"""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     UpperCamelCase__(			unittest.TestCase ):





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=7		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=99		,__UpperCAmelCase=32		,__UpperCAmelCase=5		,__UpperCAmelCase=4		,__UpperCAmelCase=37		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.1		,__UpperCAmelCase=5_12		,__UpperCAmelCase=16		,__UpperCAmelCase=2		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=4		,)					->				Optional[Any]:
													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 				snake_case__	(       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=__UpperCAmelCase		,initializer_range=self.initializer_range		,)

													return config, input_ids, token_type_ids, attention_mask





						def 				snake_case__	(       self     )					->				Any:
													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     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						int													=		True

						lowerCAmelCase__  :						Tuple													=		(
						    (
						        FlaxRoFormerModel,
						        FlaxRoFormerForMaskedLM,
						        FlaxRoFormerForSequenceClassification,
						        FlaxRoFormerForTokenClassification,
						        FlaxRoFormerForMultipleChoice,
						        FlaxRoFormerForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							FlaxRoFormerModelTester(self     )





						@slow
						def 				snake_case__	(       self     )					->				Any:
													for model_class_name in self.all_model_classes:
																				A__	     =							model_class_name.from_pretrained('junnyu/roformer_chinese_small'		,from_pt=__UpperCAmelCase     )
																				A__	     =							model(np.ones((1, 1)     )     )
																				self.assertIsNotNone(__UpperCAmelCase     )






@require_flax
class     UpperCamelCase__(			unittest.TestCase ):





						@slow
						def 				snake_case__	(       self     )					->				str:
													A__	     =							FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'     )
													A__	     =							jnp.array([[0, 1, 2, 3, 4, 5]]     )
													A__	     =							model(__UpperCAmelCase     )[0]

													A__	     =							5_00_00

													A__	     =							(1, 6, vocab_size)
													self.assertEqual(output.shape		,__UpperCAmelCase     )

													A__	     =							jnp.array(
													    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]     )

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






"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__lowerCamelCase											=  logging.get_logger(__name__)

__lowerCamelCase											=  {"vocab_file": "vocab.txt"}

__lowerCamelCase											=  {
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

__lowerCamelCase											=  {
    "facebook/esm2_t6_8M_UR50D": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							with open(UpperCamelCase__       ,    'r'				) as f:
														A__	     =							f.read().splitlines()
														return [l.strip() for l in lines]


class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Any													=		VOCAB_FILES_NAMES
						lowerCAmelCase__  :						List[str]													=		PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__  :						List[Any]													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__  :						Dict													=		['input_ids', 'attention_mask']





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase="<unk>"		,__UpperCAmelCase="<cls>"		,__UpperCAmelCase="<pad>"		,__UpperCAmelCase="<mask>"		,__UpperCAmelCase="<eos>"		,**__UpperCAmelCase		,)					->				Optional[int]:
													super().__init__(**__UpperCAmelCase     )
													A__	     =							load_vocab_file(__UpperCAmelCase     )
													A__	     =							dict(enumerate(self.all_tokens     )     )
													A__	     =							{tok: ind for ind, tok in enumerate(self.all_tokens     )}
													A__	     =							unk_token
													A__	     =							cls_token
													A__	     =							pad_token
													A__	     =							mask_token
													A__	     =							eos_token
													A__	     =							self.all_tokens
													self._create_trie(self.unique_no_split_tokens     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self._id_to_token.get(__UpperCAmelCase		,self.unk_token     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													return self._token_to_id.get(__UpperCAmelCase		,self._token_to_id.get(self.unk_token     )     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													return text.split()





						def 				snake_case__	(       self		,__UpperCAmelCase=False     )					->				int:
													return len(self._id_to_token     )





						def 				snake_case__	(       self     )					->				List[str]:
													return {token: i for i, token in enumerate(self.all_tokens     )}





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													return self._token_to_id.get(__UpperCAmelCase		,self._token_to_id.get(self.unk_token     )     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self._id_to_token.get(__UpperCAmelCase		,self.unk_token     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				List[int]:
													A__	     =							[self.cls_token_id]
													A__	     =							[self.eos_token_id]  # No sep token in ESM vocabulary
													if token_ids_a is None:
																				if self.eos_token_id is None:
																											return cls + token_ids_a
																				else:
																											return cls + token_ids_a + sep
													elif self.eos_token_id is None:
																				raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'     )
													return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = False     )					->				List[int]:
													if already_has_special_tokens:
																				if token_ids_a is not None:
																											raise ValueError(
																											    'You should not supply a second sequence if the provided sequence of '
																											    'ids is already formatted with special tokens for the model.'     )

																				return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
													A__	     =							[1] + ([0] * len(__UpperCAmelCase     )) + [1]
													if token_ids_a is not None:
																				mask += [0] * len(__UpperCAmelCase     ) + [1]
													return mask





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[str]:
													A__	     =							os.path.join(__UpperCAmelCase		,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'     )
													with open(__UpperCAmelCase		,'w'     ) as f:
																				f.write('\n'.join(self.all_tokens     )     )
													return (vocab_file,)





						@property
						def 				snake_case__	(       self     )					->				int:
													return self.get_vocab_size(with_added_tokens=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = False     )					->				int:
													return super()._add_tokens(__UpperCAmelCase		,special_tokens=__UpperCAmelCase     )





