
"""simple docstring"""





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors


@require_tokenizers
class A_      (lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						BartTokenizer
  SCREAMING_SNAKE_CASE__     :		str    	=						BartTokenizerFast
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						True
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						filter_roberta_detectors







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     super().setUp()
     UpperCAmelCase_   :   Optional[int]     	=       [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     UpperCAmelCase_   :   Optional[int]     	=       dict(zip(lowercase_							,  range(len(lowercase_    )    )    )    )
     UpperCAmelCase_   :   Tuple     	=       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     UpperCAmelCase_   :   Tuple     	=       {"unk_token": "<unk>"}

     UpperCAmelCase_   :   int     	=       os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES["vocab_file"]    )
     UpperCAmelCase_   :   Optional[Any]     	=       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(lowercase_    ) + "\n"    )
     with open(self.merges_file							,  "w"							,  encoding="utf-8"    ) as fp:
        fp.write("\n".join(lowercase_    )    )







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map    )
     return self.tokenizer_class.from_pretrained(self.tmpdirname							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map    )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     return "lower newer", "lower newer"







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return BartTokenizer.from_pretrained("facebook/bart-large"    )







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return BartTokenizerFast.from_pretrained("facebook/bart-large"    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
     UpperCAmelCase_   :   int     	=       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   str     	=       tokenizer(lowercase_							,  max_length=len(lowercase_    )							,  padding=lowercase_							,  return_tensors="pt"    )
        self.assertIsInstance(lowercase_							,  lowercase_    )

        self.assertEqual((2, 9)							,  batch.input_ids.shape    )
        self.assertEqual((2, 9)							,  batch.attention_mask.shape    )
        UpperCAmelCase_   :   Union[str, Any]     	=       batch.input_ids.tolist()[0]
        self.assertListEqual(lowercase_							,  lowercase_    )
        # Test that special tokens are reset







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Dict     	=       tokenizer(lowercase_							,  padding=lowercase_							,  return_tensors="pt"    )
        # check if input_ids are returned and no labels
        self.assertIn("input_ids"							,  lowercase_    )
        self.assertIn("attention_mask"							,  lowercase_    )
        self.assertNotIn("labels"							,  lowercase_    )
        self.assertNotIn("decoder_attention_mask"							,  lowercase_    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       [
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   List[Any]     	=       tokenizer(text_target=lowercase_							,  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_   :   Optional[int]     	=       tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"]							,  padding=lowercase_							,  truncation=lowercase_							,  return_tensors="pt"    )
        self.assertIsInstance(lowercase_							,  lowercase_    )
        self.assertEqual(batch.input_ids.shape							,  (2, 1024)    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       ["A long paragraph for summarization."]
     UpperCAmelCase_   :   List[str]     	=       [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Any     	=       tokenizer(lowercase_							,  return_tensors="pt"    )
        UpperCAmelCase_   :   int     	=       tokenizer(text_target=lowercase_							,  return_tensors="pt"    )
        UpperCAmelCase_   :   Optional[int]     	=       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()    )







  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_   :   List[str]     	=       self.rust_tokenizer_class.from_pretrained(lowercase_							,  **lowercase_    )
           UpperCAmelCase_   :   Optional[int]     	=       self.tokenizer_class.from_pretrained(lowercase_							,  **lowercase_    )
           UpperCAmelCase_   :   Optional[Any]     	=       "A, <mask> AllenNLP sentence."
           UpperCAmelCase_   :   List[Any]     	=       tokenizer_r.encode_plus(lowercase_							,  add_special_tokens=lowercase_							,  return_token_type_ids=lowercase_    )
           UpperCAmelCase_   :   Optional[int]     	=       tokenizer_p.encode_plus(lowercase_							,  add_special_tokens=lowercase_							,  return_token_type_ids=lowercase_    )

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]    )							,  sum(tokens_p["token_type_ids"]    )    )

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    )							,  sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    )							,  )

           UpperCAmelCase_   :   Union[str, Any]     	=       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
           UpperCAmelCase_   :   Tuple     	=       tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )

           # Rust correctly handles the space before the mask while python doesnt
           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(
               lowercase_							,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )
           self.assertSequenceEqual(
               lowercase_							,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )




"""simple docstring"""




from typing import TYPE_CHECKING

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


A__   :						Optional[Any] 							=     {"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						List[Any] 							=     ["""ViltFeatureExtractor"""]
    A__   :						Any 							=     ["""ViltImageProcessor"""]
    A__   :						int 							=     ["""ViltProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						str 							=     [
        """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViltForImageAndTextRetrieval""",
        """ViltForImagesAndTextClassification""",
        """ViltForTokenClassification""",
        """ViltForMaskedLM""",
        """ViltForQuestionAnswering""",
        """ViltLayer""",
        """ViltModel""",
        """ViltPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_vilt import ViltFeatureExtractor
        from .image_processing_vilt import ViltImageProcessor
        from .processing_vilt import ViltProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vilt import (
            VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViltForImageAndTextRetrieval,
            ViltForImagesAndTextClassification,
            ViltForMaskedLM,
            ViltForQuestionAnswering,
            ViltForTokenClassification,
            ViltLayer,
            ViltModel,
            ViltPreTrainedModel,
        )


else:
    import sys

    A__   :						Any 							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure)



