



"""simple docstring"""






import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

@require_tokenizers
class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					_a       =				EsmTokenizer



					def 						snake_case			(			self				:    Optional[Any]	)->   str:
								super().setUp()
								lowerCamelCase__						:						int						      =tempfile.mkdtemp()
								# fmt: off
								lowerCamelCase__						:						List[str]						      =["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
								# fmt: on
								lowerCamelCase__						:						str						      =os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES['''vocab_file''']	)
								with open(self.vocab_file,    '''w''',    encoding='''utf-8'''	) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)



					def 						snake_case			(			self				:    Union[str, Any],    **lowerCamelCase				:    List[str]	)->   List[PreTrainedTokenizerBase]:
								return [self.get_tokenizer(**lowerCamelCase	)]



					def 						snake_case			(			self				:    Tuple,    **lowerCamelCase				:    Optional[int]	)->   PreTrainedTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname,    **lowerCamelCase	)



					def 						snake_case			(			self				:    Union[str, Any]	)->   Optional[Any]:
								lowerCamelCase__						:						Any						      =self.tokenizer_class(self.vocab_file	)

								lowerCamelCase__						:						List[str]						      =tokenizer.tokenize('''LAGVS'''	)
								self.assertListEqual(lowerCamelCase,    ['''L''', '''A''', '''G''', '''V''', '''S''']	)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	),    [4, 5, 6, 7, 8]	)



					def 						snake_case			(			self				:    int	)->   Tuple:
								lowerCamelCase__						:						Dict						      =self.tokenizer_class(self.vocab_file	)

								lowerCamelCase__						:						int						      ='''LAGVS'''
								self.assertListEqual(tokenizer.encode(lowerCamelCase	),    [0, 4, 5, 6, 7, 8, 2]	)



					def 						snake_case			(			self				:    Union[str, Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						Optional[Any]						      =self.tokenizer_class(self.vocab_file	)

								lowerCamelCase__						:						str						      =['''LAGVS''', '''WCB''']
								lowerCamelCase__						:						List[Any]						      =tokenizer(lowerCamelCase,    padding=lowerCamelCase	)['''input_ids''']

								self.assertListEqual(lowerCamelCase,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]	)



					def 						snake_case			(			self				:    Optional[Any]	)->   int:
								lowerCamelCase__						:						Tuple						      =self.tokenizer_class(self.vocab_file	)

								lowerCamelCase__						:						int						      =['''LAGVS''', '''WCB''']
								lowerCamelCase__						:						Dict						      =tokenizer(lowerCamelCase,    padding=lowerCamelCase	)['''input_ids''']

								self.assertListEqual(lowerCamelCase,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]	)




					def 						snake_case			(			self				:    List[Any]	)->   List[str]:
								lowerCamelCase__						:						Tuple						      =self.get_tokenizers(fast=lowerCamelCase	)
								for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''	):
														lowerCamelCase__						:						Optional[int]						      ='''<unk>'''
														lowerCamelCase__						:						Any						      ='''<mask>'''

														lowerCamelCase__						:						List[Any]						      =tokenizer.tokenize(lowerCamelCase	)
														lowerCamelCase__						:						Optional[Any]						      =tokenizer.tokenize(lowerCamelCase	)

														self.assertEqual(len(lowerCamelCase	),    1	)
														self.assertEqual(len(lowerCamelCase	),    1	)
														self.assertEqual(token_a[0],    lowerCamelCase	)
														self.assertEqual(token_a[0],    lowerCamelCase	)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    List[str]			      = 'opt'
   UpperCamelCase_					:    List[str]			      = ['past_key_values']
   def __init__(     self				,  lowerCAmelCase_=5_02_72				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=12				,  lowerCAmelCase_=30_72				,  lowerCAmelCase_=20_48				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=12				,  lowerCAmelCase_="relu"				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  lowerCAmelCase_=1				,  lowerCAmelCase_=2				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Any:
        super().__init__(
            pad_token_id=lowerCAmelCase_				,  bos_token_id=lowerCAmelCase_				,  eos_token_id=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     Dict						      =    vocab_size
        a	:     int						      =    max_position_embeddings
        a	:     List[Any]						      =    num_attention_heads
        a	:     Union[str, Any]						      =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        a	:     Any						      =    ffn_dim
        a	:     Optional[int]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Optional[int]						      =    dropout
        a	:     Optional[Any]						      =    attention_dropout
        a	:     Union[str, Any]						      =    activation_function
        a	:     Any						      =    init_std
        a	:     List[Any]						      =    layerdrop
        a	:     str						      =    use_cache
        a	:     Any						      =    do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        a	:     List[Any]						      =    enable_bias
        a	:     List[str]						      =    layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        a	:     Dict						      =    _remove_final_layer_norm