



'''simple docstring'''


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class      __UpperCamelCase       (unittest.TestCase			):







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






							lowercase        =							tempfile.mkdtemp()

							lowercase        =							BlipImageProcessor()
							lowercase        =							BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""						)

							lowercase        =							BlipProcessor(_lowerCAmelCase ,				_lowerCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)







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






							return AutoProcessor.from_pretrained(self.tmpdirname ,				**_lowerCAmelCase						).tokenizer







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							return AutoProcessor.from_pretrained(self.tmpdirname ,				**_lowerCAmelCase						).image_processor







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname						)







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






							lowercase        =							[np.random.randint(255 ,				size=(3, 30, 400) ,				dtype=np.uinta						)]

							lowercase        =							[Image.fromarray(np.moveaxis(_lowerCAmelCase ,				0 ,				-1						)						) for x in image_inputs]

							return image_inputs







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							BlipProcessor(tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							lowercase        =							self.get_tokenizer(bos_token="""(BOS)""" ,				eos_token="""(EOS)"""						)
							lowercase        =							self.get_image_processor(do_normalize=_lowerCAmelCase ,				padding_value=1.0						)

							lowercase        =							BlipProcessor.from_pretrained(
							    self.tmpdirname ,				bos_token="""(BOS)""" ,				eos_token="""(EOS)""" ,				do_normalize=_lowerCAmelCase ,				padding_value=1.0						)

							self.assertEqual(processor.tokenizer.get_vocab() ,				tokenizer_add_kwargs.get_vocab()						)
							self.assertIsInstance(processor.tokenizer ,				_lowerCAmelCase						)

							self.assertEqual(processor.image_processor.to_json_string() ,				image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor ,				_lowerCAmelCase						)







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






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()

							lowercase        =							BlipProcessor(tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase						)

							lowercase        =							self.prepare_image_inputs()

							lowercase        =							image_processor(_lowerCAmelCase ,				return_tensors="""np"""						)
							lowercase        =							processor(images=_lowerCAmelCase ,				return_tensors="""np"""						)

							for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum() ,				input_processor[key].sum() ,				delta=1E-2						)







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






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()

							lowercase        =							BlipProcessor(tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase						)

							lowercase        =							"""lower newer"""

							lowercase        =							processor(text=_lowerCAmelCase						)

							lowercase        =							tokenizer(_lowerCAmelCase ,				return_token_type_ids=_lowerCAmelCase						)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key] ,				encoded_processor[key]						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()

							lowercase        =							BlipProcessor(tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase						)

							lowercase        =							"""lower newer"""
							lowercase        =							self.prepare_image_inputs()

							lowercase        =							processor(text=_lowerCAmelCase ,				images=_lowerCAmelCase						)

							self.assertListEqual(list(inputs.keys()						) ,				["""pixel_values""", """input_ids""", """attention_mask"""]						)

							# test if it raises when no input is passed
							with pytest.raises(_lowerCAmelCase						):
													processor()







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






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()

							lowercase        =							BlipProcessor(tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase						)

							lowercase        =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowercase        =							processor.batch_decode(_lowerCAmelCase						)
							lowercase        =							tokenizer.batch_decode(_lowerCAmelCase						)

							self.assertListEqual(_lowerCAmelCase ,				_lowerCAmelCase						)







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






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()

							lowercase        =							BlipProcessor(tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase						)

							lowercase        =							"""lower newer"""
							lowercase        =							self.prepare_image_inputs()

							lowercase        =							processor(text=_lowerCAmelCase ,				images=_lowerCAmelCase						)

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()						) ,				["""pixel_values""", """input_ids""", """attention_mask"""]						)







'''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.''')


