
import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     LongformerTokenizer
	snake_case_					    =     True
	snake_case_					    =     LongformerTokenizerFast
	snake_case_					    =     True






	def 				UpperCamelCase_  (       self					: Any ):
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__A   			= [
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "\u0120",
					    "\u0120l",
					    "\u0120n",
					    "\u0120lo",
					    "\u0120low",
					    "er",
					    "\u0120lowest",
					    "\u0120newer",
					    "\u0120wider",
					    "<unk>",
					]
					__A   			= dict(zip(A							,range(len(A ) ) ) )
					__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(A ) + "\n" )
					with open(self.merges_file							,"w"							,encoding="utf-8" ) as fp:
									fp.write("\n".join(A ) )






	def 				UpperCamelCase_  (       self					: Tuple							,**A					: Dict ):
					kwargs.update(self.special_tokens_map )
					return self.tokenizer_class.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: int							,**A					: Optional[Any] ):
					kwargs.update(self.special_tokens_map )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: Any							,A					: Tuple ):
					__A   			= "lower newer"
					__A   			= "lower newer"
					return input_text, output_text






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.tokenizer_class(self.vocab_file							,self.merges_file							,**self.special_tokens_map )
					__A   			= "lower newer"
					__A   			= ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
					__A   			= tokenizer.tokenize(A )  # , add_prefix_space=True)
					self.assertListEqual(A							,A )

					__A   			= tokens + [tokenizer.unk_token]
					__A   			= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A )							,A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.get_tokenizer()

					self.assertListEqual(tokenizer.encode("Hello world!"							,add_special_tokens=A )							,[0, 3_14_14, 2_32, 3_28, 2] )
					self.assertListEqual(
					    tokenizer.encode("Hello world! cécé herlolip 418"							,add_special_tokens=A )							,[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]							,)






	@slow
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )

					__A   			= tokenizer.encode("sequence builders"							,add_special_tokens=A )
					__A   			= tokenizer.encode("multi-sequence build"							,add_special_tokens=A )

					__A   			= tokenizer.encode(
					    "sequence builders"							,add_special_tokens=A							,add_prefix_space=A )
					__A   			= tokenizer.encode(
					    "sequence builders"							,"multi-sequence build"							,add_special_tokens=A							,add_prefix_space=A )

					__A   			= tokenizer.build_inputs_with_special_tokens(A )
					__A   			= tokenizer.build_inputs_with_special_tokens(A							,A )

					assert encoded_sentence == encoded_text_from_decode
					assert encoded_pair == encoded_pair_from_decode






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.get_tokenizer()

					__A   			= "Encode this sequence."
					__A   			= tokenizer.byte_encoder[" ".encode("utf-8" )[0]]

					# Testing encoder arguments
					__A   			= tokenizer.encode(A							,add_special_tokens=A							,add_prefix_space=A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[0] )[0]
					self.assertNotEqual(A							,A )

					__A   			= tokenizer.encode(A							,add_special_tokens=A							,add_prefix_space=A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[0] )[0]
					self.assertEqual(A							,A )

					tokenizer.add_special_tokens({"bos_token": "<s>"} )
					__A   			= tokenizer.encode(A							,add_special_tokens=A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[1] )[0]
					self.assertNotEqual(A							,A )

					# Testing spaces after special tokens
					__A   			= "<mask>"
					tokenizer.add_special_tokens(
					    {"mask_token": AddedToken(A							,lstrip=A							,rstrip=A )} )  # mask token has a left space
					__A   			= tokenizer.convert_tokens_to_ids(A )

					__A   			= "Encode <mask> sequence"
					__A   			= "Encode <mask>sequence"

					__A   			= tokenizer.encode(A )
					__A   			= encoded.index(A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
					self.assertEqual(A							,A )

					__A   			= tokenizer.encode(A )
					__A   			= encoded.index(A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
					self.assertNotEqual(A							,A )






	def 				UpperCamelCase_  (       self					: int ):
					pass






	def 				UpperCamelCase_  (       self					: str ):
					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(A							,**A )
													__A   			= self.tokenizer_class.from_pretrained(A							,**A )
													__A   			= "A, <mask> AllenNLP sentence."
													__A   			= tokenizer_r.encode_plus(A							,add_special_tokens=A							,return_token_type_ids=A )
													__A   			= tokenizer_p.encode_plus(A							,add_special_tokens=A							,return_token_type_ids=A )

													# 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, 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(
													    A							,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
													self.assertSequenceEqual(
													    A							,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )






	def 				UpperCamelCase_  (       self					: int ):
					for trim_offsets, add_prefix_space in itertools.product([True, False]							,repeat=2 ):
									__A   			= self.rust_tokenizer_class.from_pretrained(
									    self.tmpdirname							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )

									__A   			= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
									__A   			= json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

									self.assertEqual(pre_tokenizer_state["add_prefix_space"]							,A )

									self.assertEqual(post_processor_state["add_prefix_space"]							,A )
									self.assertEqual(post_processor_state["trim_offsets"]							,A )







	def 				UpperCamelCase_  (       self					: Any ):
					# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
					# `trim_offsets`
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
													__A   			= "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													__A   			= f'''{text_of_1_token} {text_of_1_token}'''

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ) + 1, len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ) + 1, len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ), len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ), len(A ) + 1 + len(A ))							,)

													__A   			= f''' {text}'''

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(1, 1 + len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(1 + len(A ) + 1, 1 + len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, 1 + len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(1 + len(A ), 1 + len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, 1 + len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(1 + len(A ), 1 + len(A ) + 1 + len(A ))							,)








from typing import TYPE_CHECKING

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


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	UpperCAmelCase_ :						List[Any]      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
