






"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Union[str, Any]:
 UpperCAmelCase__ 						=				CLIPSegTextConfig()
 UpperCAmelCase__ 						=				CLIPSegVisionConfig(patch_size=1_6			)

 UpperCAmelCase__ 						=				True if """refined""" in model_name else False
 UpperCAmelCase__ 						=				1_6 if """rd16""" in model_name else 6_4

 UpperCAmelCase__ 						=				CLIPSegConfig.from_text_vision_configs(
     a_       ,	a_       ,	use_complex_transposed_convolution=a_       ,	reduce_dim=a_       ,	)
 return config




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->List[Any]:







 if "clip_model" in name:
  UpperCAmelCase__ 						=				name.replace("""clip_model"""       ,	"""clip"""			)
 if "transformer" in name:
  if "visual" in name:
   UpperCAmelCase__ 						=				name.replace("""visual.transformer"""       ,	"""vision_model"""			)
  else:
   UpperCAmelCase__ 						=				name.replace("""transformer"""       ,	"""text_model"""			)
 if "resblocks" in name:
  UpperCAmelCase__ 						=				name.replace("""resblocks"""       ,	"""encoder.layers"""			)
 if "ln_1" in name:
  UpperCAmelCase__ 						=				name.replace("""ln_1"""       ,	"""layer_norm1"""			)
 if "ln_2" in name:
  UpperCAmelCase__ 						=				name.replace("""ln_2"""       ,	"""layer_norm2"""			)
 if "c_fc" in name:
  UpperCAmelCase__ 						=				name.replace("""c_fc"""       ,	"""fc1"""			)
 if "c_proj" in name:
  UpperCAmelCase__ 						=				name.replace("""c_proj"""       ,	"""fc2"""			)
 if "attn" in name and "self" not in name:
  UpperCAmelCase__ 						=				name.replace("""attn"""       ,	"""self_attn"""			)
 # text encoder
 if "token_embedding" in name:
  UpperCAmelCase__ 						=				name.replace("""token_embedding"""       ,	"""text_model.embeddings.token_embedding"""			)
 if "positional_embedding" in name and "visual" not in name:
  UpperCAmelCase__ 						=				name.replace("""positional_embedding"""       ,	"""text_model.embeddings.position_embedding.weight"""			)
 if "ln_final" in name:
  UpperCAmelCase__ 						=				name.replace("""ln_final"""       ,	"""text_model.final_layer_norm"""			)
 # vision encoder
 if "visual.class_embedding" in name:
  UpperCAmelCase__ 						=				name.replace("""visual.class_embedding"""       ,	"""vision_model.embeddings.class_embedding"""			)
 if "visual.conv1" in name:
  UpperCAmelCase__ 						=				name.replace("""visual.conv1"""       ,	"""vision_model.embeddings.patch_embedding"""			)
 if "visual.positional_embedding" in name:
  UpperCAmelCase__ 						=				name.replace("""visual.positional_embedding"""       ,	"""vision_model.embeddings.position_embedding.weight"""			)
 if "visual.ln_pre" in name:
  UpperCAmelCase__ 						=				name.replace("""visual.ln_pre"""       ,	"""vision_model.pre_layrnorm"""			)
 if "visual.ln_post" in name:
  UpperCAmelCase__ 						=				name.replace("""visual.ln_post"""       ,	"""vision_model.post_layernorm"""			)
 # projection layers
 if "visual.proj" in name:
  UpperCAmelCase__ 						=				name.replace("""visual.proj"""       ,	"""visual_projection.weight"""			)
 if "text_projection" in name:
  UpperCAmelCase__ 						=				name.replace("""text_projection"""       ,	"""text_projection.weight"""			)
 # decoder
 if "trans_conv" in name:
  UpperCAmelCase__ 						=				name.replace("""trans_conv"""       ,	"""transposed_convolution"""			)
 if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
  UpperCAmelCase__ 						=				"""decoder.""" + name
 if "blocks" in name:
  UpperCAmelCase__ 						=				name.replace("""blocks"""       ,	"""decoder.layers"""			)
 if "linear1" in name:
  UpperCAmelCase__ 						=				name.replace("""linear1"""       ,	"""mlp.fc1"""			)
 if "linear2" in name:
  UpperCAmelCase__ 						=				name.replace("""linear2"""       ,	"""mlp.fc2"""			)
 if "norm1" in name and "layer_" not in name:
  UpperCAmelCase__ 						=				name.replace("""norm1"""       ,	"""layer_norm1"""			)
 if "norm2" in name and "layer_" not in name:
  UpperCAmelCase__ 						=				name.replace("""norm2"""       ,	"""layer_norm2"""			)

 return name




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Any:







 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__ 						=				orig_state_dict.pop(a_			)

  if key.startswith("""clip_model"""			) and "attn.in_proj" in key:
   UpperCAmelCase__ 						=				key.split("""."""			)
   if "visual" in key:
    UpperCAmelCase__ 						=				int(key_split[4]			)
    UpperCAmelCase__ 						=				config.vision_config.hidden_size
    UpperCAmelCase__ 						=				"""vision_model"""
   else:
    UpperCAmelCase__ 						=				int(key_split[3]			)
    UpperCAmelCase__ 						=				config.text_config.hidden_size
    UpperCAmelCase__ 						=				"""text_model"""

   if "weight" in key:
    UpperCAmelCase__ 						=				val[:dim, :]
    UpperCAmelCase__ 						=				val[
        dim : dim * 2, :
    ]
    UpperCAmelCase__ 						=				val[-dim:, :]
   else:
    UpperCAmelCase__ 						=				val[:dim]
    UpperCAmelCase__ 						=				val[dim : dim * 2]
    UpperCAmelCase__ 						=				val[-dim:]
  elif "self_attn" in key and "out_proj" not in key:
   UpperCAmelCase__ 						=				key.split("""."""			)
   UpperCAmelCase__ 						=				int(key_split[1]			)
   UpperCAmelCase__ 						=				config.reduce_dim
   if "weight" in key:
    UpperCAmelCase__ 						=				val[:dim, :]
    UpperCAmelCase__ 						=				val[dim : dim * 2, :]
    UpperCAmelCase__ 						=				val[-dim:, :]
   else:
    UpperCAmelCase__ 						=				val[:dim]
    UpperCAmelCase__ 						=				val[dim : dim * 2]
    UpperCAmelCase__ 						=				val[-dim:]
  else:
   UpperCAmelCase__ 						=				rename_key(a_			)
   if "visual_projection" in new_name or "text_projection" in new_name:
    UpperCAmelCase__ 						=				val.T
   UpperCAmelCase__ 						=				val

 return orig_state_dict




def       snake_case__		(   )    ->Optional[int]:
 UpperCAmelCase__ 						=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase__ 						=				Image.open(requests.get(a_       ,	stream=a_			).raw			)
 return image




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Optional[int]:
 UpperCAmelCase__ 						=				get_clipseg_config(a_			)
 UpperCAmelCase__ 						=				CLIPSegForImageSegmentation(a_			)
 model.eval()

 UpperCAmelCase__ 						=				torch.load(a_       ,	map_location="""cpu"""			)

 # remove some keys
 for key in state_dict.copy().keys():
  if key.startswith("""model"""			):
   state_dict.pop(a_       ,	a_			)

    # rename some keys
 UpperCAmelCase__ 						=				convert_state_dict(a_       ,	a_			)
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				model.load_state_dict(a_       ,	strict=a_			)

 if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
  raise ValueError("""Missing keys that are not expected: {}""".format(a_			)			)
 if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  raise ValueError(F'''Unexpected keys: {unexpected_keys}'''			)

 UpperCAmelCase__ 						=				ViTImageProcessor(size=3_5_2			)
 UpperCAmelCase__ 						=				CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			)
 UpperCAmelCase__ 						=				CLIPSegProcessor(image_processor=a_       ,	tokenizer=a_			)

 UpperCAmelCase__ 						=				prepare_img()
 UpperCAmelCase__ 						=				["""a glass""", """something to fill""", """wood""", """a jar"""]

 UpperCAmelCase__ 						=				processor(text=a_       ,	images=[image] * len(a_			)       ,	padding="""max_length"""       ,	return_tensors="""pt"""			)

 with torch.no_grad():
  UpperCAmelCase__ 						=				model(**a_			)

 # verify values
 UpperCAmelCase__ 						=				torch.tensor([0.1110, -0.1882, 0.1645]			)
 UpperCAmelCase__ 						=				torch.tensor([0.2692, -0.7197, -0.1328]			)
 if model_name == "clipseg-rd64-refined":
  UpperCAmelCase__ 						=				torch.tensor(
      [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]			)
 elif model_name == "clipseg-rd64":
  UpperCAmelCase__ 						=				torch.tensor(
      [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]			)
 elif model_name == "clipseg-rd16":
  UpperCAmelCase__ 						=				torch.tensor(
      [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]			)
 else:
  raise ValueError(F'''Model name {model_name} not supported.'''			)

 assert torch.allclose(outputs.logits[0, :3, :3]       ,	a_       ,	atol=1E-3			)
 assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,	a_       ,	atol=1E-3			)
 assert torch.allclose(outputs.pooled_output[0, :3]       ,	a_       ,	atol=1E-3			)
 print("""Looks ok!"""			)

 if pytorch_dump_folder_path is not None:
  print(F'''Saving model and processor to {pytorch_dump_folder_path}'''			)
  model.save_pretrained(a_			)
  processor.save_pretrained(a_			)

 if push_to_hub:
  print(F'''Pushing model and processor for {model_name} to the hub'''			)
  model.push_to_hub(F'''CIDAS/{model_name}'''			)
  processor.push_to_hub(F'''CIDAS/{model_name}'''			)


if __name__ == "__main__":
 a  :						List[Any]					       =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''clipseg-rd64''',
     type=str,
     choices=['''clipseg-rd16''', '''clipseg-rd64''', '''clipseg-rd64-refined'''],
     help=(
         '''Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'''
         ''' reduce dimension)'''
     ),
 )
 parser.add_argument(
     '''--checkpoint_path''',
     default='''/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth''',
     type=str,
     help=(
         '''Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'''
         ''' the decoder weights.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )

 a  :						Dict					       =							parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






from collections import OrderedDict
from typing import Mapping

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


A__							=				logging.get_logger(__name__)

A__							=				{
    "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
    "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
    "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
    "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
    "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Dict												=	"ernie"







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict=3_05_22	,			__UpperCamelCase:			str=7_68	,			__UpperCamelCase:			Tuple=12	,			__UpperCamelCase:			int=12	,			__UpperCamelCase:			Optional[Any]=30_72	,			__UpperCamelCase:			str="gelu"	,			__UpperCamelCase:			Any=0.1	,			__UpperCamelCase:			int=0.1	,			__UpperCamelCase:			int=5_12	,			__UpperCamelCase:			Any=2	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Any=False	,			__UpperCamelCase:			Optional[int]=0.02	,			__UpperCamelCase:			List[Any]=1E-12	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Tuple="absolute"	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			Optional[Any]=None	,			**__UpperCamelCase:			List[Any]	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     hidden_act
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     type_vocab_size
						__magic_name__				=     task_type_vocab_size
						__magic_name__				=     use_task_id
						__magic_name__				=     initializer_range
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     position_embedding_type
						__magic_name__				=     use_cache
						__magic_name__				=     classifier_dropout




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__				=     {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__				=     {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]   )




