


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 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      Dict     		=		LEDTokenizer
   __A :      List[Any]     		=		LEDTokenizerFast
   __A :      str     		=		True






   def _snake_case    (		self ):






     """simple docstring"""





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

     lowerCamelCase :					Optional[int]    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					Dict    =				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 _snake_case    (		self						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return "lower newer", "lower newer"






   @cached_property
   def _snake_case    (		self ):






     """simple docstring"""





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






   @cached_property
   def _snake_case    (		self ):






     """simple docstring"""





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






   @require_torch
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				["A long paragraph for summarization.", "Another paragraph for summarization."]
     lowerCamelCase :					Union[str, Any]    =				[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       lowerCamelCase :					int    =				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 )
       lowerCamelCase :					Tuple    =				batch.input_ids.tolist()[0]
       self.assertListEqual(__A						,				__A )






   @require_torch
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       lowerCamelCase :					Union[str, Any]    =				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 _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				[
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       lowerCamelCase :					str    =				tokenizer(text_target=__A						,				max_length=32						,				padding="max_length"						,				return_tensors="pt" )
       self.assertEqual(32						,				targets["input_ids"].shape[1] )






   @require_torch
   def _snake_case    (		self ):






     """simple docstring"""





     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       lowerCamelCase :					Tuple    =				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 _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				["A long paragraph for summarization."]
     lowerCamelCase :					str    =				[
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       lowerCamelCase :					Union[str, Any]    =				tokenizer(__A						,				return_tensors="pt" )
       lowerCamelCase :					List[str]    =				tokenizer(text_target=__A						,				return_tensors="pt" )
       lowerCamelCase :					Tuple    =				inputs["input_ids"]
       lowerCamelCase :					List[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 ):






     """simple docstring"""





     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       lowerCamelCase :					Dict    =				["Summary of the text.", "Another summary."]
       lowerCamelCase :					Union[str, Any]    =				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

       lowerCamelCase :					Tuple    =				tokenizer(__A						,				padding=__A )
       lowerCamelCase :					Union[str, Any]    =				[[0] * len(__A ) for x in encoded_output["input_ids"]]
       lowerCamelCase :					Union[str, Any]    =				tokenizer.pad(__A )
       self.assertSequenceEqual(outputs["global_attention_mask"]						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     pass






   def _snake_case    (		self ):






     """simple docstring"""





     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
         lowerCamelCase :					Dict    =				self.rust_tokenizer_class.from_pretrained(__A						,				**__A )
         lowerCamelCase :					Union[str, Any]    =				self.tokenizer_class.from_pretrained(__A						,				**__A )
         lowerCamelCase :					Optional[int]    =				"A, <mask> AllenNLP sentence."
         lowerCamelCase :					List[Any]    =				tokenizer_r.encode_plus(__A						,				add_special_tokens=__A						,				return_token_type_ids=__A )
         lowerCamelCase :					int    =				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"] )						,				)

         lowerCamelCase :					Optional[int]    =				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
         lowerCamelCase :					Optional[Any]    =				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>"] )







from typing import TYPE_CHECKING

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


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

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



