






"""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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




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





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

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





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		return "lower newer", "lower newer"





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





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





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		A_		:    Any						     = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = 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_		:    int						     = batch.input_ids.tolist()[0]
			self.assertListEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Tuple						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIn("""input_ids"""  ,  lowerCAmelCase_				)
			self.assertIn("""attention_mask"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""labels"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""decoder_attention_mask"""  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Tuple						     = [
		    """Summary of the text.""",
		    """Another summary.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = tokenizer(text_target=lowerCAmelCase_  ,  max_length=32  ,  padding="""max_length"""  ,  return_tensors="""pt"""				)
			self.assertEqual(32  ,  targets["""input_ids"""].shape[1]				)





	@require_torch
	def  lowerCamelCase(self				):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[Any]						     = 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, 5122)				)





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

			A_		:    Optional[Any]						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_				)
			A_		:    Any						     = [[0] * len(lowerCAmelCase_				) for x in encoded_output["""input_ids"""]]
			A_		:    Tuple						     = tokenizer.pad(lowerCAmelCase_				)
			self.assertSequenceEqual(outputs["""global_attention_mask"""]  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
				A_		:    Any						     = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = self.tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = """A, <mask> AllenNLP sentence."""
				A_		:    List[Any]						     = tokenizer_r.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				A_		:    Any						     = tokenizer_p.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				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"""]				)  ,  )

				A_		:    List[Any]						     = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
				A_		:    Union[str, Any]						     = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]				)
				self.assertSequenceEqual(tokens_p["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)
				self.assertSequenceEqual(tokens_r["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 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"""
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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




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





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

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





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		return "lower newer", "lower newer"





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





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





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		A_		:    Any						     = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = 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_		:    int						     = batch.input_ids.tolist()[0]
			self.assertListEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Tuple						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIn("""input_ids"""  ,  lowerCAmelCase_				)
			self.assertIn("""attention_mask"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""labels"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""decoder_attention_mask"""  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Tuple						     = [
		    """Summary of the text.""",
		    """Another summary.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = tokenizer(text_target=lowerCAmelCase_  ,  max_length=32  ,  padding="""max_length"""  ,  return_tensors="""pt"""				)
			self.assertEqual(32  ,  targets["""input_ids"""].shape[1]				)





	@require_torch
	def  lowerCamelCase(self				):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[Any]						     = 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, 5122)				)





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

			A_		:    Optional[Any]						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_				)
			A_		:    Any						     = [[0] * len(lowerCAmelCase_				) for x in encoded_output["""input_ids"""]]
			A_		:    Tuple						     = tokenizer.pad(lowerCAmelCase_				)
			self.assertSequenceEqual(outputs["""global_attention_mask"""]  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
				A_		:    Any						     = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = self.tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = """A, <mask> AllenNLP sentence."""
				A_		:    List[Any]						     = tokenizer_r.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				A_		:    Any						     = tokenizer_p.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				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"""]				)  ,  )

				A_		:    List[Any]						     = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
				A_		:    Union[str, Any]						     = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]				)
				self.assertSequenceEqual(tokens_p["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)
				self.assertSequenceEqual(tokens_r["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 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>"""]				)

