


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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class 					snake_case__(				unittest.TestCase				):







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   int     					=					tempfile.mkdtemp()

										lowerCAmelCase_			:   str     					=					CLIPImageProcessor()
										lowerCAmelCase_			:   List[Any]     					=					BertTokenizer.from_pretrained(
										    '''hf-internal-testing/tiny-random-BertModel''' ,    model_input_names=['''input_ids''', '''attention_mask''']				)

										lowerCAmelCase_			:   Optional[int]     					=					GitProcessor(__lowercase ,    __lowercase				)

										processor.save_pretrained(self.tmpdirname				)




						def     lowercase_			(					self ,    **__lowercase				)       ->     Optional[int]:
										return AutoProcessor.from_pretrained(self.tmpdirname ,    **__lowercase				).tokenizer




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




						def     lowercase_			(					self				)       ->     Any:
										shutil.rmtree(self.tmpdirname				)




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   Union[str, Any]     					=					[np.random.randint(2_5_5 ,    size=(3, 3_0, 4_0_0) ,    dtype=np.uinta				)]

										lowerCAmelCase_			:   Union[str, Any]     					=					[Image.fromarray(np.moveaxis(__lowercase ,    0 ,    -1				)				) for x in image_inputs]

										return image_inputs




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										lowerCAmelCase_			:   Any     					=					GitProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()				)
										processor.save_pretrained(self.tmpdirname				)

										lowerCAmelCase_			:   int     					=					self.get_tokenizer(bos_token='''(BOS)''' ,    eos_token='''(EOS)'''				)
										lowerCAmelCase_			:   str     					=					self.get_image_processor(do_normalize=__lowercase ,    padding_value=1.0				)

										lowerCAmelCase_			:   int     					=					GitProcessor.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     lowercase_			(					self				)       ->     Tuple:
										lowerCAmelCase_			:   List[str]     					=					self.get_image_processor()
										lowerCAmelCase_			:   Tuple     					=					self.get_tokenizer()

										lowerCAmelCase_			:   Optional[int]     					=					GitProcessor(tokenizer=__lowercase ,    image_processor=__lowercase				)

										lowerCAmelCase_			:   str     					=					self.prepare_image_inputs()

										lowerCAmelCase_			:   List[str]     					=					image_processor(__lowercase ,    return_tensors='''np'''				)
										lowerCAmelCase_			:   int     					=					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     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_			:   List[str]     					=					self.get_image_processor()
										lowerCAmelCase_			:   str     					=					self.get_tokenizer()

										lowerCAmelCase_			:   List[str]     					=					GitProcessor(tokenizer=__lowercase ,    image_processor=__lowercase				)

										lowerCAmelCase_			:   Tuple     					=					'''lower newer'''

										lowerCAmelCase_			:   Tuple     					=					processor(text=__lowercase				)

										lowerCAmelCase_			:   List[Any]     					=					tokenizer(__lowercase ,    return_token_type_ids=__lowercase				)

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




						def     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_			:   List[str]     					=					self.get_image_processor()
										lowerCAmelCase_			:   Tuple     					=					self.get_tokenizer()

										lowerCAmelCase_			:   List[str]     					=					GitProcessor(tokenizer=__lowercase ,    image_processor=__lowercase				)

										lowerCAmelCase_			:   List[Any]     					=					'''lower newer'''
										lowerCAmelCase_			:   Dict     					=					self.prepare_image_inputs()

										lowerCAmelCase_			:   Union[str, Any]     					=					processor(text=__lowercase ,    images=__lowercase				)

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

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




						def     lowercase_			(					self				)       ->     Tuple:
										lowerCAmelCase_			:   Optional[Any]     					=					self.get_image_processor()
										lowerCAmelCase_			:   Optional[int]     					=					self.get_tokenizer()

										lowerCAmelCase_			:   Optional[int]     					=					GitProcessor(tokenizer=__lowercase ,    image_processor=__lowercase				)

										lowerCAmelCase_			:   List[Any]     					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCAmelCase_			:   List[Any]     					=					processor.batch_decode(__lowercase				)
										lowerCAmelCase_			:   Dict     					=					tokenizer.batch_decode(__lowercase				)

										self.assertListEqual(__lowercase ,    __lowercase				)




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   int     					=					self.get_image_processor()
										lowerCAmelCase_			:   Optional[int]     					=					self.get_tokenizer()

										lowerCAmelCase_			:   str     					=					GitProcessor(tokenizer=__lowercase ,    image_processor=__lowercase				)

										lowerCAmelCase_			:   Tuple     					=					'''lower newer'''
										lowerCAmelCase_			:   List[str]     					=					self.prepare_image_inputs()

										lowerCAmelCase_			:   Optional[Any]     					=					processor(text=__lowercase ,    images=__lowercase				)

										# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
										self.assertListEqual(list(inputs.keys()				) ,    ['''input_ids''', '''attention_mask''', '''pixel_values''']				)
import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				{
            '''model_details''': {
                '''Organization''': '''testing''',
                '''Model date''': '''today''',
                '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
                '''Architecture''': '''Convolutional Neural Network.''',
            },
            '''metrics''': '''BLEU and ROUGE-1''',
            '''evaluation_data''': {
                '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
                '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
            },
            '''training_data''': {
                '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
                '''Preprocessing''': (
                    '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
                    ''' https://arxiv.org/pdf/1810.03993.pdf'''
                ),
            },
            '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
        }
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Tuple	      =				ModelCard.from_dict(self.inputs_dict      )
        self.assertTrue(hasattr(__magic_name__ ,       '''model_details'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''intended_use'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''factors'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''metrics'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''evaluation_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''training_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''quantitative_analyses'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''ethical_considerations'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''caveats_and_recommendations'''      )      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				ModelCard.from_dict(self.inputs_dict      )
        A_							:       List[Any]	      =				json.loads(modelcard.to_json_string()      )
        for key, value in self.inputs_dict.items():
               self.assertEqual(obj[key] ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Dict	      =				ModelCard.from_dict(self.inputs_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
               A_							:       str	      =				os.path.join(__magic_name__ ,       '''modelcard.json'''      )
               model_card_first.to_json_file(__magic_name__      )
               A_							:       str	      =				ModelCard.from_json_file(__magic_name__      )

        self.assertEqual(model_card_second.to_dict() ,       model_card_first.to_dict()      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				ModelCard.from_dict(self.inputs_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
               model_card_first.save_pretrained(__magic_name__      )
               A_							:       Optional[int]	      =				ModelCard.from_pretrained(__magic_name__      )

        self.assertEqual(model_card_second.to_dict() ,       model_card_first.to_dict()      )


