



import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Union[str, Any]									=  RobertaTokenizer
					lowerCAmelCase_				:    List[str]									=  RobertaTokenizerFast
					lowerCAmelCase_				:    List[str]									=  True
					lowerCAmelCase_				:    Optional[int]									=  {'cls_token': '<s>'}



					def    A__		(			self     ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_						=				[
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							UpperCAmelCase_						=				dict(zip(lowerCAmelCase    ,	range(len(lowerCAmelCase     )     )     )     )
							UpperCAmelCase_						=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							UpperCAmelCase_						=				{"unk_token": "<unk>"}

							UpperCAmelCase_						=				os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES["vocab_file"]     )
							UpperCAmelCase_						=				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     ):
							kwargs.update(self.special_tokens_map     )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	**lowerCAmelCase     ):
							kwargs.update(self.special_tokens_map     )
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				"lower newer"
							UpperCAmelCase_						=				"lower newer"
							return input_text, output_text



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

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



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("Hello world!"    ,	add_special_tokens=lowerCAmelCase     )    ,	[0, 3_1414, 232, 328, 2]     )
							self.assertListEqual(
							    tokenizer.encode("Hello world! cécé herlolip 418"    ,	add_special_tokens=lowerCAmelCase     )    ,	[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]    ,	)



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				self.tokenizer_class.from_pretrained("roberta-base"     )

							UpperCAmelCase_						=				tokenizer.encode("sequence builders"    ,	add_special_tokens=lowerCAmelCase     )
							UpperCAmelCase_						=				tokenizer.encode("multi-sequence build"    ,	add_special_tokens=lowerCAmelCase     )

							UpperCAmelCase_						=				tokenizer.encode(
							    "sequence builders"    ,	add_special_tokens=lowerCAmelCase    ,	add_prefix_space=lowerCAmelCase     )
							UpperCAmelCase_						=				tokenizer.encode(
							    "sequence builders"    ,	"multi-sequence build"    ,	add_special_tokens=lowerCAmelCase    ,	add_prefix_space=lowerCAmelCase     )

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

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.get_tokenizer()

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

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

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

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

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

							UpperCAmelCase_						=				"Encode <mask> sequence"
							UpperCAmelCase_						=				"Encode <mask>sequence"

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

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



					def    A__		(			self     ):
							pass



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

											UpperCAmelCase_						=				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
											UpperCAmelCase_						=				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>"]     )



					def    A__		(			self     ):
							for trim_offsets, add_prefix_space in itertools.product([True, False]    ,	repeat=2     ):
									UpperCAmelCase_						=				self.rust_tokenizer_class.from_pretrained(
									    self.tmpdirname    ,	use_fast=lowerCAmelCase    ,	add_prefix_space=lowerCAmelCase    ,	trim_offsets=lowerCAmelCase     )

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

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

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



					def    A__		(			self     ):
							# 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})'''     ):
											UpperCAmelCase_						=				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
											UpperCAmelCase_						=				f'''{text_of_1_token} {text_of_1_token}'''

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

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

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

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

											UpperCAmelCase_						=				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)),
											# )

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

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

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






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

