



"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case							:     Optional[int]   =					logging.get_logger(__name__)

__snake_case							:     str   =					{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class     A__     (						__SCREAMING_SNAKE_CASE							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			'mobilenet_v2'

 def __init__(  self:	Any ,    _SCREAMING_SNAKE_CASE:	int=3 ,    _SCREAMING_SNAKE_CASE:	Tuple=224 ,    _SCREAMING_SNAKE_CASE:	Dict=1.0 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=8 ,    _SCREAMING_SNAKE_CASE:	List[Any]=8 ,    _SCREAMING_SNAKE_CASE:	int=6 ,    _SCREAMING_SNAKE_CASE:	Dict=32 ,    _SCREAMING_SNAKE_CASE:	str=True ,    _SCREAMING_SNAKE_CASE:	List[str]=True ,    _SCREAMING_SNAKE_CASE:	Tuple="relu6" ,    _SCREAMING_SNAKE_CASE:	Optional[int]=True ,    _SCREAMING_SNAKE_CASE:	Dict=0.8 ,    _SCREAMING_SNAKE_CASE:	str=0.02 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.001 ,    _SCREAMING_SNAKE_CASE:	int=255 ,    **_SCREAMING_SNAKE_CASE:	str ,    )       -> Union[str, Any]:

    """simple docstring"""

    super().__init__(**_SCREAMING_SNAKE_CASE)

    if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero.")

    __lowerCAmelCase   :						List[Any]				= num_channels
    __lowerCAmelCase   :						str				= image_size
    __lowerCAmelCase   :						Dict				= depth_multiplier
    __lowerCAmelCase   :						str				= depth_divisible_by
    __lowerCAmelCase   :						Optional[int]				= min_depth
    __lowerCAmelCase   :						Union[str, Any]				= expand_ratio
    __lowerCAmelCase   :						Tuple				= output_stride
    __lowerCAmelCase   :						List[str]				= first_layer_is_expansion
    __lowerCAmelCase   :						List[str]				= finegrained_output
    __lowerCAmelCase   :						Dict				= hidden_act
    __lowerCAmelCase   :						str				= tf_padding
    __lowerCAmelCase   :						Union[str, Any]				= classifier_dropout_prob
    __lowerCAmelCase   :						Union[str, Any]				= initializer_range
    __lowerCAmelCase   :						List[str]				= layer_norm_eps
    __lowerCAmelCase   :						int				= semantic_loss_ignore_index






class     A__     (						__SCREAMING_SNAKE_CASE							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			version.parse('1.11'							)

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

    """simple docstring"""

    return OrderedDict([("pixel_values", {0: "batch"})])

 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Mapping[str, Mapping[int, str]]:

    """simple docstring"""

    if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})])
    else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])







 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> float:

    """simple docstring"""

    return 1e-4




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)