

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 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    ["""image_processor""", """tokenizer"""]
  _a		     =    """Pix2StructImageProcessor"""
  _a		     =    ("""T5Tokenizer""", """T5TokenizerFast""")




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=False
        super().__init__(lowerCAmelCase				,  lowerCAmelCase   )




  def __call__(							self				,  lowerCAmelCase=None				,  lowerCAmelCase = None				,  lowerCAmelCase = True				,  lowerCAmelCase = False				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = 2_048				,  lowerCAmelCase = 0				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = False				,  lowerCAmelCase = False				,  lowerCAmelCase = False				,  lowerCAmelCase = False				,  lowerCAmelCase = False				,  lowerCAmelCase = True				,  lowerCAmelCase = None				,  **lowerCAmelCase				,  )    ->					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:
              _lowercase      			=self.tokenizer
              _lowercase      			=self.tokenizer(
                  text=lowerCAmelCase				,  add_special_tokens=lowerCAmelCase				,  padding=lowerCAmelCase				,  truncation=lowerCAmelCase				,  max_length=lowerCAmelCase				,  stride=lowerCAmelCase				,  pad_to_multiple_of=lowerCAmelCase				,  return_attention_mask=lowerCAmelCase				,  return_overflowing_tokens=lowerCAmelCase				,  return_special_tokens_mask=lowerCAmelCase				,  return_offsets_mapping=lowerCAmelCase				,  return_token_type_ids=lowerCAmelCase				,  return_length=lowerCAmelCase				,  verbose=lowerCAmelCase				,  return_tensors=lowerCAmelCase				,  **lowerCAmelCase				,  )
              return text_encoding

        if not self.image_processor.is_vqa:
              # add pixel_values
              _lowercase      			=self.image_processor(
                  lowerCAmelCase				,  return_tensors=lowerCAmelCase				,  max_patches=lowerCAmelCase				,  **lowerCAmelCase   )
        else:
              # add pixel_values and bbox
              _lowercase      			=self.image_processor(
                  lowerCAmelCase				,  return_tensors=lowerCAmelCase				,  max_patches=lowerCAmelCase				,  header_text=lowerCAmelCase				,  **lowerCAmelCase   )

        if text is not None and not self.image_processor.is_vqa:
              _lowercase      			=self.tokenizer(
                  text=lowerCAmelCase				,  add_special_tokens=lowerCAmelCase				,  padding=lowerCAmelCase				,  truncation=lowerCAmelCase				,  max_length=lowerCAmelCase				,  stride=lowerCAmelCase				,  pad_to_multiple_of=lowerCAmelCase				,  return_attention_mask=lowerCAmelCase				,  return_overflowing_tokens=lowerCAmelCase				,  return_special_tokens_mask=lowerCAmelCase				,  return_offsets_mapping=lowerCAmelCase				,  return_token_type_ids=lowerCAmelCase				,  return_length=lowerCAmelCase				,  verbose=lowerCAmelCase				,  return_tensors=lowerCAmelCase				,  **lowerCAmelCase				,  )

              if "attention_mask" in text_encoding:
                    _lowercase      			=text_encoding.pop('attention_mask'   )
              if "input_ids" in text_encoding:
                    _lowercase      			=text_encoding.pop('input_ids'   )
        else:
              _lowercase      			=None

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

        return encoding_image_processor




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






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




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






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




  @property
  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=self.tokenizer.model_input_names
        _lowercase      			=self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase_   							=  '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'





class 					__lowerCAmelCase	(   unittest.TestCase     ,     SCREAMING_SNAKE_CASE				):




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=load_tool('text-question-answering'   )
        self.tool.setup()
        _lowercase      			=load_tool('text-question-answering'				,  remote=lowerCAmelCase   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.tool(lowerCAmelCase				,  'What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.remote_tool(lowerCAmelCase				,  'What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=self.tool(text=lowerCAmelCase				,  question='What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.remote_tool(text=lowerCAmelCase				,  question='What did Hugging Face do in April 2021?'   )
        self.assertEqual(lowerCAmelCase				,  'launched the BigScience Research Workshop'   )


