

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


_UpperCAmelCase	:     Optional[Any]		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Dict		       = {
    """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 				lowercase       ( lowercase_    ):
	__SCREAMING_SNAKE_CASE      :				List[Any]     =    '''gpt_neo'''
	__SCREAMING_SNAKE_CASE      :				Optional[int]     =    ['''past_key_values''']
	__SCREAMING_SNAKE_CASE      :				Tuple     =    {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




	def __init__(      self   ,     snake_case=5_0257   ,     snake_case=2048   ,     snake_case=2048   ,     snake_case=24   ,     snake_case=[[["global", "local"], 12]]   ,     snake_case=16   ,     snake_case=None   ,     snake_case=256   ,     snake_case="gelu_new"   ,     snake_case=0.0   ,     snake_case=0.0   ,     snake_case=0.0   ,     snake_case=0.1   ,     snake_case=1e-5   ,     snake_case=0.02   ,     snake_case=True   ,     snake_case=5_0256   ,     snake_case=5_0256   ,     **snake_case   ,     ):
								snake_case_           =					vocab_size
								snake_case_           =					max_position_embeddings
								snake_case_           =					hidden_size
								snake_case_           =					num_layers
								snake_case_           =					num_heads
								snake_case_           =					intermediate_size
								snake_case_           =					window_size
								snake_case_           =					activation_function
								snake_case_           =					resid_dropout
								snake_case_           =					embed_dropout
								snake_case_           =					attention_dropout
								snake_case_           =					classifier_dropout
								snake_case_           =					layer_norm_epsilon
								snake_case_           =					initializer_range
								snake_case_           =					use_cache

								snake_case_           =					bos_token_id
								snake_case_           =					eos_token_id

								snake_case_           =					attention_types
								snake_case_           =					self.expand_attention_types_params(snake_case )

								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=snake_case   ,     eos_token_id=snake_case   ,     **snake_case )




	@staticmethod
	def 					a							(      snake_case ):
								snake_case_           =					[]
								for item in attention_types:
															for _ in range(item[1] ):
																						attentions.extend(item[0] )
								return attentions





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import torch

							snake_case_           =					input.size()
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					shape[dimension]

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

							snake_case_           =					[slice(UpperCamelCase__   )] * rank
							snake_case_           =					indices
							snake_case_           =					input[s]

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

							return sliced.permute(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import torch

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







class 				lowercase       ( lowercase_    ):




	@property
	def 					a							(      self ):
								snake_case_           =					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
								if self.use_past:
															self.fill_with_past_key_values_(snake_case   ,     direction='inputs' )
															snake_case_           =					{0: 'batch', 1: 'past_sequence + sequence'}
								else:
															snake_case_           =					{0: 'batch', 1: 'sequence'}

								return common_inputs




	@property
	def 					a							(      self ):
								return self._config.num_heads




	def 					a							(      self   ,     snake_case   ,     snake_case = -1   ,     snake_case = -1   ,     snake_case = False   ,     snake_case = None   ,     ):
								snake_case_           =					super(snake_case   ,     self ).generate_dummy_inputs(
								    snake_case   ,     batch_size=snake_case   ,     seq_length=snake_case   ,     is_pair=snake_case   ,     framework=snake_case )

								# We need to order the input in the way they appears in the forward()
								snake_case_           =					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

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

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

								return ordered_inputs




	@property
	def 					a							(      self ):
								return 13







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




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

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											snake_case_           =					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', 'l à</w>']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['merges_file'] )

											with open(self.vocab_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		for token in vocab_tokens:
																									fp.write(F'''{token} {vocab_tokens[token]}\n''' )
											with open(self.merges_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		fp.write('\n'.join(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case )   ,     snake_case )




