





'''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 (					__lowercase						,					unittest.TestCase		):
		lowercase__:   Optional[int]				     =   LEDTokenizer
		lowercase__:   Optional[Any]				     =   LEDTokenizerFast
		lowercase__:   Optional[Any]				     =   True

		def 	lowercase__     (   self					:   Any			)					->    int:


					"""simple docstring"""

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

					__snake_case					:      int							      =     os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["""vocab_file"""]			)
					__snake_case					:      Optional[int]							      =     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 	lowercase__     (   self					:   List[str]  ,      **__magic_name__					:   Dict			)					->    Any:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Tuple  ,      **__magic_name__					:   List[str]			)					->    Optional[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   List[Any]			)					->    Dict:


					"""simple docstring"""

					return "lower newer", "lower newer"

		@cached_property
		def 	lowercase__     (   self					:   str			)					->    Optional[Any]:


					"""simple docstring"""

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

		@cached_property
		def 	lowercase__     (   self					:   Tuple			)					->    List[Any]:


					"""simple docstring"""

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

		@require_torch
		def 	lowercase__     (   self					:   Dict			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					__snake_case					:      List[str]							      =     [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]:
								__snake_case					:      Optional[Any]							      =     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			)
								__snake_case					:      Tuple							      =     batch.input_ids.tolist()[0]
								self.assertListEqual(__UpperCamelCase  ,      __UpperCamelCase			)

		@require_torch
		def 	lowercase__     (   self					:   List[Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__snake_case					:      int							      =     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 	lowercase__     (   self					:   List[str]			)					->    str:


					"""simple docstring"""

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

		@require_torch
		def 	lowercase__     (   self					:   int			)					->    Dict:


					"""simple docstring"""

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__snake_case					:      List[Any]							      =     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 	lowercase__     (   self					:   List[str]			)					->    Union[str, Any]:


					"""simple docstring"""

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


					"""simple docstring"""

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__snake_case					:      Optional[int]							      =     ["""Summary of the text.""", """Another summary."""]
								__snake_case					:      Tuple							      =     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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

		def 	lowercase__     (   self					:   int			)					->    Tuple:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   str			)					->    Optional[int]:


					"""simple docstring"""

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

											__snake_case					:      List[Any]							      =     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]			)
											__snake_case					:      List[Any]							      =     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>"""]			)




from typing import TYPE_CHECKING

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


lowercase   =	{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2TokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2FeatureExtractor''']
		lowercase   =	['''LayoutLMv2ImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv2ForQuestionAnswering''',
		    '''LayoutLMv2ForSequenceClassification''',
		    '''LayoutLMv2ForTokenClassification''',
		    '''LayoutLMv2Layer''',
		    '''LayoutLMv2Model''',
		    '''LayoutLMv2PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaLayer,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)
else:
		import sys

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





