'''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 		__A       (__magic_name__ ,					unittest.TestCase   ):
       snake_case       :List[Any]				    =					LEDTokenizer
       snake_case       :List[str]				    =					LEDTokenizerFast
       snake_case       :Tuple				    =					True






       def 			_snake_case						(      self      ):
              super().setUp()
              __UpperCAmelCase  :				Dict       				=		[
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "\u0120",
                  "\u0120l",
                  "\u0120n",
                  "\u0120lo",
                  "\u0120low",
                  "er",
                  "\u0120lowest",
                  "\u0120newer",
                  "\u0120wider",
                  "<unk>",
              ]
              __UpperCAmelCase  :				Any       				=		dict(zip(UpperCamelCase_				,     range(len(UpperCamelCase_      )      )      )      )
              __UpperCAmelCase  :				Any       				=		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
              __UpperCAmelCase  :				Optional[Any]       				=		{"unk_token": "<unk>"}

              __UpperCAmelCase  :				Dict       				=		os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["vocab_file"]      )
              __UpperCAmelCase  :				Tuple       				=		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 			_snake_case						(      self				,     **UpperCamelCase_      ):
              kwargs.update(self.special_tokens_map      )
              return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              kwargs.update(self.special_tokens_map      )
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return "lower newer", "lower newer"






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






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






       @require_torch
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		["A long paragraph for summarization.", "Another paragraph for summarization."]
              __UpperCAmelCase  :				Tuple       				=		[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     __UpperCAmelCase  :				Tuple       				=		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      )
                     __UpperCAmelCase  :				List[str]       				=		batch.input_ids.tolist()[0]
                     self.assertListEqual(UpperCamelCase_				,     UpperCamelCase_      )






       @require_torch
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		["A long paragraph for summarization.", "Another paragraph for summarization."]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     __UpperCAmelCase  :				Tuple       				=		tokenizer(UpperCamelCase_				,     padding=UpperCamelCase_				,     return_tensors="pt"      )
                     self.assertIn("input_ids"				,     UpperCamelCase_      )
                     self.assertIn("attention_mask"				,     UpperCamelCase_      )
                     self.assertNotIn("labels"				,     UpperCamelCase_      )
                     self.assertNotIn("decoder_attention_mask"				,     UpperCamelCase_      )






       @require_torch
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		[
                  "Summary of the text.",
                  "Another summary.",
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     __UpperCAmelCase  :				List[str]       				=		tokenizer(text_target=UpperCamelCase_				,     max_length=32				,     padding="max_length"				,     return_tensors="pt"      )
                     self.assertEqual(32				,     targets["input_ids"].shape[1]      )






       @require_torch
       def 			_snake_case						(      self      ):
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     __UpperCAmelCase  :				Tuple       				=		tokenizer(
                         ["I am a small frog" * 10_24, "I am a small frog"]				,     padding=UpperCamelCase_				,     truncation=UpperCamelCase_				,     return_tensors="pt"      )
                     self.assertIsInstance(UpperCamelCase_				,     UpperCamelCase_      )
                     self.assertEqual(batch.input_ids.shape				,     (2, 51_22)      )






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

                     __UpperCAmelCase  :				int       				=		tokenizer(UpperCamelCase_				,     padding=UpperCamelCase_      )
                     __UpperCAmelCase  :				Tuple       				=		[[0] * len(UpperCamelCase_      ) for x in encoded_output["input_ids"]]
                     __UpperCAmelCase  :				int       				=		tokenizer.pad(UpperCamelCase_      )
                     self.assertSequenceEqual(outputs["global_attention_mask"]				,     UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              pass







       def 			_snake_case						(      self      ):
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
                            __UpperCAmelCase  :				str       				=		self.rust_tokenizer_class.from_pretrained(UpperCamelCase_				,     **UpperCamelCase_      )
                            __UpperCAmelCase  :				Any       				=		self.tokenizer_class.from_pretrained(UpperCamelCase_				,     **UpperCamelCase_      )
                            __UpperCAmelCase  :				Tuple       				=		"A, <mask> AllenNLP sentence."
                            __UpperCAmelCase  :				str       				=		tokenizer_r.encode_plus(UpperCamelCase_				,     add_special_tokens=UpperCamelCase_				,     return_token_type_ids=UpperCamelCase_      )
                            __UpperCAmelCase  :				List[str]       				=		tokenizer_p.encode_plus(UpperCamelCase_				,     add_special_tokens=UpperCamelCase_				,     return_token_type_ids=UpperCamelCase_      )
                            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  :				Optional[Any]       				=		tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
                            __UpperCAmelCase  :				int       				=		tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]      )
                            self.assertSequenceEqual(tokens_p["input_ids"]				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
                            self.assertSequenceEqual(tokens_r["input_ids"]				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 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'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






