





'''simple docstring'''







import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def __init__(  self     :					str					,				__a     :					Any					,				__a     :					List[str]=13					,				__a     :					Tuple=7					,				__a     :					List[str]=True					,				__a     :					Union[str, Any]=True					,				__a     :					List[Any]=True					,				__a     :					List[Any]=True					,				__a     :					Union[str, Any]=99					,				__a     :					str=32					,				__a     :					Union[str, Any]=5					,				__a     :					int=4					,				__a     :					Tuple=37					,				__a     :					int="gelu"					,				__a     :					List[str]=0.1					,				__a     :					Any=0.1					,				__a     :					Union[str, Any]=5_12					,				__a     :					Optional[Any]=16					,				__a     :					Any=2					,				__a     :					str=0.02					,				__a     :					str=4					,				):
								_a								= parent
								_a								= batch_size
								_a								= seq_length
								_a								= is_training
								_a								= use_attention_mask
								_a								= use_token_type_ids
								_a								= use_labels
								_a								= vocab_size
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= intermediate_size
								_a								= hidden_act
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= max_position_embeddings
								_a								= type_vocab_size
								_a								= type_sequence_label_size
								_a								= initializer_range
								_a								= num_choices




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size					)

								_a								= None
								if self.use_attention_mask:
												_a								= random_attention_mask([self.batch_size, self.seq_length]					)

								_a								= None
								if self.use_token_type_ids:
												_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size					)

								_a								= RoFormerConfig(
								    vocab_size=self.vocab_size					,				hidden_size=self.hidden_size					,				num_hidden_layers=self.num_hidden_layers					,				num_attention_heads=self.num_attention_heads					,				intermediate_size=self.intermediate_size					,				hidden_act=self.hidden_act					,				hidden_dropout_prob=self.hidden_dropout_prob					,				attention_probs_dropout_prob=self.attention_probs_dropout_prob					,				max_position_embeddings=self.max_position_embeddings					,				type_vocab_size=self.type_vocab_size					,				is_decoder=__a					,				initializer_range=self.initializer_range					,				)

								return config, input_ids, token_type_ids, attention_mask




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.prepare_config_and_inputs()
								_a	,					_a	,					_a	,					_a								= config_and_inputs
								_a								= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict


@require_flax
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =True

				__a            =(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= FlaxRoFormerModelTester(self					)




				@slow
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								for model_class_name in self.all_model_classes:
												_a								= model_class_name.from_pretrained("junnyu/roformer_chinese_small"					,				from_pt=__a					)
												_a								= model(np.ones((1, 1)					)					)
												self.assertIsNotNone(__a					)


@require_flax
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@slow
				def   UpperCamelCase__   (  self     :					str					):
								_a								= FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"					)
								_a								= jnp.array([[0, 1, 2, 3, 4, 5]]					)
								_a								= model(__a					)[0]

								_a								= 5_00_00

								_a								= (1, 6, vocab_size)
								self.assertEqual(output.shape					,				__a					)

								_a								= jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]					)

								self.assertTrue(jnp.allclose(output[:, :3, :3]					,				__a					,				atol=1e-4					)					)




"""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      lowerCamelCase__   (     A     ,    unittest.TestCase				):







					"""simple docstring"""


					__a	       =							None
					__a	       =							BloomTokenizerFast
					__a	       =							BloomTokenizerFast
					__a	       =							True
					__a	       =							False
					__a	       =							"""tokenizer_file"""
					__a	       =							{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												super().setUp()
												__UpperCAmelCase   :						Any      							=					BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""  )
												tokenizer.save_pretrained(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Any ,       **UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					self.get_rust_tokenizer()

												__UpperCAmelCase   :						Optional[Any]      							=					["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
												__UpperCAmelCase   :						int      							=					[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

												__UpperCAmelCase   :						Dict      							=					tokenizer.batch_encode_plus(UpperCamelCase  )["""input_ids"""]
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					tokenizer.batch_decode(UpperCamelCase  )
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : Any=6  ):
												'''simple docstring'''






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

																										__UpperCAmelCase   :						Union[str, Any]      							=					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 lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					self.get_rust_tokenizer()
												__UpperCAmelCase   :						Optional[Any]      							=					load_dataset("""xnli""" ,       """all_languages""" ,       split="""test""" ,       streaming=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					next(iter(UpperCamelCase  )  )["""premise"""]  # pick up one data
												__UpperCAmelCase   :						Any      							=					list(sample_data.values()  )

												__UpperCAmelCase   :						Optional[Any]      							=					list(map(tokenizer.encode ,       UpperCamelCase  )  )
												__UpperCAmelCase   :						List[Any]      							=					[tokenizer.decode(UpperCamelCase ,       clean_up_tokenization_spaces=UpperCamelCase  ) for x in output_tokens]
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''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  )
