

"""simple docstring"""


from typing import TYPE_CHECKING

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


__A						   =			{"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}

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

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						   =			["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						   =			[
			    "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

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





"""simple docstring"""


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      str         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      int         =  True
				SCREAMING_SNAKE_CASE_   :      Tuple         =  False
				SCREAMING_SNAKE_CASE_   :      int         =  """tokenizer_file"""
				SCREAMING_SNAKE_CASE_   :      List[str]         =  {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

				def 							lowercase_     (	self							:				List[Any])->   Dict:



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase:       Optional[Any]          =			BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
						tokenizer.save_pretrained(self.tmpdirname)

				def 							lowercase_     (	self							:				List[Any]     ,  **UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.get_rust_tokenizer()

						__lowerCAmelCase:       int          =			["The quick brown fox</s>", "jumps over the lazy dog</s>"]
						__lowerCAmelCase:       List[str]          =			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

						__lowerCAmelCase:       List[str]          =			tokenizer.batch_encode_plus(UpperCamelCase__)["input_ids"]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			tokenizer.batch_decode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple=6)->   Tuple:



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Optional[Any]          =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										# tokenizer_r.pad_token = None # Hotfixing padding = None
										# Simple input
										__lowerCAmelCase:       Dict          =			"This is a simple input"
										__lowerCAmelCase:       str          =			["This is a simple input 1", "This is a simple input 2"]
										__lowerCAmelCase:       int          =			("This is a simple input", "This is a pair")
										__lowerCAmelCase:       Union[str, Any]          =			[
										    ("This is a simple input 1", "This is a simple input 2"),
										    ("This is a simple pair 1", "This is a simple pair 2"),
										]

										# Simple input tests
										try:
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)

												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
										except ValueError:
												self.fail("Bloom Tokenizer should be able to deal with padding")

										__lowerCAmelCase:       Tuple          =			None  # Hotfixing padding = None
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.get_rust_tokenizer()
						__lowerCAmelCase:       List[str]          =			load_dataset("xnli"     ,  "all_languages"     ,  split="test"     ,  streaming=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			next(iter(UpperCamelCase__))["premise"]  # pick up one data
						__lowerCAmelCase:       Any          =			list(sample_data.values())

						__lowerCAmelCase:       int          =			list(map(tokenizer.encode     ,  UpperCamelCase__))
						__lowerCAmelCase:       str          =			[tokenizer.decode(UpperCamelCase__     ,  clean_up_tokenization_spaces=UpperCamelCase__) for x in output_tokens]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)




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



						'''simple docstring'''


						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)     ,  1)
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])     ,  1)


