


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


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						{
    """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      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		Any        =	'''gpt_neo'''
	_lowerCAmelCase    :		Tuple        =	['''past_key_values''']
	_lowerCAmelCase    :		str        =	{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


	def __init__(						self							,    lowercase__=5_0_2_5_7							,    lowercase__=2_0_4_8							,    lowercase__=2_0_4_8							,    lowercase__=2_4							,    lowercase__=[[["global", "local"], 1_2]]							,    lowercase__=1_6							,    lowercase__=None							,    lowercase__=2_5_6							,    lowercase__="gelu_new"							,    lowercase__=0.0							,    lowercase__=0.0							,    lowercase__=0.0							,    lowercase__=0.1							,    lowercase__=1e-5							,    lowercase__=0.0_2							,    lowercase__=True							,    lowercase__=5_0_2_5_6							,    lowercase__=5_0_2_5_6							,    **lowercase__							,    ):
							__UpperCAmelCase :       Dict  						=  vocab_size
							__UpperCAmelCase :       Dict  						=  max_position_embeddings
							__UpperCAmelCase :       List[Any]  						=  hidden_size
							__UpperCAmelCase :       Dict  						=  num_layers
							__UpperCAmelCase :       Union[str, Any]  						=  num_heads
							__UpperCAmelCase :       List[Any]  						=  intermediate_size
							__UpperCAmelCase :       Optional[Any]  						=  window_size
							__UpperCAmelCase :       List[str]  						=  activation_function
							__UpperCAmelCase :       Union[str, Any]  						=  resid_dropout
							__UpperCAmelCase :       Any  						=  embed_dropout
							__UpperCAmelCase :       Optional[int]  						=  attention_dropout
							__UpperCAmelCase :       List[str]  						=  classifier_dropout
							__UpperCAmelCase :       Optional[int]  						=  layer_norm_epsilon
							__UpperCAmelCase :       Tuple  						=  initializer_range
							__UpperCAmelCase :       Optional[int]  						=  use_cache

							__UpperCAmelCase :       Tuple  						=  bos_token_id
							__UpperCAmelCase :       Optional[Any]  						=  eos_token_id

							__UpperCAmelCase :       List[Any]  						=  attention_types
							__UpperCAmelCase :       str  						=  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    A(						lowercase__):
							__UpperCAmelCase :       int  						=  []
							for item in attention_types:
													for _ in range(item[1]):
																			attentions.extend(item[0])
							return attentions






def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)		->    Union[str, Any]:




						'''simple docstring'''







						import torch

						__UpperCAmelCase :       Tuple  						=  input.size()
						__UpperCAmelCase :       Any  						=  len(lowercase_			)
						__UpperCAmelCase :       str  						=  shape[dimension]

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

						__UpperCAmelCase :       Tuple  						=  [slice(lowercase_			)] * rank
						__UpperCAmelCase :       str  						=  indices
						__UpperCAmelCase :       Any  						=  input[s]

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

						return sliced.permute(lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						import torch

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





class        lowerCamelCase      (     _UpperCamelCase   ):


	@property
	def    A(						self):
							__UpperCAmelCase :       Dict  						=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
							if self.use_past:
													self.fill_with_past_key_values_(lowercase__							,    direction='''inputs''')
													__UpperCAmelCase :       Dict  						=  {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
													__UpperCAmelCase :       List[Any]  						=  {0: '''batch''', 1: '''sequence'''}

							return common_inputs


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


	def    A(						self							,    lowercase__							,    lowercase__ = -1							,    lowercase__ = -1							,    lowercase__ = False							,    lowercase__ = None							,    ):
							__UpperCAmelCase :       Dict  						=  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()
							__UpperCAmelCase :       List[Any]  						=  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

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

							__UpperCAmelCase :       Union[str, Any]  						=  common_inputs['''attention_mask''']
							if self.use_past:
													__UpperCAmelCase :       Dict  						=  ordered_inputs['''attention_mask'''].dtype
													__UpperCAmelCase :       int  						=  torch.cat(
													    [ordered_inputs['''attention_mask'''], torch.ones(lowercase__							,    lowercase__							,    dtype=lowercase__)]							,    dim=1)

							return ordered_inputs


	@property
	def    A(						self):
							return 1_3





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		List[str]        =	'''sew-d'''


	def __init__(						self							,    lowercase__=3_2							,    lowercase__=7_6_8							,    lowercase__=1_2							,    lowercase__=1_2							,    lowercase__=3_0_7_2							,    lowercase__=2							,    lowercase__=5_1_2							,    lowercase__=2_5_6							,    lowercase__=True							,    lowercase__=True							,    lowercase__=("p2c", "c2p")							,    lowercase__="layer_norm"							,    lowercase__="gelu_python"							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=0.0							,    lowercase__=0.1							,    lowercase__=0.0_2							,    lowercase__=1e-7							,    lowercase__=1e-5							,    lowercase__="group"							,    lowercase__="gelu"							,    lowercase__=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)							,    lowercase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,    lowercase__=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,    lowercase__=False							,    lowercase__=1_2_8							,    lowercase__=1_6							,    lowercase__=True							,    lowercase__=0.0_5							,    lowercase__=1_0							,    lowercase__=2							,    lowercase__=0.0							,    lowercase__=1_0							,    lowercase__=0							,    lowercase__="mean"							,    lowercase__=False							,    lowercase__=False							,    lowercase__=2_5_6							,    lowercase__=0							,    lowercase__=1							,    lowercase__=2							,    **lowercase__							,    ):
							super().__init__(**lowercase__							,    pad_token_id=lowercase__							,    bos_token_id=lowercase__							,    eos_token_id=lowercase__)
							__UpperCAmelCase :       Dict  						=  hidden_size
							__UpperCAmelCase :       int  						=  feat_extract_norm
							__UpperCAmelCase :       List[str]  						=  feat_extract_activation
							__UpperCAmelCase :       str  						=  list(lowercase__)
							__UpperCAmelCase :       Optional[int]  						=  list(lowercase__)
							__UpperCAmelCase :       Tuple  						=  list(lowercase__)
							__UpperCAmelCase :       Tuple  						=  conv_bias
							__UpperCAmelCase :       int  						=  num_conv_pos_embeddings
							__UpperCAmelCase :       int  						=  num_conv_pos_embedding_groups
							__UpperCAmelCase :       Any  						=  len(self.conv_dim)
							__UpperCAmelCase :       str  						=  num_hidden_layers
							__UpperCAmelCase :       Optional[Any]  						=  intermediate_size
							__UpperCAmelCase :       Union[str, Any]  						=  squeeze_factor
							__UpperCAmelCase :       Union[str, Any]  						=  max_position_embeddings
							__UpperCAmelCase :       List[str]  						=  position_buckets
							__UpperCAmelCase :       Tuple  						=  share_att_key
							__UpperCAmelCase :       int  						=  relative_attention
							__UpperCAmelCase :       str  						=  norm_rel_ebd
							__UpperCAmelCase :       Dict  						=  list(lowercase__)
							__UpperCAmelCase :       int  						=  hidden_act
							__UpperCAmelCase :       int  						=  num_attention_heads
							__UpperCAmelCase :       Optional[int]  						=  hidden_dropout
							__UpperCAmelCase :       int  						=  attention_dropout
							__UpperCAmelCase :       Optional[int]  						=  activation_dropout
							__UpperCAmelCase :       Optional[Any]  						=  feat_proj_dropout
							__UpperCAmelCase :       Optional[Any]  						=  final_dropout
							__UpperCAmelCase :       Optional[int]  						=  layer_norm_eps
							__UpperCAmelCase :       str  						=  feature_layer_norm_eps
							__UpperCAmelCase :       Optional[int]  						=  initializer_range
							__UpperCAmelCase :       Tuple  						=  vocab_size

							if (
							    (len(self.conv_stride) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel) != self.num_feat_extract_layers)
							    or (len(self.conv_dim) != self.num_feat_extract_layers)
							):
													raise ValueError(
													    '''Configuration for convolutional layers is incorrect.'''
													    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
													    F"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"
													    F"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.")

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							__UpperCAmelCase :       Optional[int]  						=  apply_spec_augment
							__UpperCAmelCase :       List[str]  						=  mask_time_prob
							__UpperCAmelCase :       Union[str, Any]  						=  mask_time_length
							__UpperCAmelCase :       Optional[int]  						=  mask_time_min_masks
							__UpperCAmelCase :       Optional[int]  						=  mask_feature_prob
							__UpperCAmelCase :       List[str]  						=  mask_feature_length
							__UpperCAmelCase :       List[Any]  						=  mask_feature_min_masks

							# ctc loss
							__UpperCAmelCase :       int  						=  ctc_loss_reduction
							__UpperCAmelCase :       Union[str, Any]  						=  ctc_zero_infinity

							# sequence classification
							__UpperCAmelCase :       List[str]  						=  use_weighted_layer_sum
							__UpperCAmelCase :       Tuple  						=  classifier_proj_size


	@property
	def    A(						self):
							return functools.reduce(operator.mul							,    self.conv_stride							,    1)

