






'''simple docstring'''






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    	=   logging.get_logger(__name__)

__A    	=   {
    '''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 a_    (    UpperCamelCase_							):
			_snake_case        =							"""gpt_neo"""
			_snake_case        =							["""past_key_values"""]
			_snake_case        =							{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







			def __init__(self					,   __a=5_0_2_5_7					,   __a=2_0_4_8					,   __a=2_0_4_8					,   __a=2_4					,   __a=[[["global", "local"], 1_2]]					,   __a=1_6					,   __a=None					,   __a=2_5_6					,   __a="gelu_new"					,   __a=0.0					,   __a=0.0					,   __a=0.0					,   __a=0.1					,   __a=1E-5					,   __a=0.02					,   __a=True					,   __a=5_0_2_5_6					,   __a=5_0_2_5_6					,   **__a					,   )			->      Dict:
						"""simple docstring"""






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

						__snake_case				:      List[str]     							= bos_token_id
						__snake_case				:      int     							= eos_token_id

						__snake_case				:      Any     							= attention_types
						__snake_case				:      Tuple     							= self.expand_attention_types_params(__a)

						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=__a					,   eos_token_id=__a					,   **__a)







			@staticmethod
			def     SCREAMING_SNAKE_CASE__					(__a)			->      Tuple:
						"""simple docstring"""






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




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       Tuple  ,		A  :       Dict  ,		A  :       List[str]  )    -> Optional[int]:







			"""simple docstring"""
			import torch

			__snake_case				:      str     							= input.size()
			__snake_case				:      str     							= len(A  )
			__snake_case				:      List[Any]     							= shape[dimension]

			__snake_case				:      int     							= torch.arange(0  ,		A  ,		A  )
			__snake_case				:      List[Any]     							= torch.div(sizedim - size  ,		A  ,		rounding_mode='floor'  ) + 1
			__snake_case				:      Dict     							= torch.arange(A  ) + low_indices[:min_length][:, None]

			__snake_case				:      int     							= [slice(A  )] * rank
			__snake_case				:      Dict     							= indices
			__snake_case				:      List[str]     							= input[s]

			__snake_case				:      Tuple     							= list(range(0  ,		rank + 1  )  )
			perm.append(perm.pop(dimension + 1  )  )

			return sliced.permute(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       List[Any]  )    -> Union[str, Any]:







			"""simple docstring"""
			import torch

			__snake_case				:      Tuple     							= torch.arange(1  ,		A  )
			__snake_case				:      Tuple     							= torch.remainder(A  ,		A  )
			__snake_case				:      Optional[Any]     							= remainders == 0
			__snake_case				:      Optional[int]     							= candidates[divisor_indices]
			__snake_case				:      Tuple     							= torch.max(A  )
			return largest_divisor, torch.div(A  ,		A  ,		rounding_mode='floor'  )





class a_    (    UpperCamelCase_							):







			@property
			def     SCREAMING_SNAKE_CASE__					(self)			->      Mapping[str, Mapping[int, str]]:
						"""simple docstring"""






						__snake_case				:      str     							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
						if self.use_past:
									self.fill_with_past_key_values_(__a					,   direction='inputs')
									__snake_case				:      List[str]     							= {0: 'batch', 1: 'past_sequence + sequence'}
						else:
									__snake_case				:      Dict     							= {0: 'batch', 1: 'sequence'}

						return common_inputs







			@property
			def     SCREAMING_SNAKE_CASE__					(self)			->      int:
						"""simple docstring"""






						return self._config.num_heads







			def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = -1					,   __a = -1					,   __a = False					,   __a = None					,   )			->      Mapping[str, Any]:
						"""simple docstring"""






						__snake_case				:      int     							= super(__a					,   self).generate_dummy_inputs(
						    __a					,   batch_size=__a					,   seq_length=__a					,   is_pair=__a					,   framework=__a)

						# We need to order the input in the way they appears in the forward()
						__snake_case				:      str     							= 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				:      Optional[Any]     							= common_inputs['input_ids'].shape
												# Not using the same length for past_key_values
												__snake_case				:      Optional[Any]     							= seqlen + 2
												__snake_case				:      str     							= (
												    batch,
												    self.num_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // self.num_attention_heads,
												)
												__snake_case				:      List[Any]     							= [
												    (torch.zeros(__a), torch.zeros(__a)) for _ in range(self.num_layers)
												]

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

						return ordered_inputs







			@property
			def     SCREAMING_SNAKE_CASE__					(self)			->      int:
						"""simple docstring"""






						return 1_3







'''simple docstring'''






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A    	=   abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> str:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(A  )





def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Optional[int]:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   __snake_case				:      Any     							= terminalreporter.config.getoption('--make-reports'  )
   if make_reports:
      pytest_terminal_summary_main(A  ,		id=A  )