



"""simple docstring"""






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__snake_case							:     Union[str, Any]   =					logging.get_logger(__name__)

__snake_case							:     str   =					OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

__snake_case							:     List[str]   =					_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def     _lowercase       (						__snake_case			)		->     List[str]:
			for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
						if class_name in extractors:
									__lowerCAmelCase   :						Optional[int]				= model_type_to_module_name(__snake_case			)

									__lowerCAmelCase   :						List[Any]				= importlib.import_module(F""".{module_name}"""      ,"transformers.models"			)
									try:
												return getattr(__snake_case      ,__snake_case			)
									except AttributeError:
												continue

			for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
						if getattr(__snake_case      ,"__name__"      ,__snake_case			) == class_name:
									return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			__lowerCAmelCase   :						Dict				= importlib.import_module("transformers"			)
			if hasattr(__snake_case      ,__snake_case			):
						return getattr(__snake_case      ,__snake_case			)

			return None


def     _lowercase       (						__snake_case      ,__snake_case = None      ,__snake_case = False      ,__snake_case = False      ,__snake_case = None      ,__snake_case = None      ,__snake_case = None      ,__snake_case = False      ,**__snake_case      ,)		->     Any:
			__lowerCAmelCase   :						str				= get_file_from_repo(
			    __snake_case      ,__snake_case      ,cache_dir=__snake_case      ,force_download=__snake_case      ,resume_download=__snake_case      ,proxies=__snake_case      ,use_auth_token=__snake_case      ,revision=__snake_case      ,local_files_only=__snake_case      ,)
			if resolved_config_file is None:
						logger.info(
						    "Could not locate the image processor configuration file, will try to use the model config instead."			)
						return {}

			with open(__snake_case      ,encoding="utf-8"			) as reader:
						return json.load(__snake_case			)





class     A__     :




	'''simple docstring'''





	def __init__(  self:	int)       -> List[Any]:

				"""simple docstring"""

				raise EnvironmentError(
				    "AutoImageProcessor is designed to be instantiated "
				    "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.")

	@classmethod
	@replace_list_option_in_docstrings(_SCREAMING_SNAKE_CASE)
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Dict ,    _SCREAMING_SNAKE_CASE:	Tuple ,    **_SCREAMING_SNAKE_CASE:	int)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= kwargs.pop("config" ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= kwargs.pop("trust_remote_code" ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[int]				= True

				__lowerCAmelCase		,			__lowerCAmelCase   :						Tuple				= ImageProcessingMixin.get_image_processor_dict(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= config_dict.get("image_processor_type" ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= None
				if "AutoImageProcessor" in config_dict.get("auto_map" ,    {}):
							__lowerCAmelCase   :						int				= config_dict["auto_map"]["AutoImageProcessor"]

				# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
				# and if so, infer the image processor class from there.
				if image_processor_class is None and image_processor_auto_map is None:
							__lowerCAmelCase   :						Tuple				= config_dict.pop("feature_extractor_type" ,    _SCREAMING_SNAKE_CASE)
							if feature_extractor_class is not None:
										logger.warning(
										    "Could not find image processor class in the image processor config or the model config. Loading"
										    " based on pattern matching with the model's feature extractor configuration.")
										__lowerCAmelCase   :						Tuple				= feature_extractor_class.replace("FeatureExtractor" ,    "ImageProcessor")
							if "AutoFeatureExtractor" in config_dict.get("auto_map" ,    {}):
										__lowerCAmelCase   :						int				= config_dict["auto_map"]["AutoFeatureExtractor"]
										__lowerCAmelCase   :						Any				= feature_extractor_auto_map.replace("FeatureExtractor" ,    "ImageProcessor")
										logger.warning(
										    "Could not find image processor auto map in the image processor config or the model config."
										    " Loading based on pattern matching with the model's feature extractor configuration.")

        # If we don't find the image processor class in the image processor config, let's try the model config.
				if image_processor_class is None and image_processor_auto_map is None:
							if not isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
										__lowerCAmelCase   :						int				= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
							# It could be in `config.image_processor_type``
							__lowerCAmelCase   :						List[Any]				= getattr(_SCREAMING_SNAKE_CASE ,    "image_processor_type" ,    _SCREAMING_SNAKE_CASE)
							if hasattr(_SCREAMING_SNAKE_CASE ,    "auto_map") and "AutoImageProcessor" in config.auto_map:
										__lowerCAmelCase   :						List[Any]				= config.auto_map["AutoImageProcessor"]

				if image_processor_class is not None:
							__lowerCAmelCase   :						Any				= image_processor_class_from_name(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= image_processor_auto_map is not None
				__lowerCAmelCase   :						Optional[Any]				= image_processor_class is not None or type(_SCREAMING_SNAKE_CASE) in IMAGE_PROCESSOR_MAPPING
				__lowerCAmelCase   :						Union[str, Any]				= resolve_trust_remote_code(
				    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				if has_remote_code and trust_remote_code:
							__lowerCAmelCase   :						Union[str, Any]				= get_class_from_dynamic_module(
							    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						int				= kwargs.pop("code_revision" ,    _SCREAMING_SNAKE_CASE)
							if os.path.isdir(_SCREAMING_SNAKE_CASE):
										image_processor_class.register_for_auto_class()
							return image_processor_class.from_dict(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
				elif image_processor_class is not None:
							return image_processor_class.from_dict(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
				# Last try: we use the IMAGE_PROCESSOR_MAPPING.
				elif type(_SCREAMING_SNAKE_CASE) in IMAGE_PROCESSOR_MAPPING:
							__lowerCAmelCase   :						Tuple				= IMAGE_PROCESSOR_MAPPING[type(_SCREAMING_SNAKE_CASE)]
							return image_processor_class.from_dict(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				raise ValueError(
				    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
				    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
				    F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}""")







	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Any)       -> str:

				"""simple docstring"""

				IMAGE_PROCESSOR_MAPPING.register(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)




"""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)