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 					__SCREAMING_SNAKE_CASE       (       UpperCamelCase						,	unittest.TestCase):






       """simple docstring"""

       __UpperCAmelCase       =					LEDTokenizer
       __UpperCAmelCase       =					LEDTokenizerFast
       __UpperCAmelCase       =					True







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

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







       def      lowercase_   ( self			,		**_UpperCAmelCase				):
          kwargs.update(self.special_tokens_map				)
          return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**_lowerCAmelCase				)







       def      lowercase_   ( self			,		**_UpperCAmelCase				):
          kwargs.update(self.special_tokens_map				)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,		**_lowerCAmelCase				)







       def      lowercase_   ( self			,		_UpperCAmelCase				):
          return "lower newer", "lower newer"







       @cached_property
       def      lowercase_   ( self				):
          return LEDTokenizer.from_pretrained('allenai/led-base-16384'				)







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







       @require_torch
       def      lowercase_   ( self				):
          __snake_case   :			Any										=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
          __snake_case   :			Optional[Any]										=						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

             self.assertEqual((2, 9)			,		batch.input_ids.shape				)
             self.assertEqual((2, 9)			,		batch.attention_mask.shape				)
             __snake_case   :			Any										=						batch.input_ids.tolist()[0]
             self.assertListEqual(_lowerCAmelCase			,		_lowerCAmelCase				)







       @require_torch
       def      lowercase_   ( self				):
          __snake_case   :			Any										=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __snake_case   :			List[Any]										=						tokenizer(_lowerCAmelCase			,		padding=_lowerCAmelCase			,		return_tensors='pt'				)
             self.assertIn('input_ids'			,		_lowerCAmelCase				)
             self.assertIn('attention_mask'			,		_lowerCAmelCase				)
             self.assertNotIn('labels'			,		_lowerCAmelCase				)
             self.assertNotIn('decoder_attention_mask'			,		_lowerCAmelCase				)







       @require_torch
       def      lowercase_   ( self				):
          __snake_case   :			Union[str, Any]										=						[
              'Summary of the text.',
              'Another summary.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __snake_case   :			List[str]										=						tokenizer(text_target=_lowerCAmelCase			,		max_length=32			,		padding='max_length'			,		return_tensors='pt'				)
             self.assertEqual(32			,		targets['input_ids'].shape[1]				)







       @require_torch
       def      lowercase_   ( self				):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __snake_case   :			str										=						tokenizer(
                 ['I am a small frog' * 1_024, 'I am a small frog']			,		padding=_lowerCAmelCase			,		truncation=_lowerCAmelCase			,		return_tensors='pt'				)
             self.assertIsInstance(_lowerCAmelCase			,		_lowerCAmelCase				)
             self.assertEqual(batch.input_ids.shape			,		(2, 5_122)				)







       @require_torch
       def      lowercase_   ( self				):
          __snake_case   :			Union[str, Any]										=						['A long paragraph for summarization.']
          __snake_case   :			Any										=						[
              'Summary of the text.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __snake_case   :			int										=						tokenizer(_lowerCAmelCase			,		return_tensors='pt'				)
             __snake_case   :			List[str]										=						tokenizer(text_target=_lowerCAmelCase			,		return_tensors='pt'				)
             __snake_case   :			Optional[Any]										=						inputs['input_ids']
             __snake_case   :			str										=						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      lowercase_   ( self				):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __snake_case   :			Optional[int]										=						['Summary of the text.', 'Another summary.']
             __snake_case   :			List[Any]										=						[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

             __snake_case   :			Union[str, Any]										=						tokenizer(_lowerCAmelCase			,		padding=_lowerCAmelCase				)
             __snake_case   :			int										=						[[0] * len(_lowerCAmelCase				) for x in encoded_output['input_ids']]
             __snake_case   :			int										=						tokenizer.pad(_lowerCAmelCase				)
             self.assertSequenceEqual(outputs['global_attention_mask']			,		_lowerCAmelCase				)







       def      lowercase_   ( self				):
          pass







       def      lowercase_   ( self				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                __snake_case   :			Optional[int]										=						self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase			,		**_lowerCAmelCase				)
                __snake_case   :			Any										=						self.tokenizer_class.from_pretrained(_lowerCAmelCase			,		**_lowerCAmelCase				)
                __snake_case   :			int										=						'A, <mask> AllenNLP sentence.'
                __snake_case   :			Tuple										=						tokenizer_r.encode_plus(_lowerCAmelCase			,		add_special_tokens=_lowerCAmelCase			,		return_token_type_ids=_lowerCAmelCase				)
                __snake_case   :			List[str]										=						tokenizer_p.encode_plus(_lowerCAmelCase			,		add_special_tokens=_lowerCAmelCase			,		return_token_type_ids=_lowerCAmelCase				)
                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']				)			,		)

                __snake_case   :			Optional[Any]										=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
                __snake_case   :			Optional[Any]										=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)
                self.assertSequenceEqual(tokens_p['input_ids']			,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
                self.assertSequenceEqual(tokens_r['input_ids']			,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

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










'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE									=				{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MCTCTForCTC",
           "MCTCTModel",
           "MCTCTPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
       from .feature_extraction_mctct import MCTCTFeatureExtractor
       from .processing_mctct import MCTCTProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
       import sys

       _SCREAMING_SNAKE_CASE									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


