





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase__						:      List[Any]	=    logging.get_logger(__name__)

lowercase__						:      Optional[Any]	=    {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Tuple						    =   'gpt_neo'
       _snake_case     :					Optional[int]						    =   ['past_key_values']
       _snake_case     :					List[str]						    =   {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





       def __init__(							self       :		Optional[int]	,			lowerCAmelCase__       :		str=50257	,			lowerCAmelCase__       :		Any=2048	,			lowerCAmelCase__       :		List[Any]=2048	,			lowerCAmelCase__       :		Optional[int]=24	,			lowerCAmelCase__       :		Any=[[["global", "local"], 12]]	,			lowerCAmelCase__       :		Optional[int]=16	,			lowerCAmelCase__       :		Any=None	,			lowerCAmelCase__       :		int=256	,			lowerCAmelCase__       :		Optional[int]="gelu_new"	,			lowerCAmelCase__       :		Optional[int]=0.0	,			lowerCAmelCase__       :		Optional[int]=0.0	,			lowerCAmelCase__       :		List[str]=0.0	,			lowerCAmelCase__       :		Dict=0.1	,			lowerCAmelCase__       :		int=1e-5	,			lowerCAmelCase__       :		Tuple=0.02	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Any=50256	,			lowerCAmelCase__       :		Optional[int]=50256	,			**lowerCAmelCase__       :		List[str]	,			)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_layers
            _UpperCamelCase       =	num_heads
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	window_size
            _UpperCamelCase       =	activation_function
            _UpperCamelCase       =	resid_dropout
            _UpperCamelCase       =	embed_dropout
            _UpperCamelCase       =	attention_dropout
            _UpperCamelCase       =	classifier_dropout
            _UpperCamelCase       =	layer_norm_epsilon
            _UpperCamelCase       =	initializer_range
            _UpperCamelCase       =	use_cache

            _UpperCamelCase       =	bos_token_id
            _UpperCamelCase       =	eos_token_id

            _UpperCamelCase       =	attention_types
            _UpperCamelCase       =	self.expand_attention_types_params(lowerCAmelCase__					)

            if len(self.attention_layers					) != self.num_layers:
                 raise ValueError(
                     '''Configuration for convolutional module is incorrect. '''
                     '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
                     f"""but is `len(config.attention_layers) = {len(self.attention_layers					)}`, """
                     f"""`config.num_layers = {self.num_layers}`. """
                     '''`config.attention_layers` is prepared using `config.attention_types`. '''
                     '''Please verify the value of `config.attention_types` argument.'''					)

            super().__init__(bos_token_id=lowerCAmelCase__	,			eos_token_id=lowerCAmelCase__	,			**lowerCAmelCase__					)







       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		str					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[]
            for item in attention_types:
                 for _ in range(item[1]					):
                      attentions.extend(item[0]					)
            return attentions







def  a__    (    lowercase :					int, lowercase :					str, lowercase :					Any, lowercase :					Union[str, Any]				)		->		Union[str, Any]:





     """simple docstring"""

     import torch

     _UpperCamelCase       =	input.size()
     _UpperCamelCase       =	len(lowercase				)
     _UpperCamelCase       =	shape[dimension]

     _UpperCamelCase       =	torch.arange(0, lowercase, lowercase				)
     _UpperCamelCase       =	torch.div(sizedim - size, lowercase, rounding_mode='''floor'''				) + 1
     _UpperCamelCase       =	torch.arange(lowercase				) + low_indices[:min_length][:, None]

     _UpperCamelCase       =	[slice(lowercase				)] * rank
     _UpperCamelCase       =	indices
     _UpperCamelCase       =	input[s]

     _UpperCamelCase       =	list(range(0, rank + 1				)				)
     perm.append(perm.pop(dimension + 1				)				)

     return sliced.permute(lowercase				)







def  a__    (    lowercase :					int, lowercase :					List[str]				)		->		str:





     """simple docstring"""

     import torch

     _UpperCamelCase       =	torch.arange(1, lowercase				)
     _UpperCamelCase       =	torch.remainder(lowercase, lowercase				)
     _UpperCamelCase       =	remainders == 0
     _UpperCamelCase       =	candidates[divisor_indices]
     _UpperCamelCase       =	torch.max(lowercase				)
     return largest_divisor, torch.div(lowercase, lowercase, rounding_mode='''floor'''				)




class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       @property
       def snake_case__						(							self       :		Union[str, Any]					)   ->					Mapping[str, Mapping[int, str]]:

            '''simple docstring'''




            _UpperCamelCase       =	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}					)
            if self.use_past:
                 self.fill_with_past_key_values_(lowerCAmelCase__	,			direction='''inputs'''					)
                 _UpperCamelCase       =	{0: '''batch''', 1: '''past_sequence + sequence'''}
            else:
                 _UpperCamelCase       =	{0: '''batch''', 1: '''sequence'''}

            return common_inputs





       @property
       def snake_case__						(							self       :		List[str]					)   ->					int:

            '''simple docstring'''




            return self._config.num_heads





       def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		PreTrainedTokenizer	,			lowerCAmelCase__       :		int = -1	,			lowerCAmelCase__       :		int = -1	,			lowerCAmelCase__       :		bool = False	,			lowerCAmelCase__       :		Optional[TensorType] = None	,			)   ->					Mapping[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	super(lowerCAmelCase__	,			self					).generate_dummy_inputs(
                lowerCAmelCase__	,			batch_size=lowerCAmelCase__	,			seq_length=lowerCAmelCase__	,			is_pair=lowerCAmelCase__	,			framework=lowerCAmelCase__					)

            # We need to order the input in the way they appears in the forward()
            _UpperCamelCase       =	OrderedDict({'''input_ids''': common_inputs['''input_ids''']}					)

            # Need to add the past_keys
            if self.use_past:
                 if not is_torch_available():
                      raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
                 else:
                      import torch

                      _UpperCamelCase   ,    _UpperCamelCase       =	common_inputs['''input_ids'''].shape
                      # Not using the same length for past_key_values
                      _UpperCamelCase       =	seqlen + 2
                      _UpperCamelCase       =	(
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      _UpperCamelCase       =	[
                          (torch.zeros(lowerCAmelCase__					), torch.zeros(lowerCAmelCase__					)) for _ in range(self.num_layers					)
                      ]

            _UpperCamelCase       =	common_inputs['''attention_mask''']
            if self.use_past:
                 _UpperCamelCase       =	ordered_inputs['''attention_mask'''].dtype
                 _UpperCamelCase       =	torch.cat(
                     [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase__	,			lowerCAmelCase__	,			dtype=lowerCAmelCase__					)]	,			dim=1					)

            return ordered_inputs







       @property
       def snake_case__						(							self       :		Optional[Any]					)   ->					int:

            '''simple docstring'''




            return 13










'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__					)	,			lowerCAmelCase__					)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__					)	,			lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,			'''<s>NOTUSED'''					)
            self.assertEqual(vocab_keys[1]	,			'''<pad>'''					)
            self.assertEqual(vocab_keys[-1]	,			'''<mask>'''					)
            self.assertEqual(len(lowerCAmelCase__					)	,			1004					)





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

            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size	,			1005					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            _UpperCamelCase       =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowerCAmelCase__	,			model_name='''camembert-base'''	,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''	,			sequences=lowerCAmelCase__	,			)



