"""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	(   __a    ,						unittest.TestCase    ):
     lowercase		     =		LEDTokenizer
     lowercase		     =		LEDTokenizerFast
     lowercase		     =		True





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      __UpperCamelCase       						= os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES['vocab_file']   )
      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      kwargs.update(self.special_tokens_map   )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,  **A__   )





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      kwargs.update(self.special_tokens_map   )
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,  **A__   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return "lower newer", "lower newer"





     @cached_property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return LEDTokenizer.from_pretrained('allenai/led-base-16384'   )





     @cached_property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'   )





     @require_torch
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      __UpperCamelCase       						= [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __UpperCamelCase       						= 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   )
       __UpperCamelCase       						= batch.input_ids.tolist()[0]
       self.assertListEqual(A__			,  A__   )





     @require_torch
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     @require_torch
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ['A long paragraph for summarization.']
      __UpperCamelCase       						= [
          'Summary of the text.',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __UpperCamelCase       						= tokenizer(A__			,  return_tensors='pt'   )
       __UpperCamelCase       						= tokenizer(text_target=A__			,  return_tensors='pt'   )
       __UpperCamelCase       						= inputs['input_ids']
       __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __UpperCamelCase       						= ['Summary of the text.', 'Another summary.']
       __UpperCamelCase       						= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
        __UpperCamelCase       						= self.rust_tokenizer_class.from_pretrained(A__			,  **A__   )
        __UpperCamelCase       						= self.tokenizer_class.from_pretrained(A__			,  **A__   )
        __UpperCamelCase       						= 'A, <mask> AllenNLP sentence.'
        __UpperCamelCase       						= tokenizer_r.encode_plus(A__			,  add_special_tokens=A__			,  return_token_type_ids=A__   )
        __UpperCamelCase       						= 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']   )			,  )

        __UpperCamelCase       						= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
        __UpperCamelCase       						= 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase			:       int									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"bit"
     lowercase		     =		["preactivation", "bottleneck"]
     lowercase		     =		["SAME", "VALID"]





     def __init__(			self			,  __UpperCAmelCase=3			,  __UpperCAmelCase=64			,  __UpperCAmelCase=[256, 512, 1024, 2048]			,  __UpperCAmelCase=[3, 4, 6, 3]			,  __UpperCAmelCase="preactivation"			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=None			,  __UpperCAmelCase=32			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=1			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      if layer_type not in self.layer_types:
       raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
      if global_padding is not None:
       if global_padding.upper() in self.supported_padding:
        __UpperCamelCase       						= global_padding.upper()
       else:
        raise ValueError(F'Padding strategy {global_padding} not supported'   )
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embedding_size
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= layer_type
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= global_padding
      __UpperCamelCase       						= num_groups
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= embedding_dynamic_padding
      __UpperCamelCase       						= output_stride
      __UpperCamelCase       						= width_factor

      __UpperCamelCase       						= ['stem'] + [F'stage{idx}' for idx in range(1			,  len(__UpperCAmelCase   ) + 1   )]
      __UpperCamelCase						,			__UpperCamelCase       						= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase			,  out_indices=__UpperCAmelCase			,  stage_names=self.stage_names   )



