqual(info["missing_keys"],[]  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Optional[Any]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Dict		    = self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*__A  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase    ,		_lowerCamelCase					: Dict		    = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                _lowerCamelCase					: int		    = model_class(__A  )
                model.to(__A  )
                model.eval()

                _lowerCamelCase					: List[str]		    = copy.deepcopy(self._prepare_for_class(__A,__A  )  )

                if not self.is_encoder_decoder:
                      _lowerCamelCase					: List[str]		    = inputs["input_ids"]
                      del inputs["input_ids"]
                else:
                      _lowerCamelCase					: Tuple		    = inputs["input_ids"]
                      _lowerCamelCase					: Union[str, Any]		    = inputs.get("decoder_input_ids",__A  )
                      del inputs["input_ids"]
                      inputs.pop("decoder_input_ids",__A  )

                _lowerCamelCase					: Tuple		    = model.get_input_embeddings()
                if not self.is_encoder_decoder:
                      _lowerCamelCase					: List[Any]		    = wte(__A  )
                else:
                      _lowerCamelCase					: List[str]		    = wte(__A  )
                      _lowerCamelCase					: Dict		    = wte(__A  )

                with torch.no_grad():
                      model(**__A  )[0]



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase    ,		_lowerCamelCase					: int		    = self.model_tester.prepare_config_and_inputs()
          _lowerCamelCase					: Union[str, Any]		    = input_dict["input_ids"]
          _lowerCamelCase					: Union[str, Any]		    = input_ids.ne(1  ).to(__A  )
          _lowerCamelCase					: Any		    = MaMaaaForConditionalGeneration(__A  ).eval().to(__A  )
          if torch_device == "cuda":
                model.half()
          model.generate(__A,attention_mask=__A  )
          model.generate(num_beams=4,do_sample=__A,early_stopping=__A,num_return_sequences=3  )







def 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      return torch.tensor(_lowerCAmelCase					,						dtype=torch.long					,						device=_lowerCAmelCase				)


UpperCAmelCase_							: Optional[Any] =							1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 			UpperCAmelCase__    (							unittest.TestCase			):



    @cached_property
    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Any		    = MaMaaaModel.from_pretrained("facebook/m2m100_418M"  ).to(__A  )
          _lowerCamelCase					: Optional[int]		    = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]  )
          _lowerCamelCase					: List[Any]		    = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]  )
          _lowerCamelCase					: List[Any]		    = prepare_mam_aaa_inputs_dict(model.config,__A,__A  )
          with torch.no_grad():
                _lowerCamelCase					: Any		    = model(**__A  )[0]
          _lowerCamelCase					: List[Any]		    = torch.Size((1, 1_1, 1_0_2_4)  )
          self.assertEqual(output.shape,__A  )
          # change to expected output here
          _lowerCamelCase					: Union[str, Any]		    = torch.tensor(
              [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]],device=__A  )
          self.assertTrue(torch.allclose(output[:, :3, :3],__A,atol=__A  )  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: int		    = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(__A  )

          # change to intended input
          _lowerCamelCase					: Optional[Any]		    = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]  )
          _lowerCamelCase					: List[str]		    = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]  )
          _lowerCamelCase					: List[Any]		    = prepare_mam_aaa_inputs_dict(model.config,__A,__A  )
          with torch.no_grad():
                _lowerCamelCase					: Any		    = model(**__A  )[0]
          _lowerCamelCase					: str		    = torch.Size((1, 1_1, model.config.vocab_size)  )
          self.assertEqual(output.shape,__A  )
          # change to expected output here
          _lowerCamelCase					: str		    = torch.tensor(
              [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]],device=__A  )
          self.assertTrue(torch.allclose(output[:, :3, :3],__A,atol=__A  )  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: List[str]		    = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(__A  )
          _lowerCamelCase					: Any		    = MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M",src_lang="fr",tgt_lang="en"  )

          _lowerCamelCase					: Union[str, Any]		    = [
              "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
              "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
              "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
              " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
              " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
          ]

          # The below article tests that we don't add any hypotheses outside of the top n_beams
          _lowerCamelCase					: str		    = tokenizer(__A,padding=__A,return_tensors="pt"  )

          _lowerCamelCase					: Tuple		    = model.generate(
              input_ids=dct["input_ids"].to(__A  ),attention_mask=dct["attention_mask"].to(__A  ),num_beams=5,forced_bos_token_id=tokenizer.get_lang_id("en"  ),)

          _lowerCamelCase					: str		    = [
              "The NSA case highlights the total absence of intelligence debate",
              "I think there are two levels of response from the French government.",
              "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
              " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
              " communications in France.",
          ]

          _lowerCamelCase					: str		    = tokenizer.batch_decode(
              hypotheses_batch.tolist(),clean_up_tokenization_spaces=__A,skip_special_tokens=__A  )
          assert generated == expected_en



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


lowerCamelCase		   =			logging.get_logger(__name__)


if is_vision_available():
 import PIL




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =['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_		:	str     ,  )   ->		None:
             """simple docstring"""




             super().__init__(**lowercase_					)
             _lowerCamelCase			:						Optional[int]            =size if size is not None else {'shortest_edge': 224}
             _lowerCamelCase			:						List[Any]            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =crop_size if crop_size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						str            =get_size_dict(lowercase_     ,  default_to_square=lowercase_     ,  param_name='crop_size'					)

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






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Any     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						int            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             if "shortest_edge" not in size:
                    raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
             _lowerCamelCase			:						Union[str, Any]            =get_resize_output_image_size(lowercase_     ,  size=size['shortest_edge']     ,  default_to_square=lowercase_					)
             return resize(lowercase_     ,  size=lowercase_     ,  resample=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =get_size_dict(lowercase_					)
             if "height" not in size or "width" not in size:
                    raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
             return center_crop(lowercase_     ,  size=(size['height'], size['width'])     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[int, float]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	int     ,  )   ->		str:
             """simple docstring"""




             return rescale(lowercase_     ,  scale=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             return normalize(lowercase_     ,  mean=lowercase_     ,  std=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]     ,  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_		:	Union[str, Any]     ,  )   ->		PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =do_resize if do_resize is not None else self.do_resize
             _lowerCamelCase			:						List[str]            =size if size is not None else self.size
             _lowerCamelCase			:						Any            =get_size_dict(lowercase_     ,  param_name='size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =resample if resample is not None else self.resample
             _lowerCamelCase			:						List[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else self.crop_size
             _lowerCamelCase			:						Dict            =get_size_dict(lowercase_     ,  param_name='crop_size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase			:						Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase			:						List[str]            =do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase			:						Tuple            =image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase			:						int            =image_std if image_std is not None else self.image_std
             _lowerCamelCase			:						Tuple            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _lowerCamelCase			:						Any            =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:
                    _lowerCamelCase			:						Tuple            =[convert_to_rgb(lowercase_					) for image in images]

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

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

             if do_center_crop:
                    _lowerCamelCase			:						Optional[int]            =[self.center_crop(image=lowercase_     ,  size=lowercase_					) for image in images]

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

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

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

             _lowerCamelCase			:						Tuple            ={'pixel_values': images}
             return BatchFeature(data=lowercase_     ,  tensor_type=lowercase_					)


