





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    int	  =  ['''image_processor''', '''tokenizer''']
   __A       :    List[str]	  =  '''Pix2StructImageProcessor'''
   __A       :    Any	  =  ('''T5Tokenizer''', '''T5TokenizerFast''')

   def __init__( self ,  lowercase ,  lowercase)      ->						int:







          '''simple docstring'''

          a__						:     int			=    False
          super().__init__(lowercase ,  lowercase)

   def __call__( self ,  lowercase=None ,  lowercase = None ,  lowercase = True ,  lowercase = False ,  lowercase = None ,  lowercase = None ,  lowercase = 2048 ,  lowercase = 0 ,  lowercase = None ,  lowercase = None ,  lowercase = False ,  lowercase = False ,  lowercase = False ,  lowercase = False ,  lowercase = False ,  lowercase = True ,  lowercase = None ,  **lowercase ,  )      ->						BatchEncoding:







          '''simple docstring'''

          if images is None and text is None:
                 raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None and not self.image_processor.is_vqa:
                 a__						:     List[Any]			=    self.tokenizer
                 a__						:     List[str]			=    self.tokenizer(
                     text=lowercase ,  add_special_tokens=lowercase ,  padding=lowercase ,  truncation=lowercase ,  max_length=lowercase ,  stride=lowercase ,  pad_to_multiple_of=lowercase ,  return_attention_mask=lowercase ,  return_overflowing_tokens=lowercase ,  return_special_tokens_mask=lowercase ,  return_offsets_mapping=lowercase ,  return_token_type_ids=lowercase ,  return_length=lowercase ,  verbose=lowercase ,  return_tensors=lowercase ,  **lowercase ,  )
                 return text_encoding

          if not self.image_processor.is_vqa:
                 # add pixel_values
                 a__						:     Any			=    self.image_processor(
                     lowercase ,  return_tensors=lowercase ,  max_patches=lowercase ,  **lowercase)
          else:
                 # add pixel_values and bbox
                 a__						:     Tuple			=    self.image_processor(
                     lowercase ,  return_tensors=lowercase ,  max_patches=lowercase ,  header_text=lowercase ,  **lowercase)

          if text is not None and not self.image_processor.is_vqa:
                 a__						:     Optional[Any]			=    self.tokenizer(
                     text=lowercase ,  add_special_tokens=lowercase ,  padding=lowercase ,  truncation=lowercase ,  max_length=lowercase ,  stride=lowercase ,  pad_to_multiple_of=lowercase ,  return_attention_mask=lowercase ,  return_overflowing_tokens=lowercase ,  return_special_tokens_mask=lowercase ,  return_offsets_mapping=lowercase ,  return_token_type_ids=lowercase ,  return_length=lowercase ,  verbose=lowercase ,  return_tensors=lowercase ,  **lowercase ,  )

                 if "attention_mask" in text_encoding:
                        a__						:     List[Any]			=    text_encoding.pop('attention_mask')
                 if "input_ids" in text_encoding:
                        a__						:     List[str]			=    text_encoding.pop('input_ids')
          else:
                 a__						:     List[Any]			=    None

          if text_encoding is not None:
                 encoding_image_processor.update(lowercase)

          return encoding_image_processor

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						Tuple:







          '''simple docstring'''

          return self.tokenizer.batch_decode(*lowercase ,  **lowercase)

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						Dict:







          '''simple docstring'''

          return self.tokenizer.decode(*lowercase ,  **lowercase)

   @property
   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     Optional[int]			=    self.tokenizer.model_input_names
          a__						:     Dict			=    self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))









import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=4 ,  )      ->						Tuple:







          '''simple docstring'''

          a__						:     str			=    parent
          a__						:     Optional[Any]			=    batch_size
          a__						:     str			=    seq_length
          a__						:     int			=    is_training
          a__						:     str			=    use_attention_mask
          a__						:     List[str]			=    use_token_type_ids
          a__						:     Optional[Any]			=    use_labels
          a__						:     List[Any]			=    vocab_size
          a__						:     Tuple			=    hidden_size
          a__						:     Dict			=    num_hidden_layers
          a__						:     List[str]			=    num_attention_heads
          a__						:     int			=    intermediate_size
          a__						:     Any			=    hidden_act
          a__						:     Optional[int]			=    hidden_dropout_prob
          a__						:     Tuple			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Optional[int]			=    type_vocab_size
          a__						:     List[Any]			=    type_sequence_label_size
          a__						:     Union[str, Any]			=    initializer_range
          a__						:     str			=    num_choices

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     Dict			=    None
          if self.use_attention_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

          a__						:     Dict			=    None
          if self.use_token_type_ids:
                 a__						:     int			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     Dict			=    BertConfig(
              vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  )

          return config, input_ids, token_type_ids, attention_mask

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     Optional[int]			=    config_and_inputs
          a__						:     str			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     int			=    config_and_inputs

          a__						:     str			=    True
          a__						:     Any			=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length] ,  vocab_size=2)

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )





@require_flax
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  True

   __A       :    Tuple	  =  (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[Any]			=    FlaxBertModelTester(self)

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







          '''simple docstring'''

          a__						:     Union[str, Any]			=    FlaxBertModel.from_pretrained('bert-base-cased')
          a__						:     Optional[Any]			=    model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)


