'''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


_lowercase						: Tuple						=						logging.get_logger(__name__)

_lowercase						: Optional[int]						=						{
    "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     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''gpt_neo'''
			UpperCamelCase__				=       ['''past_key_values''']
			UpperCamelCase__				=       {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







			def __init__(	self				:  List[str]				,			lowercase_				:  Optional[int]=50257				,			lowercase_				:  int=2048				,			lowercase_				:  Union[str, Any]=2048				,			lowercase_				:  List[Any]=24				,			lowercase_				:  Dict=[[["global", "local"], 12]]				,			lowercase_				:  Any=16				,			lowercase_				:  Optional[int]=None				,			lowercase_				:  List[Any]=256				,			lowercase_				:  Dict="gelu_new"				,			lowercase_				:  Any=0.0				,			lowercase_				:  Optional[Any]=0.0				,			lowercase_				:  List[Any]=0.0				,			lowercase_				:  Optional[Any]=0.1				,			lowercase_				:  int=1E-5				,			lowercase_				:  Optional[Any]=0.02				,			lowercase_				:  List[Any]=True				,			lowercase_				:  int=50256				,			lowercase_				:  List[Any]=50256				,			**lowercase_				:  Optional[Any]				,			):
										lowercase_					:			Union[str, Any]										=       vocab_size
										lowercase_					:			List[str]										=       max_position_embeddings
										lowercase_					:			Optional[int]										=       hidden_size
										lowercase_					:			Optional[int]										=       num_layers
										lowercase_					:			Optional[Any]										=       num_heads
										lowercase_					:			str										=       intermediate_size
										lowercase_					:			Tuple										=       window_size
										lowercase_					:			Any										=       activation_function
										lowercase_					:			Any										=       resid_dropout
										lowercase_					:			str										=       embed_dropout
										lowercase_					:			Optional[int]										=       attention_dropout
										lowercase_					:			Optional[int]										=       classifier_dropout
										lowercase_					:			Optional[int]										=       layer_norm_epsilon
										lowercase_					:			Any										=       initializer_range
										lowercase_					:			List[Any]										=       use_cache

										lowercase_					:			List[Any]										=       bos_token_id
										lowercase_					:			Optional[int]										=       eos_token_id

										lowercase_					:			Optional[int]										=       attention_types
										lowercase_					:			Dict										=       self.expand_attention_types_params(lowercase_    )

										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=lowercase_				,			eos_token_id=lowercase_				,			**lowercase_    )







			@staticmethod
			def 	SCREAMING_SNAKE_CASE_     (	lowercase_				:  Optional[int]    ):
										lowercase_					:			str										=       []
										for item in attention_types:
																	for _ in range(item[1]    ):
																								attentions.extend(item[0]    )
										return attentions




def 							lowerCamelCase	(      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					List[str]      ,      UpperCAmelCase__       :					List[str]      ,      UpperCAmelCase__       :					List[str] )			->      Tuple:
							import torch

							lowercase_					:			List[Any]										=       input.size()
							lowercase_					:			Any										=       len(UpperCAmelCase__ )
							lowercase_					:			Dict										=       shape[dimension]

							lowercase_					:			Optional[int]										=       torch.arange(0      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       torch.div(sizedim - size      ,      UpperCAmelCase__      ,      rounding_mode="""floor""" ) + 1
							lowercase_					:			Tuple										=       torch.arange(UpperCAmelCase__ ) + low_indices[:min_length][:, None]

							lowercase_					:			Dict										=       [slice(UpperCAmelCase__ )] * rank
							lowercase_					:			Union[str, Any]										=       indices
							lowercase_					:			str										=       input[s]

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

							return sliced.permute(UpperCAmelCase__ )




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					Dict )			->      List[Any]:
							import torch

							lowercase_					:			int										=       torch.arange(1      ,      UpperCAmelCase__ )
							lowercase_					:			Dict										=       torch.remainder(UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Any										=       remainders == 0
							lowercase_					:			Union[str, Any]										=       candidates[divisor_indices]
							lowercase_					:			Dict										=       torch.max(UpperCAmelCase__ )
							return largest_divisor, torch.div(UpperCAmelCase__      ,      UpperCAmelCase__      ,      rounding_mode="""floor""" )





class     __magic_name__						(    _UpperCAmelCase):







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_					:			Tuple										=       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
										if self.use_past:
																	self.fill_with_past_key_values_(lowercase_				,			direction="""inputs"""    )
																	lowercase_					:			Union[str, Any]										=       {0: """batch""", 1: """past_sequence + sequence"""}
										else:
																	lowercase_					:			List[Any]										=       {0: """batch""", 1: """sequence"""}

										return common_inputs







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										return self._config.num_heads







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  PreTrainedTokenizer				,			lowercase_				:  int = -1				,			lowercase_				:  int = -1				,			lowercase_				:  bool = False				,			lowercase_				:  Optional[TensorType] = None				,			):
										lowercase_					:			int										=       super(lowercase_				,			self    ).generate_dummy_inputs(
										    lowercase_				,			batch_size=lowercase_				,			seq_length=lowercase_				,			is_pair=lowercase_				,			framework=lowercase_    )

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

										lowercase_					:			Optional[Any]										=       common_inputs["""attention_mask"""]
										if self.use_past:
																	lowercase_					:			List[Any]										=       ordered_inputs["""attention_mask"""].dtype
																	lowercase_					:			Dict										=       torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(lowercase_				,			lowercase_				,			dtype=lowercase_    )]				,			dim=1    )

										return ordered_inputs







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										return 13






'''simple docstring'''




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
						import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class     __magic_name__						(    unittest.TestCase,     _UpperCAmelCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[str]										=       load_tool("""text-to-speech"""    )
										self.tool.setup()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			List[str]										=       self.tool("""hey"""    )
										lowercase_					:			Optional[int]										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			Union[str, Any]										=       self.tool("""hey"""    )
										lowercase_					:			int										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )





