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 						a__							(       snake_case__				,						unittest.TestCase		):
			A__    :       Union[str, Any]        =      LongformerTokenizer
			A__    :       int        =      True
			A__    :       Optional[int]        =      LongformerTokenizerFast
			A__    :       Optional[int]        =      True



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[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(UpperCAmelCase_				,						range(len(UpperCAmelCase_   )   )   )   )
								__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(UpperCAmelCase_   ) + '\n'   )
								with open(self.merges_file				,						'w'				,						encoding='utf-8'   ) as fp:
													fp.write('\n'.join(UpperCAmelCase_   )   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Tuple:
								kwargs.update(self.special_tokens_map   )
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,						**UpperCAmelCase_   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Optional[Any]:
								kwargs.update(self.special_tokens_map   )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,						**UpperCAmelCase_   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Tuple:
								__a		 =       "lower newer"
								__a		 =       "lower newer"
								return input_text, output_text



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__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(UpperCAmelCase_   )  # , add_prefix_space=True)
								self.assertListEqual(UpperCAmelCase_				,						UpperCAmelCase_   )

								__a		 =       tokens + [tokenizer.unk_token]
								__a		 =       [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )				,						UpperCAmelCase_   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('Hello world!'				,						add_special_tokens=UpperCAmelCase_   )				,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]   )
								self.assertListEqual(
								    tokenizer.encode('Hello world! cécé herlolip 418'				,						add_special_tokens=UpperCAmelCase_   )				,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]				,						)



			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'   )

								__a		 =       tokenizer.encode('sequence builders'				,						add_special_tokens=UpperCAmelCase_   )
								__a		 =       tokenizer.encode('multi-sequence build'				,						add_special_tokens=UpperCAmelCase_   )

								__a		 =       tokenizer.encode(
								    'sequence builders'				,						add_special_tokens=UpperCAmelCase_				,						add_prefix_space=UpperCAmelCase_   )
								__a		 =       tokenizer.encode(
								    'sequence builders'				,						'multi-sequence build'				,						add_special_tokens=UpperCAmelCase_				,						add_prefix_space=UpperCAmelCase_   )

								__a		 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_   )
								__a		 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,						UpperCAmelCase_   )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.get_tokenizer()

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

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

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

								tokenizer.add_special_tokens({'bos_token': '<s>'}   )
								__a		 =       tokenizer.encode(UpperCAmelCase_				,						add_special_tokens=UpperCAmelCase_   )
								__a		 =       tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
								self.assertNotEqual(UpperCAmelCase_				,						UpperCAmelCase_   )

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

								__a		 =       "Encode <mask> sequence"
								__a		 =       "Encode <mask>sequence"

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

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								pass



			def __SCREAMING_SNAKE_CASE							(						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(UpperCAmelCase_				,						**UpperCAmelCase_   )
																		__a		 =       self.tokenizer_class.from_pretrained(UpperCAmelCase_				,						**UpperCAmelCase_   )
																		__a		 =       "A, <mask> AllenNLP sentence."
																		__a		 =       tokenizer_r.encode_plus(UpperCAmelCase_				,						add_special_tokens=UpperCAmelCase_				,						return_token_type_ids=UpperCAmelCase_   )
																		__a		 =       tokenizer_p.encode_plus(UpperCAmelCase_				,						add_special_tokens=UpperCAmelCase_				,						return_token_type_ids=UpperCAmelCase_   )

																		# 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_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )
																		self.assertSequenceEqual(tokens_r['input_ids']				,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[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=UpperCAmelCase_				,						add_prefix_space=UpperCAmelCase_				,						trim_offsets=UpperCAmelCase_   )

													__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']				,						UpperCAmelCase_   )

													self.assertEqual(post_processor_state['add_prefix_space']				,						UpperCAmelCase_   )
													self.assertEqual(post_processor_state['trim_offsets']				,						UpperCAmelCase_   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								# 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(
																		    UpperCAmelCase_				,						use_fast=UpperCAmelCase_				,						add_prefix_space=UpperCAmelCase_				,						trim_offsets=UpperCAmelCase_   )
																		__a		 =       tokenizer_r(UpperCAmelCase_				,						return_offsets_mapping=UpperCAmelCase_				,						add_special_tokens=UpperCAmelCase_   )
																		self.assertEqual(encoding.offset_mapping[0]				,						(0, len(UpperCAmelCase_   ))   )
																		self.assertEqual(
																		    encoding.offset_mapping[1]				,						(len(UpperCAmelCase_   ) + 1, len(UpperCAmelCase_   ) + 1 + len(UpperCAmelCase_   ))				,						)

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

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

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

																		__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(
																		    UpperCAmelCase_				,						use_fast=UpperCAmelCase_				,						add_prefix_space=UpperCAmelCase_				,						trim_offsets=UpperCAmelCase_   )
																		__a		 =       tokenizer_r(UpperCAmelCase_				,						return_offsets_mapping=UpperCAmelCase_				,						add_special_tokens=UpperCAmelCase_   )
																		self.assertEqual(encoding.offset_mapping[0]				,						(1, 1 + len(UpperCAmelCase_   ))   )
																		self.assertEqual(
																		    encoding.offset_mapping[1]				,						(1 + len(UpperCAmelCase_   ) + 1, 1 + len(UpperCAmelCase_   ) + 1 + len(UpperCAmelCase_   ))				,						)

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

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







from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Dict							=	{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		int							=	[
	    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SwinForImageClassification""",
	    """SwinForMaskedImageModeling""",
	    """SwinModel""",
	    """SwinPreTrainedModel""",
	    """SwinBackbone""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSwinForImageClassification""",
	    """TFSwinForMaskedImageModeling""",
	    """TFSwinModel""",
	    """TFSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

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




