



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 lowerCAmelCase_       (   __lowerCamelCase ,unittest.TestCase			):







	'''simple docstring'''




	A_       :     Tuple									=		BartTokenizer
	A_       :     Any									=		BartTokenizerFast
	A_       :     List[Any]									=		True
	A_       :     Dict									=		filter_roberta_detectors

	def 				_A			(	self       ):



				'''simple docstring'''


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

				a__					=  os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]       )
				a__					=  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 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				return "lower newer", "lower newer"

	@cached_property
	def 				_A			(	self       ):



				'''simple docstring'''


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

	@cached_property
	def 				_A			(	self       ):



				'''simple docstring'''


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

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


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

				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							a__					=  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       )
							a__					=  batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase   ,       __lowerCAmelCase       )
							# Test that special tokens are reset

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


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

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


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

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							a__					=  tokenizer(
							    ["""I am a small frog""" * 1024, """I am a small frog"""]   ,       padding=__lowerCAmelCase   ,       truncation=__lowerCAmelCase   ,       return_tensors="""pt"""       )
							self.assertIsInstance(__lowerCAmelCase   ,       __lowerCAmelCase       )
							self.assertEqual(batch.input_ids.shape   ,       (2, 1024)       )

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  ["""A long paragraph for summarization."""]
				a__					=  [
				    """Summary of the text.""",
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							a__					=  tokenizer(__lowerCAmelCase   ,       return_tensors="""pt"""       )
							a__					=  tokenizer(text_target=__lowerCAmelCase   ,       return_tensors="""pt"""       )
							a__					=  inputs["""input_ids"""]
							a__					=  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 				_A			(	self       ):



				'''simple docstring'''


				pass







	def 				_A			(	self       ):



				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
										a__					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase   ,       **__lowerCAmelCase       )
										a__					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase   ,       **__lowerCAmelCase       )
										a__					=  """A, <mask> AllenNLP sentence."""
										a__					=  tokenizer_r.encode_plus(__lowerCAmelCase   ,       add_special_tokens=__lowerCAmelCase   ,       return_token_type_ids=__lowerCAmelCase       )
										a__					=  tokenizer_p.encode_plus(__lowerCAmelCase   ,       add_special_tokens=__lowerCAmelCase   ,       return_token_type_ids=__lowerCAmelCase       )

										# 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"""]       )   ,       )

										a__					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]       )
										a__					=  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(
										    __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, is_vision_available


lowerCAmelCase_			:			Tuple    	=   {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Any    	=   [
           "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

       lowerCAmelCase_			:			str    	=   _LazyModule(__name__, globals()["__file__"], _import_structure)






