

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


__UpperCAmelCase								=			logging.get_logger(__name__)


if is_vision_available():
   import PIL



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Optional[Any]										=["pixel_values"]







  def __init__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   bool = True	,							lowerCAmelCase			:   Dict[str, int] = None	,							lowerCAmelCase			:   PILImageResampling = PILImageResampling.BICUBIC	,							lowerCAmelCase			:   bool = True	,							lowerCAmelCase			:   Dict[str, int] = None	,							lowerCAmelCase			:   bool = True	,							lowerCAmelCase			:   Union[int, float] = 1 / 2_55	,							lowerCAmelCase			:   bool = True	,							lowerCAmelCase			:   Optional[Union[float, List[float]]] = None	,							lowerCAmelCase			:   Optional[Union[float, List[float]]] = None	,							lowerCAmelCase			:   bool = True	,							**lowerCAmelCase			:   Optional[Any]	,							)							->     None:






     """simple docstring"""







     super().__init__(**lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						size if size is not None else {"""shortest_edge""": 2_24}
     __lowerCAmelCase  :      Any    =						get_size_dict(lowerCAmelCase	,							default_to_square=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
     __lowerCAmelCase  :      Tuple    =						get_size_dict(lowerCAmelCase	,							default_to_square=lowerCAmelCase	,							param_name="""crop_size"""   )

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   np.ndarray	,							lowerCAmelCase			:   Dict[str, int]	,							lowerCAmelCase			:   PILImageResampling = PILImageResampling.BICUBIC	,							lowerCAmelCase			:   Optional[Union[str, ChannelDimension]] = None	,							**lowerCAmelCase			:   List[str]	,							)							->     np.ndarray:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						get_size_dict(lowerCAmelCase	,							default_to_square=lowerCAmelCase   )
     if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
     __lowerCAmelCase  :      List[Any]    =						get_resize_output_image_size(lowerCAmelCase	,							size=size["""shortest_edge"""]	,							default_to_square=lowerCAmelCase   )
     return resize(lowerCAmelCase	,							size=lowerCAmelCase	,							resample=lowerCAmelCase	,							data_format=lowerCAmelCase	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   np.ndarray	,							lowerCAmelCase			:   Dict[str, int]	,							lowerCAmelCase			:   Optional[Union[str, ChannelDimension]] = None	,							**lowerCAmelCase			:   Any	,							)							->     np.ndarray:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						get_size_dict(lowerCAmelCase   )
     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(lowerCAmelCase	,							size=(size["""height"""], size["""width"""])	,							data_format=lowerCAmelCase	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   np.ndarray	,							lowerCAmelCase			:   Union[int, float]	,							lowerCAmelCase			:   Optional[Union[str, ChannelDimension]] = None	,							**lowerCAmelCase			:   Dict	,							)							->     int:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   np.ndarray	,							lowerCAmelCase			:   Union[float, List[float]]	,							lowerCAmelCase			:   Union[float, List[float]]	,							lowerCAmelCase			:   Optional[Union[str, ChannelDimension]] = None	,							**lowerCAmelCase			:   Optional[int]	,							)							->     np.ndarray:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   ImageInput	,							lowerCAmelCase			:   bool = None	,							lowerCAmelCase			:   Dict[str, int] = None	,							lowerCAmelCase			:   PILImageResampling = None	,							lowerCAmelCase			:   bool = None	,							lowerCAmelCase			:   int = None	,							lowerCAmelCase			:   bool = None	,							lowerCAmelCase			:   float = None	,							lowerCAmelCase			:   bool = None	,							lowerCAmelCase			:   Optional[Union[float, List[float]]] = None	,							lowerCAmelCase			:   Optional[Union[float, List[float]]] = None	,							lowerCAmelCase			:   bool = None	,							lowerCAmelCase			:   Optional[Union[str, TensorType]] = None	,							lowerCAmelCase			:   Optional[ChannelDimension] = ChannelDimension.FIRST	,							**lowerCAmelCase			:   Dict	,							)							->     PIL.Image.Image:






     """simple docstring"""







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

     __lowerCAmelCase  :      Union[str, Any]    =						make_list_of_images(lowerCAmelCase   )

     if not valid_images(lowerCAmelCase   ):
        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  :      Dict    =						[convert_to_rgb(lowerCAmelCase   ) for image in images]

     # All transformations expect numpy arrays.
     __lowerCAmelCase  :      Dict    =						[to_numpy_array(lowerCAmelCase   ) for image in images]

     if do_resize:
        __lowerCAmelCase  :      str    =						[self.resize(image=lowerCAmelCase	,							size=lowerCAmelCase	,							resample=lowerCAmelCase   ) for image in images]

     if do_center_crop:
        __lowerCAmelCase  :      List[Any]    =						[self.center_crop(image=lowerCAmelCase	,							size=lowerCAmelCase   ) for image in images]

     if do_rescale:
        __lowerCAmelCase  :      Dict    =						[self.rescale(image=lowerCAmelCase	,							scale=lowerCAmelCase   ) for image in images]

     if do_normalize:
        __lowerCAmelCase  :      List[str]    =						[self.normalize(image=lowerCAmelCase	,							mean=lowerCAmelCase	,							std=lowerCAmelCase   ) for image in images]

     __lowerCAmelCase  :      Any    =						[to_channel_dimension_format(lowerCAmelCase	,							lowerCAmelCase   ) for image in images]

     __lowerCAmelCase  :      List[Any]    =						{"""pixel_values""": images}
     return BatchFeature(data=lowerCAmelCase	,							tensor_type=lowerCAmelCase   )





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel



@require_tf
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=MBartConfig
  lowerCamelCase   :      Optional[Any]										={}
  lowerCamelCase   :      Dict										="gelu"







  def __init__(      self			:   str	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]=13	,							lowerCAmelCase			:   List[str]=7	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[Any]=False	,							lowerCAmelCase			:   Union[str, Any]=99	,							lowerCAmelCase			:   List[str]=32	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Any=37	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   List[Any]=0.1	,							lowerCAmelCase			:   Dict=20	,							lowerCAmelCase			:   Any=2	,							lowerCAmelCase			:   Union[str, Any]=1	,							lowerCAmelCase			:   str=0	,							)							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      int    =						batch_size
     __lowerCAmelCase  :      int    =						seq_length
     __lowerCAmelCase  :      Tuple    =						is_training
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Dict    =						num_hidden_layers
     __lowerCAmelCase  :      int    =						num_attention_heads
     __lowerCAmelCase  :      Any    =						intermediate_size
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[Any]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Tuple    =						max_position_embeddings
     __lowerCAmelCase  :      Union[str, Any]    =						eos_token_id
     __lowerCAmelCase  :      Optional[Any]    =						pad_token_id
     __lowerCAmelCase  :      int    =						bos_token_id







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size   )
     __lowerCAmelCase  :      Optional[Any]    =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )	,							1   )
     __lowerCAmelCase  :      int    =						tf.concat([input_ids, eos_tensor]	,							axis=1   )

     __lowerCAmelCase  :      Any    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						self.config_cls(
         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_ids=[2]	,							bos_token_id=self.bos_token_id	,							pad_token_id=self.pad_token_id	,							decoder_start_token_id=self.pad_token_id	,							**self.config_updates	,							)
     __lowerCAmelCase  :      Tuple    =						prepare_mbart_inputs_dict(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						TFMBartModel(config=lowerCAmelCase   ).get_decoder()
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""input_ids"""]

     __lowerCAmelCase  :      Optional[Any]    =						input_ids[:1, :]
     __lowerCAmelCase  :      Union[str, Any]    =						inputs_dict["""attention_mask"""][:1, :]
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""head_mask"""]
     __lowerCAmelCase  :      Any    =						1

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

     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						outputs.to_tuple()
     __lowerCAmelCase  :      Union[str, Any]    =						past_key_values[1]






def      snake_case_				(__A					:						str ,       __A					:						Union[str, Any] ,       __A					:						Tuple ,       __A					:						Tuple=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[int]=None ,       __A					:						Optional[Any]=None ,       )      ->     int:
   if attention_mask is None:
      __lowerCAmelCase  :      Dict    =						tf.cast(tf.math.not_equal(__A ,       config.pad_token_id					) ,       tf.inta					)
   if decoder_attention_mask is None:
      __lowerCAmelCase  :      str    =						tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape ,       dtype=tf.inta					),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,       config.pad_token_id					) ,       tf.inta					),
          ] ,       axis=-1 ,       )
   if head_mask is None:
      __lowerCAmelCase  :      Tuple    =						tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      __lowerCAmelCase  :      List[Any]    =						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      __lowerCAmelCase  :      Any    =						tf.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": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }



@require_tf
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Optional[int]										=(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  lowerCamelCase   :      List[str]										=(TFMBartForConditionalGeneration,) if is_tf_available() else ()
  lowerCamelCase   :      Union[str, Any]										=(
      {
          "conversational": TFMBartForConditionalGeneration,
          "feature-extraction": TFMBartModel,
          "summarization": TFMBartForConditionalGeneration,
          "text2text-generation": TFMBartForConditionalGeneration,
          "translation": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase   :      str										=True
  lowerCamelCase   :      Tuple										=False
  lowerCamelCase   :      Dict										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[Any]   )							->     Optional[int]:






     """simple docstring"""







     if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
        # Exception encountered when calling layer '...'
        return True

     return False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						TFMBartModelTester(self   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     List[Any]:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase   )






@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=[
      " UN Chief Says There Is No Military Solution in Syria",
  ]
  lowerCamelCase   :      Tuple										=[
      "Şeful ONU declară că nu există o soluţie militară în Siria",
  ]
  lowerCamelCase   :      List[Any]										="facebook/mbart-large-en-ro"







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Any:






     """simple docstring"""







     return AutoTokenizer.from_pretrained(self.model_name   )







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
     return model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							**lowerCAmelCase			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.translate_src_text(**lowerCAmelCase   )
     self.assertListEqual(self.expected_text	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							**lowerCAmelCase			:   int   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.tokenizer(self.src_text	,							**lowerCAmelCase	,							return_tensors="""tf"""   )
     __lowerCAmelCase  :      Union[str, Any]    =						self.model.generate(
         model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2   )
     __lowerCAmelCase  :      List[str]    =						self.tokenizer.batch_decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   )
     return generated_words







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()


