

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
						from PIL import Image






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						(
			    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
			    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
			    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
			)
			SCREAMING_SNAKE_CASE							   =						'CIDAS/clipseg-rd64-refined'
			SCREAMING_SNAKE_CASE							   =						'image_segmenter'
			SCREAMING_SNAKE_CASE							   =						CLIPSegForImageSegmentation

			SCREAMING_SNAKE_CASE							   =						['image', 'text']
			SCREAMING_SNAKE_CASE							   =						['image']





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> Tuple:




						'''simple docstring'''
						requires_backends(self					,  ['vision']					)
						super().__init__(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						return self.pre_processor(text=[label]					,  images=[image]					,  padding=__snake_case					,  return_tensors='pt'					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Dict:




						'''simple docstring'''
						with torch.no_grad():
									__a        =self.model(**__snake_case					).logits
						return logits





			def 		__magic_name__    (		self					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						__a        =outputs.cpu().detach().numpy()
						__a        =0
						__a        =1
						return Image.fromarray((array * 255).astype(np.uinta					)					)









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    '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        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

			__a        ={key.replace('decoder'       ,     'model'	): val for key, val in state_dict.items()}

			__a        =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       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			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.")
						_lowerCAmelCase					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






