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

__UpperCamelCase	: 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       lowercase__		(     UpperCamelCase_):
	UpperCamelCase_  =	"""gpt_neo"""
	UpperCamelCase_  =	["""past_key_values"""]
	UpperCamelCase_  =	{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


	def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]=5_0257					,	UpperCamelCase__			:       int=2048					,	UpperCamelCase__			:       int=2048					,	UpperCamelCase__			:       List[Any]=24					,	UpperCamelCase__			:       int=[[["global", "local"], 12]]					,	UpperCamelCase__			:       int=16					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Union[str, Any]=256					,	UpperCamelCase__			:       Optional[int]="gelu_new"					,	UpperCamelCase__			:       List[str]=0.0					,	UpperCamelCase__			:       Union[str, Any]=0.0					,	UpperCamelCase__			:       str=0.0					,	UpperCamelCase__			:       int=0.1					,	UpperCamelCase__			:       Tuple=1E-5					,	UpperCamelCase__			:       Optional[int]=0.02					,	UpperCamelCase__			:       Optional[Any]=True					,	UpperCamelCase__			:       Any=5_0256					,	UpperCamelCase__			:       List[Any]=5_0256					,	**UpperCamelCase__			:       List[Any]					,	):





							'''simple docstring'''





							SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							vocab_size
							SCREAMING_SNAKE_CASE							:					List[Any]           =							max_position_embeddings
							SCREAMING_SNAKE_CASE							:					Optional[int]           =							hidden_size
							SCREAMING_SNAKE_CASE							:					List[Any]           =							num_layers
							SCREAMING_SNAKE_CASE							:					str           =							num_heads
							SCREAMING_SNAKE_CASE							:					Any           =							intermediate_size
							SCREAMING_SNAKE_CASE							:					Optional[int]           =							window_size
							SCREAMING_SNAKE_CASE							:					int           =							activation_function
							SCREAMING_SNAKE_CASE							:					Any           =							resid_dropout
							SCREAMING_SNAKE_CASE							:					List[str]           =							embed_dropout
							SCREAMING_SNAKE_CASE							:					Optional[int]           =							attention_dropout
							SCREAMING_SNAKE_CASE							:					Any           =							classifier_dropout
							SCREAMING_SNAKE_CASE							:					str           =							layer_norm_epsilon
							SCREAMING_SNAKE_CASE							:					Optional[Any]           =							initializer_range
							SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							use_cache

							SCREAMING_SNAKE_CASE							:					Any           =							bos_token_id
							SCREAMING_SNAKE_CASE							:					Tuple           =							eos_token_id

							SCREAMING_SNAKE_CASE							:					List[str]           =							attention_types
							SCREAMING_SNAKE_CASE							:					Tuple           =							self.expand_attention_types_params(UpperCamelCase__		)

							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=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__		)



	@staticmethod
	def  __A ( UpperCamelCase__			:       int		):





							'''simple docstring'''





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






def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						import torch

						SCREAMING_SNAKE_CASE							:					int           =							input.size()
						SCREAMING_SNAKE_CASE							:					Dict           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Dict           =							shape[dimension]

						SCREAMING_SNAKE_CASE							:					Any           =							torch.arange(0	,						_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							torch.div(sizedim - size	,						_lowercase	,						rounding_mode='''floor'''		) + 1
						SCREAMING_SNAKE_CASE							:					List[str]           =							torch.arange(_lowercase		) + low_indices[:min_length][:, None]

						SCREAMING_SNAKE_CASE							:					Dict           =							[slice(_lowercase		)] * rank
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							indices
						SCREAMING_SNAKE_CASE							:					int           =							input[s]

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							list(range(0	,						rank + 1		)		)
						perm.append(perm.pop(dimension + 1		)		)

						return sliced.permute(_lowercase		)
def    A			(					_lowercase	,						_lowercase		):
						import torch

						SCREAMING_SNAKE_CASE							:					List[str]           =							torch.arange(1	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							torch.remainder(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							remainders == 0
						SCREAMING_SNAKE_CASE							:					Dict           =							candidates[divisor_indices]
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.max(_lowercase		)
						return largest_divisor, torch.div(_lowercase	,						_lowercase	,						rounding_mode='''floor'''		)





class       lowercase__		(     UpperCamelCase_):


	@property
	def  __A ( self			:       Dict		):





							'''simple docstring'''





							SCREAMING_SNAKE_CASE							:					Tuple           =							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
							if self.use_past:
													self.fill_with_past_key_values_(UpperCamelCase__					,	direction='''inputs'''		)
													SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
													SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''sequence'''}

							return common_inputs


	@property
	def  __A ( self			:       List[str]		):





							'''simple docstring'''





							return self._config.num_heads


	def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       str = -1					,	UpperCamelCase__			:       Any = -1					,	UpperCamelCase__			:       Dict = False					,	UpperCamelCase__			:       Any = None					,	):





							'''simple docstring'''





							SCREAMING_SNAKE_CASE							:					Dict           =							super(UpperCamelCase__					,	self		).generate_dummy_inputs(
							    UpperCamelCase__					,	batch_size=UpperCamelCase__					,	seq_length=UpperCamelCase__					,	is_pair=UpperCamelCase__					,	framework=UpperCamelCase__		)

							# We need to order the input in the way they appears in the forward()
							SCREAMING_SNAKE_CASE							:					Dict           =							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

																			SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							common_inputs['''input_ids'''].shape
																			# Not using the same length for past_key_values
																			SCREAMING_SNAKE_CASE							:					str           =							seqlen + 2
																			SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			SCREAMING_SNAKE_CASE							:					Dict           =							[
																			    (torch.zeros(UpperCamelCase__		), torch.zeros(UpperCamelCase__		)) for _ in range(self.num_layers		)
																			]

							SCREAMING_SNAKE_CASE							:					Tuple           =							common_inputs['''attention_mask''']
							if self.use_past:
													SCREAMING_SNAKE_CASE							:					Optional[int]           =							ordered_inputs['''attention_mask'''].dtype
													SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.cat(
													    [ordered_inputs['''attention_mask'''], torch.ones(UpperCamelCase__					,	UpperCamelCase__					,	dtype=UpperCamelCase__		)]					,	dim=1		)

							return ordered_inputs



	@property
	def  __A ( self			:       Optional[Any]		):





							'''simple docstring'''





							return 13






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	FunnelTokenizer
						UpperCamelCase_  =	FunnelTokenizerFast
						UpperCamelCase_  =	True
						UpperCamelCase_  =	True


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    '''<unk>''',
												    '''<cls>''',
												    '''<sep>''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												SCREAMING_SNAKE_CASE							:					List[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  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizer.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizerFast.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''UNwant\u00E9d,running'''
												SCREAMING_SNAKE_CASE							:					int           =							'''unwanted, running'''
												return input_text, output_text


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer_class(self.vocab_file		)

												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.tokenize('''UNwant\u00E9d,running'''		)
												self.assertListEqual(UpperCamelCase__					,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']		)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)					,	[7, 4, 5, 10, 8, 9]		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer('''UNwant\u00E9d,running'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(inputs['''input_ids''']		) - 1
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len		)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer('''UNwant\u00E9d,running'''					,	'''UNwant\u00E9d,running'''		)
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len + [1] * sentence_len		)





