

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							__snake_case							:			Any      		= False

if is_vision_available():
							from PIL import Image

							from transformers import (
							    AutoProcessor,
							    PixaStructImageProcessor,
							    PixaStructProcessor,
							    PreTrainedTokenizerFast,
							    TaTokenizer,
							)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,     reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''',     )
@require_vision
@require_torch
class 					A__(unittest.TestCase       ):
						"""simple docstring"""






						def  UpperCamelCase__				(						self						)    ->   Tuple:
									a_	:	int          =   tempfile.mkdtemp()

									a_	:	Any          =   PixaStructImageProcessor()
									a_	:	Dict          =   TaTokenizer.from_pretrained("""t5-small"""						)

									a_	:	str          =   PixaStructProcessor(_lowercase     ,			_lowercase						)

									processor.save_pretrained(self.tmpdirname						)





						def  UpperCamelCase__				(						self     ,			**_lowercase						)    ->   str:
									return AutoProcessor.from_pretrained(self.tmpdirname     ,			**_lowercase						).tokenizer





						def  UpperCamelCase__				(						self     ,			**_lowercase						)    ->   str:
									return AutoProcessor.from_pretrained(self.tmpdirname     ,			**_lowercase						).image_processor





						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									shutil.rmtree(self.tmpdirname						)





						def  UpperCamelCase__				(						self						)    ->   Dict:
									a_	:	Any          =   [np.random.randint(255     ,			size=(3, 30, 400)     ,			dtype=np.uinta						)]

									a_	:	List[str]          =   [Image.fromarray(np.moveaxis(_lowercase     ,			0     ,			-1						)						) for x in image_inputs]

									return image_inputs





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	Optional[int]          =   PixaStructProcessor(tokenizer=self.get_tokenizer()     ,			image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									a_	:	List[str]          =   self.get_tokenizer(bos_token="""(BOS)"""     ,			eos_token="""(EOS)"""						)
									a_	:	int          =   self.get_image_processor(do_normalize=_lowercase     ,			padding_value=1.0						)

									a_	:	str          =   PixaStructProcessor.from_pretrained(
									    self.tmpdirname     ,			bos_token="""(BOS)"""     ,			eos_token="""(EOS)"""     ,			do_normalize=_lowercase     ,			padding_value=1.0						)

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

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





						def  UpperCamelCase__				(						self						)    ->   Dict:
									a_	:	Optional[int]          =   self.get_image_processor()
									a_	:	str          =   self.get_tokenizer()

									a_	:	Tuple          =   PixaStructProcessor(tokenizer=_lowercase     ,			image_processor=_lowercase						)

									a_	:	Dict          =   self.prepare_image_inputs()

									a_	:	Dict          =   image_processor(_lowercase     ,			return_tensors="""np"""						)
									a_	:	Dict          =   processor(images=_lowercase     ,			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  UpperCamelCase__				(						self						)    ->   Tuple:
									a_	:	Union[str, Any]          =   self.get_image_processor()
									a_	:	Dict          =   self.get_tokenizer()

									a_	:	Dict          =   PixaStructProcessor(tokenizer=_lowercase     ,			image_processor=_lowercase						)

									a_	:	List[Any]          =   'lower newer'

									a_	:	List[str]          =   processor(text=_lowercase						)

									a_	:	List[str]          =   tokenizer(_lowercase     ,			return_token_type_ids=_lowercase     ,			add_special_tokens=_lowercase						)

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





						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									a_	:	Union[str, Any]          =   self.get_image_processor()
									a_	:	Union[str, Any]          =   self.get_tokenizer()

									a_	:	Optional[Any]          =   PixaStructProcessor(tokenizer=_lowercase     ,			image_processor=_lowercase						)

									a_	:	int          =   'lower newer'
									a_	:	Optional[Any]          =   self.prepare_image_inputs()

									a_	:	List[Any]          =   processor(text=_lowercase     ,			images=_lowercase						)

									self.assertListEqual(
									    list(inputs.keys()						)     ,			["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]						)

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





						def  UpperCamelCase__				(						self						)    ->   Union[str, Any]:
									a_	:	Dict          =   self.get_image_processor()
									a_	:	Dict          =   self.get_tokenizer()

									a_	:	List[Any]          =   PixaStructProcessor(tokenizer=_lowercase     ,			image_processor=_lowercase						)

									a_	:	str          =   'lower newer'
									a_	:	Dict          =   self.prepare_image_inputs()

									a_	:	int          =   processor(text=_lowercase     ,			images=_lowercase						)

									a_	:	Dict          =   [512, 1_024, 2_048, 4_096]
									a_	:	List[Any]          =   [770, 770, 770, 770]
									# with text
									for i, max_patch in enumerate(_lowercase						):
												a_	:	str          =   processor(text=_lowercase     ,			images=_lowercase     ,			max_patches=_lowercase						)
												self.assertEqual(inputs["""flattened_patches"""][0].shape[0]     ,			_lowercase						)
												self.assertEqual(inputs["""flattened_patches"""][0].shape[1]     ,			expected_hidden_size[i]						)

									# without text input
									for i, max_patch in enumerate(_lowercase						):
												a_	:	Dict          =   processor(images=_lowercase     ,			max_patches=_lowercase						)
												self.assertEqual(inputs["""flattened_patches"""][0].shape[0]     ,			_lowercase						)
												self.assertEqual(inputs["""flattened_patches"""][0].shape[1]     ,			expected_hidden_size[i]						)





						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									a_	:	Union[str, Any]          =   self.get_image_processor()
									a_	:	Optional[Any]          =   self.get_tokenizer()

									a_	:	Union[str, Any]          =   PixaStructProcessor(tokenizer=_lowercase     ,			image_processor=_lowercase						)

									a_	:	Tuple          =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									a_	:	Any          =   processor.batch_decode(_lowercase						)
									a_	:	List[Any]          =   tokenizer.batch_decode(_lowercase						)

									self.assertListEqual(_lowercase     ,			_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Optional[int]:
									a_	:	Optional[Any]          =   self.get_image_processor()
									a_	:	Optional[Any]          =   self.get_tokenizer()

									a_	:	Optional[int]          =   PixaStructProcessor(tokenizer=_lowercase     ,			image_processor=_lowercase						)

									a_	:	List[str]          =   'lower newer'
									a_	:	Dict          =   self.prepare_image_inputs()

									a_	:	int          =   processor(text=_lowercase     ,			images=_lowercase						)

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

									a_	:	Optional[int]          =   processor(text=_lowercase						)

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





import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			class 					A__:
									"""simple docstring"""






									def __init__(						self     ,			_lowercase						)    ->   None:
												assert isinstance(_lowercase     ,			(bytes, str)						)
												a_	:	List[Any]          =   content





									def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
												return json.loads(self.content						)

			def mock_response(*a__     ,  **a__):
						assert args[0] == AUTHENTICATED_USER_ENDPOINT
						assert "Authorization" in kwargs["headers"]
						assert kwargs["headers"]["Authorization"].startswith("""token """)
						assert "Accept" in kwargs["headers"]
						return FakeResponse(B"""{\"login\":\"test\",\"id\":1}""")

			monkeypatch.setattr(a__     ,  """get"""     ,  a__)
			a_	:	Tuple          =   fetch_github_info("""token""")
			assert result["login"] == "test"
			assert result["id"] == 1


