


import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :Tuple						=   GPTSanJapaneseTokenizer
					lowerCAmelCase :List[Any]						=   False
					lowerCAmelCase :List[Any]						=   {"""do_clean_text""": False, """add_prefix_space""": False}




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

								# fmt: off
								UpperCAmelCase__					:   str       				=       ["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
								# fmt: on
								UpperCAmelCase__					:   Union[str, Any]       				=       {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
								UpperCAmelCase__					:   Tuple       				=       {"""unk_token""": """<unk>"""}

								UpperCAmelCase__					:   int       				=       os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES["""vocab_file"""])
								UpperCAmelCase__					:   str       				=       os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES["""emoji_file"""])
								with open(self.vocab_file							,							"""w"""							,							encoding="""utf-8""") as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))
								with open(self.emoji_file							,							"""w""") as emoji_writer:
											emoji_writer.write(json.dumps(_lowercase))




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								kwargs.update(self.special_tokens_map)
								return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname							,							**_lowercase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Any       				=       """こんにちは、世界。 \nこんばんは、㔺界。😀"""
								UpperCAmelCase__					:   Any       				=       """こんにちは、世界。 \nこんばんは、世界。😀"""
								return input_text, output_text




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__				,    UpperCAmelCase__					:   Dict       				=       self.get_input_output_texts(_lowercase)
								UpperCAmelCase__					:   int       				=       tokenizer.encode(_lowercase							,							add_special_tokens=_lowercase)
								UpperCAmelCase__					:   Optional[int]       				=       tokenizer.decode(_lowercase							,							clean_up_tokenization_spaces=_lowercase)
								return text, ids




					def 			snake_case__  ( self):
								pass  # TODO add if relevant




					def 			snake_case__  ( self):
								pass  # TODO add if relevant




					def 			snake_case__  ( self):
								pass  # TODO add if relevant




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       self.get_tokenizer()

								# Testing tokenization
								UpperCAmelCase__					:   Optional[Any]       				=       """こんにちは、世界。　こんばんは、㔺界。"""
								UpperCAmelCase__					:   Optional[Any]       				=       ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
								UpperCAmelCase__					:   str       				=       tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase							,							_lowercase)

								# Testing conversion to ids without special tokens
								UpperCAmelCase__					:   Dict       				=       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
								UpperCAmelCase__					:   List[Any]       				=       tokenizer.convert_tokens_to_ids(_lowercase)
								self.assertListEqual(_lowercase							,							_lowercase)

								# Testing conversion to ids with special tokens
								UpperCAmelCase__					:   Any       				=       tokens + [tokenizer.unk_token]
								UpperCAmelCase__					:   Optional[int]       				=       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
								UpperCAmelCase__					:   Tuple       				=       tokenizer.convert_tokens_to_ids(_lowercase)
								self.assertListEqual(_lowercase							,							_lowercase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       self.get_tokenizer()

								# Testing tokenization
								UpperCAmelCase__					:   str       				=       """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
								UpperCAmelCase__					:   List[str]       				=       """こんにちは、、、、世界。こんばんは、、、、世界。"""
								UpperCAmelCase__					:   str       				=       tokenizer.encode(_lowercase)
								UpperCAmelCase__					:   int       				=       tokenizer.decode(_lowercase)
								self.assertEqual(_lowercase							,							_lowercase)




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""")

								# Testing tokenization
								UpperCAmelCase__					:   List[Any]       				=       """こんにちは、世界。"""
								UpperCAmelCase__					:   Optional[int]       				=       """こんばんは、㔺界。😀"""
								UpperCAmelCase__					:   Dict       				=       """こんにちは、世界。こんばんは、世界。😀"""
								UpperCAmelCase__					:   Tuple       				=       tokenizer.encode(prefix_text + input_text)
								UpperCAmelCase__					:   int       				=       tokenizer.encode(""""""							,							prefix_text=prefix_text + input_text)
								UpperCAmelCase__					:   Union[str, Any]       				=       tokenizer.encode(_lowercase							,							prefix_text=_lowercase)
								UpperCAmelCase__					:   Dict       				=       tokenizer.decode(_lowercase)
								UpperCAmelCase__					:   List[str]       				=       tokenizer.decode(_lowercase)
								UpperCAmelCase__					:   Tuple       				=       tokenizer.decode(_lowercase)
								self.assertEqual(_lowercase							,							_lowercase)
								self.assertEqual(_lowercase							,							_lowercase)
								self.assertEqual(_lowercase							,							_lowercase)




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""")

								# Testing tokenization
								UpperCAmelCase__					:   Union[str, Any]       				=       """こんにちは、世界。"""
								UpperCAmelCase__					:   Dict       				=       """こんばんは、㔺界。😀"""

								UpperCAmelCase__					:   Union[str, Any]       				=       len(tokenizer.encode(_lowercase)) - 2
								UpperCAmelCase__					:   List[str]       				=       len(tokenizer.encode(_lowercase)) - 2

								UpperCAmelCase__					:   int       				=       [1] + [0] * (len_prefix + len_text + 1)
								UpperCAmelCase__					:   Any       				=       [1] * (len_prefix + len_text + 1) + [0]
								UpperCAmelCase__					:   Optional[Any]       				=       [1] + [1] * (len_prefix) + [0] * (len_text + 1)

								UpperCAmelCase__					:   List[str]       				=       tokenizer(prefix_text + input_text).token_type_ids
								UpperCAmelCase__					:   Union[str, Any]       				=       tokenizer(""""""							,							prefix_text=prefix_text + input_text).token_type_ids
								UpperCAmelCase__					:   List[str]       				=       tokenizer(_lowercase							,							prefix_text=_lowercase).token_type_ids
								self.assertListEqual(_lowercase							,							_lowercase)
								self.assertListEqual(_lowercase							,							_lowercase)
								self.assertListEqual(_lowercase							,							_lowercase)




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""")

								UpperCAmelCase__					:   int       				=       tokenizer.encode("""あンいワ""")
								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer.encode(""""""							,							prefix_text="""あンいワ""")
								UpperCAmelCase__					:   Union[str, Any]       				=       tokenizer.encode("""いワ"""							,							prefix_text="""あン""")

								self.assertEqual(tokenizer.decode(_lowercase)							,							tokenizer.decode(_lowercase))
								self.assertEqual(tokenizer.decode(_lowercase)							,							tokenizer.decode(_lowercase))
								self.assertNotEqual(_lowercase							,							_lowercase)
								self.assertNotEqual(_lowercase							,							_lowercase)
								self.assertEqual(x_token_a[1]							,							x_token_a[-1])  # SEG token
								self.assertEqual(x_token_a[1]							,							x_token_a[3])  # SEG token




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""")

								UpperCAmelCase__					:   Optional[Any]       				=       [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
								UpperCAmelCase__					:   Optional[int]       				=       tokenizer(_lowercase							,							padding=_lowercase)
								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer.batch_encode_plus(_lowercase							,							padding=_lowercase)

								# fmt: off
								UpperCAmelCase__					:   Optional[int]       				=       [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
								UpperCAmelCase__					:   List[Any]       				=       [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
								UpperCAmelCase__					:   Dict       				=       [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
								# fmt: on
								self.assertListEqual(x_token.input_ids							,							_lowercase)
								self.assertListEqual(x_token.token_type_ids							,							_lowercase)
								self.assertListEqual(x_token.attention_mask							,							_lowercase)
								self.assertListEqual(x_token_a.input_ids							,							_lowercase)
								self.assertListEqual(x_token_a.token_type_ids							,							_lowercase)
								self.assertListEqual(x_token_a.attention_mask							,							_lowercase)




					def 			snake_case__  ( self):
								# Intentionally convert some words to accommodate character fluctuations unique to Japanese
								pass




					def 			snake_case__  ( self):
								# tokenizer has no padding token
								pass



'''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 						_snake_case   (       unittest.TestCase		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=5							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase="gelu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=512							,							_lowerCamelCase=16							,							_lowerCamelCase=2							,							_lowerCamelCase=0.02							,							_lowerCamelCase=4							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   str       				=       batch_size
								UpperCAmelCase__					:   Tuple       				=       seq_length
								UpperCAmelCase__					:   Any       				=       is_training
								UpperCAmelCase__					:   Tuple       				=       use_attention_mask
								UpperCAmelCase__					:   Optional[Any]       				=       use_token_type_ids
								UpperCAmelCase__					:   Optional[Any]       				=       use_labels
								UpperCAmelCase__					:   str       				=       vocab_size
								UpperCAmelCase__					:   Dict       				=       hidden_size
								UpperCAmelCase__					:   int       				=       num_hidden_layers
								UpperCAmelCase__					:   Optional[int]       				=       num_attention_heads
								UpperCAmelCase__					:   Optional[int]       				=       intermediate_size
								UpperCAmelCase__					:   Tuple       				=       hidden_act
								UpperCAmelCase__					:   Optional[Any]       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Union[str, Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[Any]       				=       max_position_embeddings
								UpperCAmelCase__					:   Optional[int]       				=       type_vocab_size
								UpperCAmelCase__					:   str       				=       type_sequence_label_size
								UpperCAmelCase__					:   Optional[int]       				=       initializer_range
								UpperCAmelCase__					:   str       				=       num_choices




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

								UpperCAmelCase__					:   Union[str, Any]       				=       None
								if self.use_attention_mask:
											UpperCAmelCase__					:   Tuple       				=       random_attention_mask([self.batch_size, self.seq_length])

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

								UpperCAmelCase__					:   Optional[Any]       				=       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=_lowerCamelCase							,							initializer_range=self.initializer_range							,							)

								return config, input_ids, token_type_ids, attention_mask




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       self.prepare_config_and_inputs()
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       config_and_inputs
								UpperCAmelCase__					:   Dict       				=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict


@require_flax
class 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :Optional[int]						=   True

					lowerCAmelCase :Any						=   (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       FlaxRoFormerModelTester(self)




					@slow
					def 			snake_case__  ( self):
								for model_class_name in self.all_model_classes:
											UpperCAmelCase__					:   List[str]       				=       model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,							from_pt=_lowerCamelCase)
											UpperCAmelCase__					:   Dict       				=       model(np.ones((1, 1)))
											self.assertIsNotNone(_lowerCamelCase)


@require_flax
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""")
								UpperCAmelCase__					:   int       				=       jnp.array([[0, 1, 2, 3, 4, 5]])
								UpperCAmelCase__					:   Optional[int]       				=       model(_lowerCamelCase)[0]

								UpperCAmelCase__					:   Union[str, Any]       				=       5_0000

								UpperCAmelCase__					:   Any       				=       (1, 6, vocab_size)
								self.assertEqual(output.shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Union[str, Any]       				=       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]							,							_lowerCamelCase							,							atol=1e-4))