
"""simple docstring"""


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Union[str, Any]          =    MvpTokenizer
				a__:      Union[str, Any]          =    MvpTokenizerFast
				a__:      Tuple          =    True
				a__:      List[Any]          =    filter_roberta_detectors




				def        UpperCAmelCase__						(				self   ):
							super().setUp()
							lowerCamelCase_					=  [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							lowerCamelCase_					=  dict(zip(UpperCAmelCase							,						range(len(UpperCAmelCase   )   )   )   )
							lowerCamelCase_					=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCamelCase_					=  {'unk_token': '<unk>'}

							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file''']   )
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''merges_file''']   )
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										fp.write(json.dumps(UpperCAmelCase   ) + '''\n'''   )
							with open(self.merges_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										fp.write('''\n'''.join(UpperCAmelCase   )   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return "lower newer", "lower newer"




				@cached_property
				def        UpperCAmelCase__						(				self   ):
							return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''   )




				@cached_property
				def        UpperCAmelCase__						(				self   ):
							return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							lowerCamelCase_					=  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						max_length=len(UpperCAmelCase   )							,						padding=UpperCAmelCase							,						return_tensors='''pt'''   )
										self.assertIsInstance(UpperCAmelCase							,						UpperCAmelCase   )

										self.assertEqual((2, 9)							,						batch.input_ids.shape   )
										self.assertEqual((2, 9)							,						batch.attention_mask.shape   )
										lowerCamelCase_					=  batch.input_ids.tolist()[0]
										self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )
										# Test that special tokens are reset




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding=UpperCAmelCase							,						return_tensors='''pt'''   )
										# check if input_ids are returned and no labels
										self.assertIn('''input_ids'''							,						UpperCAmelCase   )
										self.assertIn('''attention_mask'''							,						UpperCAmelCase   )
										self.assertNotIn('''labels'''							,						UpperCAmelCase   )
										self.assertNotIn('''decoder_attention_mask'''							,						UpperCAmelCase   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(text_target=UpperCAmelCase							,						max_length=32							,						padding='''max_length'''							,						return_tensors='''pt'''   )
										self.assertEqual(32							,						targets['''input_ids'''].shape[1]   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):







							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(
										    ['''I am a small frog''' * 1024, '''I am a small frog''']							,						padding=UpperCAmelCase							,						truncation=UpperCAmelCase							,						return_tensors='''pt'''   )
										self.assertIsInstance(UpperCAmelCase							,						UpperCAmelCase   )
										self.assertEqual(batch.input_ids.shape							,						(2, 1024)   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ['A long paragraph for summarization.']
							lowerCamelCase_					=  [
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						text_target=UpperCAmelCase							,						return_tensors='''pt'''   )
										lowerCamelCase_					=  inputs['input_ids']
										lowerCamelCase_					=  inputs['labels']
										self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
										self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )




				def        UpperCAmelCase__						(				self   ):
							pass





				def        UpperCAmelCase__						(				self   ):







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase							,						**UpperCAmelCase   )
													lowerCamelCase_					=  self.tokenizer_class.from_pretrained(UpperCAmelCase							,						**UpperCAmelCase   )
													lowerCamelCase_					=  'A, <mask> AllenNLP sentence.'
													lowerCamelCase_					=  tokenizer_r.encode_plus(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						return_token_type_ids=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_p.encode_plus(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						return_token_type_ids=UpperCAmelCase   )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['''token_type_ids''']   )							,						sum(tokens_p['''token_type_ids''']   )   )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )							,						sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )							,						)

													lowerCamelCase_					=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
													lowerCamelCase_					=  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['''input_ids''']							,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )
													self.assertSequenceEqual(tokens_r['''input_ids''']							,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )

													self.assertSequenceEqual(
													    UpperCAmelCase							,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
													self.assertSequenceEqual(
													    UpperCAmelCase							,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )









'''simple docstring'''



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


UpperCamelCase_    	=				get_tests_dir("fixtures")






class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						mock.Mock()
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						500
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      Any    					=						HTTPError
             SCREAMING_SNAKE_CASE   :      Any    					=						{}

             # Download this model to make sure it's in the cache.
             SCREAMING_SNAKE_CASE   :      str    					=						ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
             # Under the mock environment we get a 500 error when trying to reach the model.
             with mock.patch('requests.Session.request',							return_value=A  ) as mock_head:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
                   # This check we did call the fake head request
                   mock_head.assert_called()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						ViTImageProcessor.from_pretrained(
                 'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             with self.assertRaises(A  ):
                   # config is in subfolder, the following should not work without specifying the subfolder
                   SCREAMING_SNAKE_CASE   :      str    					=						AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/stable-diffusion-all-variants',							subfolder='feature_extractor'  )

             self.assertIsNotNone(A  )






@is_staging_test
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						TOKEN
             HfFolder.save_token(A  )







       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             try:
                   delete_repo(token=cls._token,							repo_id='test-image-processor'  )
             except HTTPError:
                   pass

             try:
                   delete_repo(token=cls._token,							repo_id='valid_org/test-image-processor-org'  )
             except HTTPError:
                   pass

             try:
                   delete_repo(token=cls._token,							repo_id='test-dynamic-image-processor'  )
             except HTTPError:
                   pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ViTImageProcessor.from_pretrained(A  )
             image_processor.push_to_hub('test-image-processor',							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      int    					=						ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )

             # Reset repo
             delete_repo(token=self._token,							repo_id='test-image-processor'  )

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(
                       A,							repo_id='test-image-processor',							push_to_hub=A,							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						ViTImageProcessor.from_pretrained(A  )
             image_processor.push_to_hub('valid_org/test-image-processor',							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      str    					=						ViTImageProcessor.from_pretrained('valid_org/test-image-processor'  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )

             # Reset repo
             delete_repo(token=self._token,							repo_id='valid_org/test-image-processor'  )

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(
                       A,							repo_id='valid_org/test-image-processor-org',							push_to_hub=A,							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org'  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             CustomImageProcessor.register_for_auto_class()
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CustomImageProcessor.from_pretrained(A  )

             image_processor.push_to_hub('test-dynamic-image-processor',							use_auth_token=self._token  )

             # This has added the proper auto_map field to the config
             self.assertDictEqual(
                 image_processor.auto_map,							{'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'},							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoImageProcessor.from_pretrained(
                 F"{USER}/test-dynamic-image-processor",							trust_remote_code=A  )
             # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
             self.assertEqual(new_image_processor.__class__.__name__,							'CustomImageProcessor'  )


