
"""simple docstring"""


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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







@require_tokenizers
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    LEDTokenizer
						_lowerCamelCase:    Optional[int]      =    LEDTokenizerFast
						_lowerCamelCase:    Union[str, Any]      =    True


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

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,**A_     :      int      )     ->   str:
							kwargs.update(self.special_tokens_map      )
							return self.tokenizer_class.from_pretrained(self.tmpdirname  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,**A_     :      Any      )     ->   Union[str, Any]:
							kwargs.update(self.special_tokens_map      )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Optional[Any]      )     ->   Any:
							return "lower newer", "lower newer"


						@cached_property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[Any]:
							return LEDTokenizer.from_pretrained('allenai/led-base-16384'      )


						@cached_property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
							return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'      )


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

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

								self.assertEqual((2, 9)  ,batch.input_ids.shape      )
								self.assertEqual((2, 9)  ,batch.attention_mask.shape      )
								A						   =   batch.input_ids.tolist()[0]
								self.assertListEqual(A_  ,A_      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[Any]:
							A						   =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(A_  ,padding=A_  ,return_tensors='pt'      )
								self.assertIn('input_ids'  ,A_      )
								self.assertIn('attention_mask'  ,A_      )
								self.assertNotIn('labels'  ,A_      )
								self.assertNotIn('decoder_attention_mask'  ,A_      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[int]:
							A						   =   [
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(text_target=A_  ,max_length=32  ,padding='max_length'  ,return_tensors='pt'      )
								self.assertEqual(32  ,targets['input_ids'].shape[1]      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   List[Any]:
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(
								    ['I am a small frog' * 1024, 'I am a small frog']  ,padding=A_  ,truncation=A_  ,return_tensors='pt'      )
								self.assertIsInstance(A_  ,A_      )
								self.assertEqual(batch.input_ids.shape  ,(2, 5122)      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   ['A long paragraph for summarization.']
							A						   =   [
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(A_  ,return_tensors='pt'      )
								A						   =   tokenizer(text_target=A_  ,return_tensors='pt'      )
								A						   =   inputs['input_ids']
								A						   =   targets['input_ids']
								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()      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   int:
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   ['Summary of the text.', 'Another summary.']
								A						   =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

								A						   =   tokenizer(A_  ,padding=A_      )
								A						   =   [[0] * len(A_      ) for x in encoded_output['input_ids']]
								A						   =   tokenizer.pad(A_      )
								self.assertSequenceEqual(outputs['global_attention_mask']  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
							pass




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   str:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
									A						   =   self.rust_tokenizer_class.from_pretrained(A_  ,**A_      )
									A						   =   self.tokenizer_class.from_pretrained(A_  ,**A_      )
									A						   =   'A, <mask> AllenNLP sentence.'
									A						   =   tokenizer_r.encode_plus(A_  ,add_special_tokens=A_  ,return_token_type_ids=A_      )
									A						   =   tokenizer_p.encode_plus(A_  ,add_special_tokens=A_  ,return_token_type_ids=A_      )
									self.assertEqual(sum(tokens_r['token_type_ids']      )  ,sum(tokens_p['token_type_ids']      )      )
									self.assertEqual(
									    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      )  ,sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      )  ,)

									A						   =   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
									A						   =   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )
									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(
									    A_  ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
									self.assertSequenceEqual(
									    A_  ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPTokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPFeatureExtractor''']
		_lowercase							   =				['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)