
import os

import numpy
import onnx







def   UpperCamelCase_(					__magic_name__      :	List[Any]	,    __magic_name__      :	Tuple      ):


							"""simple docstring"""





							_lowerCAmelCase :Any      	=						a.name
							_lowerCAmelCase :int      	=						b.name

							_lowerCAmelCase :List[str]      	=						""
							_lowerCAmelCase :str      	=						""

							_lowerCAmelCase :Tuple      	=						a == b

							_lowerCAmelCase :Any      	=						name_a
							_lowerCAmelCase :Optional[Any]      	=						name_b

							return res







def   UpperCamelCase_(					__magic_name__      :	Tuple	,    __magic_name__      :	Optional[Any]	,    __magic_name__      :	Union[str, Any]      ):


							"""simple docstring"""





							for i, input_name in enumerate(node_proto.input      ):
														if input_name == name:
																					node_proto.input.insert(__snake_case	,    __snake_case      )
																					node_proto.input.pop(i + 1      )

							if node_proto.op_type == "If":
														_graph_replace_input_with(node_proto.attribute[0].g	,    __snake_case	,    __snake_case      )
														_graph_replace_input_with(node_proto.attribute[1].g	,    __snake_case	,    __snake_case      )
							if node_proto.op_type == "Loop":
														_graph_replace_input_with(node_proto.attribute[0].g	,    __snake_case	,    __snake_case      )







def   UpperCamelCase_(					__magic_name__      :	List[Any]	,    __magic_name__      :	str	,    __magic_name__      :	List[Any]      ):


							"""simple docstring"""





							for n in graph_proto.node:
														_node_replace_input_with(__snake_case	,    __snake_case	,    __snake_case      )







def   UpperCamelCase_(					__magic_name__      :	Optional[Any]	,    __magic_name__      :	Optional[int]	,    __magic_name__      :	Optional[Any]      ):


							"""simple docstring"""





							_lowerCAmelCase :Optional[int]      	=						list(model.graph.initializer      )
							_lowerCAmelCase :List[Any]      	=						list(model_without_ext.graph.initializer      )
							for i, ref_i in ind_to_replace:
														assert inits_with_data[i].name == inits[i].name
														assert inits_with_data[ref_i].name == inits[ref_i].name
														assert i > ref_i

														_lowerCAmelCase :Any      	=						inits[i].name
														_lowerCAmelCase :str      	=						inits[ref_i].name

														model_without_ext.graph.initializer.remove(inits[i]      )

														# for n in model.graph.node:
														_graph_replace_input_with(model_without_ext.graph	,    __snake_case	,    __snake_case      )







def   UpperCamelCase_(					__magic_name__      :	Any      ):


							"""simple docstring"""





							_lowerCAmelCase :Dict      	=						os.path.dirname(__snake_case      )
							_lowerCAmelCase :int      	=						os.path.basename(__snake_case      )

							_lowerCAmelCase :int      	=						onnx.load(os.path.join(__snake_case	,    __snake_case      )      )

							_lowerCAmelCase :Union[str, Any]      	=						list(model.graph.initializer      )

							_lowerCAmelCase :str      	=						set()
							_lowerCAmelCase :Any      	=						{}
							_lowerCAmelCase :Union[str, Any]      	=						[]

							_lowerCAmelCase :int      	=						0

							for i in range(len(__snake_case      )      ):
														if i in dup_set:
																					continue

														for j in range(i + 1	,    len(__snake_case      )      ):
																					if j in dup_set:
																												continue
																					if _is_equal_tensor_proto(inits[i]	,    inits[j]      ):
																												dup_set.add(__snake_case      )
																												dup_set.add(__snake_case      )

																												_lowerCAmelCase :List[str]      	=						inits[j].data_type
																												_lowerCAmelCase :str      	=						numpy.prod(inits[j].dims      )
																												if dtype == 1:
																																			mem_size *= 4
																												elif dtype == 6:
																																			mem_size *= 4
																												elif dtype == 7 or dtype == 11:
																																			mem_size *= 8
																												else:
																																			print('unexpected data type: '	,    __snake_case      )
																												total_reduced_size += mem_size

																												_lowerCAmelCase :int      	=						inits[i].name
																												_lowerCAmelCase :Optional[Any]      	=						inits[j].name

																												if name_i in dup_map:
																																			dup_map[name_i].append(__snake_case      )
																												else:
																																			_lowerCAmelCase :Any      	=						[name_j]
																												ind_to_replace.append((j, i)      )

							print('total reduced size: '	,    total_reduced_size / 1024 / 1024 / 1024	,    'GB'      )

							_lowerCAmelCase :Optional[int]      	=						sorted(__snake_case      )
							_remove_dup_initializers_from_model(__snake_case	,    __snake_case	,    __snake_case      )

							_lowerCAmelCase :List[str]      	=						"optimized_" + model_file_name
							_lowerCAmelCase :str      	=						os.path.join(__snake_case	,    __snake_case      )
							onnx.save(__snake_case	,    __snake_case      )

							return new_model




"""simple docstring"""






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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

	SCREAMING_SNAKE_CASE        =      {
	    'num_attention_heads': 'attention_heads',
	    'hidden_size': 'd_model',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     decoder_start_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )