



'''simple docstring'''


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      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					['''image_processor''', '''tokenizer''']
	__A			  =					'''Pix2StructImageProcessor'''
	__A			  =					('''T5Tokenizer''', '''T5TokenizerFast''')







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Union[str, 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 = 2048 ,				_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						)    ->    Union[str, Any]:
							'''simple docstring'''






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







	def   _a   (    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






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







	@property
	def   _a   (    self						)    ->    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						)						)







'''simple docstring'''


from __future__ import annotations

import os
from typing import Any

import requests

lowercase_      :				List[str]   					=    '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_      :				Any   					=    BASE_URL + '''/user'''

# https://github.com/settings/tokens
lowercase_      :				Union[str, Any]   					=    os.environ.get('''USER_TOKEN''', '''''')







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							{
						    """Authorization""": F"""token {auth_token}""",
						    """Accept""": """application/vnd.github.v3+json""",
						}
						return requests.get(lowercase_ ,  headers=lowercase_						).json()


if __name__ == "__main__":  # pragma: no cover
						if USER_TOKEN:
												for key, value in fetch_github_info(USER_TOKEN).items():
																		print(f'''{key}: {value}''')
						else:
												raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')


