


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


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

a			:			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_							(					SCREAMING_SNAKE_CASE_					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"gpt_neo"
					__UpperCAmelCase							  =					["past_key_values"]
					__UpperCAmelCase							  =					{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






					def __init__(					self			,							snake_case_=5_0_2_5_7			,							snake_case_=2_0_4_8			,							snake_case_=2_0_4_8			,							snake_case_=2_4			,							snake_case_=[[["global", "local"], 1_2]]			,							snake_case_=1_6			,							snake_case_=None			,							snake_case_=2_5_6			,							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_=0.0_2			,							snake_case_=True			,							snake_case_=5_0_2_5_6			,							snake_case_=5_0_2_5_6			,							**snake_case_			,							) ->      Union[str, Any]:







								'''simple docstring'''

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

								__lowercase			       = bos_token_id
								__lowercase			       = eos_token_id

								__lowercase			       = attention_types
								__lowercase			       = 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_    ) ->      Any:







								'''simple docstring'''

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




def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			import torch

			__lowercase			       = input.size()
			__lowercase			       = len(__UpperCAmelCase	)
			__lowercase			       = shape[dimension]

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

			__lowercase			       = [slice(__UpperCAmelCase	)] * rank
			__lowercase			       = indices
			__lowercase			       = input[s]

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

			return sliced.permute(__UpperCAmelCase	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			import torch

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





class 						lowerCamelCase_							(					SCREAMING_SNAKE_CASE_					):



					'''simple docstring'''







					@property
					def A   (					self    ) ->      Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

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

								return common_inputs






					@property
					def A   (					self    ) ->      int:







								'''simple docstring'''

								return self._config.num_heads






					def A   (					self			,							snake_case_			,							snake_case_ = -1			,							snake_case_ = -1			,							snake_case_ = False			,							snake_case_ = None			,							) ->      Mapping[str, Any]:







								'''simple docstring'''

								__lowercase			       = 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()
								__lowercase			       = 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

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

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

								return ordered_inputs






					@property
					def A   (					self    ) ->      int:







								'''simple docstring'''

								return 1_3










"""simple docstring"""





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				OpenAIGPTTokenizer
					UpperCAmelCase__								=				OpenAIGPTTokenizerFast
					UpperCAmelCase__								=				True
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[Any]:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											__magic_name__:	str							=       [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """w</w>""",
											    """r</w>""",
											    """t</w>""",
											    """lo""",
											    """low""",
											    """er</w>""",
											    """low</w>""",
											    """lowest</w>""",
											    """newer</w>""",
											    """wider</w>""",
											    """<unk>""",
											]
											__magic_name__:	Any							=       dict(zip(__snake_case     ,		range(len(__snake_case					)					)					)					)
											__magic_name__:	List[str]							=       ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

											__magic_name__:	Optional[Any]							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
											__magic_name__:	Any							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""merges_file"""]					)
											with open(self.vocab_file     ,		"""w"""					) as fp:
																	fp.write(json.dumps(__snake_case					)					)
											with open(self.merges_file     ,		"""w"""					) as fp:
																	fp.write("""\n""".join(__snake_case					)					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[Any]					)			->							Optional[int]:
											return "lower newer", "lower newer"

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Union[str, Any]:
											__magic_name__:	Optional[Any]							=       OpenAIGPTTokenizer(self.vocab_file     ,		self.merges_file					)

											__magic_name__:	Union[str, Any]							=       """lower"""
											__magic_name__:	Optional[int]							=       ["""low""", """er</w>"""]
											__magic_name__:	List[Any]							=       tokenizer.tokenize(__snake_case					)
											self.assertListEqual(__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       tokens + ["""<unk>"""]
											__magic_name__:	List[str]							=       [1_4, 1_5, 2_0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Any=1_5					)			->							Tuple:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																							__magic_name__:	str							=       self.rust_tokenizer_class.from_pretrained(__snake_case     ,		**__snake_case					)

																							# Simple input
																							__magic_name__:	Dict							=       """This is a simple input"""
																							__magic_name__:	Any							=       ["""This is a simple input 1""", """This is a simple input 2"""]
																							__magic_name__:	int							=       ("""This is a simple input""", """This is a pair""")
																							__magic_name__:	int							=       [
																							    ("""This is a simple input 1""", """This is a simple input 2"""),
																							    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																							]

																							# Simple input tests
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)






					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											pass



@require_ftfy
@require_spacy
@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_			):
					pass



