

"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

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



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Any:
 lowerCAmelCase__      :							int    =							CLIPSegTextConfig()
 lowerCAmelCase__      :							Tuple    =							CLIPSegVisionConfig(patch_size=1_6      )

 lowerCAmelCase__      :							Optional[int]    =							True if '''refined''' in model_name else False
 lowerCAmelCase__      :							Any    =							1_6 if '''rd16''' in model_name else 6_4

 lowerCAmelCase__      :							List[str]    =							CLIPSegConfig.from_text_vision_configs(
     SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				use_complex_transposed_convolution=SCREAMING_SNAKE_CASE__				,				reduce_dim=SCREAMING_SNAKE_CASE__				,				)
 return config



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:


 if "clip_model" in name:
  lowerCAmelCase__      :							int    =							name.replace("clip_model"				,				"clip"      )
 if "transformer" in name:
  if "visual" in name:
   lowerCAmelCase__      :							List[Any]    =							name.replace("visual.transformer"				,				"vision_model"      )
  else:
   lowerCAmelCase__      :							List[str]    =							name.replace("transformer"				,				"text_model"      )
 if "resblocks" in name:
  lowerCAmelCase__      :							str    =							name.replace("resblocks"				,				"encoder.layers"      )
 if "ln_1" in name:
  lowerCAmelCase__      :							Union[str, Any]    =							name.replace("ln_1"				,				"layer_norm1"      )
 if "ln_2" in name:
  lowerCAmelCase__      :							Optional[Any]    =							name.replace("ln_2"				,				"layer_norm2"      )
 if "c_fc" in name:
  lowerCAmelCase__      :							Dict    =							name.replace("c_fc"				,				"fc1"      )
 if "c_proj" in name:
  lowerCAmelCase__      :							Optional[int]    =							name.replace("c_proj"				,				"fc2"      )
 if "attn" in name and "self" not in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("attn"				,				"self_attn"      )
 # text encoder
 if "token_embedding" in name:
  lowerCAmelCase__      :							Optional[int]    =							name.replace("token_embedding"				,				"text_model.embeddings.token_embedding"      )
 if "positional_embedding" in name and "visual" not in name:
  lowerCAmelCase__      :							Union[str, Any]    =							name.replace("positional_embedding"				,				"text_model.embeddings.position_embedding.weight"      )
 if "ln_final" in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("ln_final"				,				"text_model.final_layer_norm"      )
 # vision encoder
 if "visual.class_embedding" in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("visual.class_embedding"				,				"vision_model.embeddings.class_embedding"      )
 if "visual.conv1" in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("visual.conv1"				,				"vision_model.embeddings.patch_embedding"      )
 if "visual.positional_embedding" in name:
  lowerCAmelCase__      :							Optional[Any]    =							name.replace("visual.positional_embedding"				,				"vision_model.embeddings.position_embedding.weight"      )
 if "visual.ln_pre" in name:
  lowerCAmelCase__      :							Any    =							name.replace("visual.ln_pre"				,				"vision_model.pre_layrnorm"      )
 if "visual.ln_post" in name:
  lowerCAmelCase__      :							Union[str, Any]    =							name.replace("visual.ln_post"				,				"vision_model.post_layernorm"      )
 # projection layers
 if "visual.proj" in name:
  lowerCAmelCase__      :							Any    =							name.replace("visual.proj"				,				"visual_projection.weight"      )
 if "text_projection" in name:
  lowerCAmelCase__      :							List[Any]    =							name.replace("text_projection"				,				"text_projection.weight"      )
 # decoder
 if "trans_conv" in name:
  lowerCAmelCase__      :							List[Any]    =							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:
  lowerCAmelCase__      :							Dict    =							'''decoder.''' + name
 if "blocks" in name:
  lowerCAmelCase__      :							Any    =							name.replace("blocks"				,				"decoder.layers"      )
 if "linear1" in name:
  lowerCAmelCase__      :							List[Any]    =							name.replace("linear1"				,				"mlp.fc1"      )
 if "linear2" in name:
  lowerCAmelCase__      :							int    =							name.replace("linear2"				,				"mlp.fc2"      )
 if "norm1" in name and "layer_" not in name:
  lowerCAmelCase__      :							Any    =							name.replace("norm1"				,				"layer_norm1"      )
 if "norm2" in name and "layer_" not in name:
  lowerCAmelCase__      :							Union[str, Any]    =							name.replace("norm2"				,				"layer_norm2"      )

 return name



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	Any:


 for key in orig_state_dict.copy().keys():
  lowerCAmelCase__      :							Union[str, Any]    =							orig_state_dict.pop(SCREAMING_SNAKE_CASE__      )

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

   if "weight" in key:
    lowerCAmelCase__      :							Optional[int]    =							val[:dim, :]
    lowerCAmelCase__      :							str    =							val[
        dim : dim * 2, :
    ]
    lowerCAmelCase__      :							List[Any]    =							val[-dim:, :]
   else:
    lowerCAmelCase__      :							Any    =							val[:dim]
    lowerCAmelCase__      :							str    =							val[dim : dim * 2]
    lowerCAmelCase__      :							Optional[int]    =							val[-dim:]
  elif "self_attn" in key and "out_proj" not in key:
   lowerCAmelCase__      :							List[Any]    =							key.split("."      )
   lowerCAmelCase__      :							Dict    =							int(key_split[1]      )
   lowerCAmelCase__      :							Optional[Any]    =							config.reduce_dim
   if "weight" in key:
    lowerCAmelCase__      :							List[Any]    =							val[:dim, :]
    lowerCAmelCase__      :							Optional[Any]    =							val[dim : dim * 2, :]
    lowerCAmelCase__      :							List[str]    =							val[-dim:, :]
   else:
    lowerCAmelCase__      :							Optional[Any]    =							val[:dim]
    lowerCAmelCase__      :							Tuple    =							val[dim : dim * 2]
    lowerCAmelCase__      :							Union[str, Any]    =							val[-dim:]
  else:
   lowerCAmelCase__      :							str    =							rename_key(SCREAMING_SNAKE_CASE__      )
   if "visual_projection" in new_name or "text_projection" in new_name:
    lowerCAmelCase__      :							Optional[int]    =							val.T
   lowerCAmelCase__      :							Any    =							val

 return orig_state_dict



def 			SCREAMING_SNAKE_CASE       (   ) ->	int:
 lowerCAmelCase__      :							int    =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 lowerCAmelCase__      :							List[Any]    =							Image.open(requests.get(SCREAMING_SNAKE_CASE__				,				stream=SCREAMING_SNAKE_CASE__      ).raw      )
 return image



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      ) ->	Dict:
 lowerCAmelCase__      :							Any    =							get_clipseg_config(SCREAMING_SNAKE_CASE__      )
 lowerCAmelCase__      :							int    =							CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__      )
 model.eval()

 lowerCAmelCase__      :							List[str]    =							torch.load(SCREAMING_SNAKE_CASE__				,				map_location="cpu"      )

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

    # rename some keys
 lowerCAmelCase__      :							List[str]    =							convert_state_dict(SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__      )
 lowerCAmelCase__      :							Dict    =							model.load_state_dict(SCREAMING_SNAKE_CASE__				,				strict=SCREAMING_SNAKE_CASE__      )

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

 lowerCAmelCase__      :							Any    =							ViTImageProcessor(size=3_5_2      )
 lowerCAmelCase__      :							List[Any]    =							CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"      )
 lowerCAmelCase__      :							Optional[Any]    =							CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__				,				tokenizer=SCREAMING_SNAKE_CASE__      )

 lowerCAmelCase__      :							List[Any]    =							prepare_img()
 lowerCAmelCase__      :							Union[str, Any]    =							['''a glass''', '''something to fill''', '''wood''', '''a jar''']

 lowerCAmelCase__      :							Any    =							processor(text=SCREAMING_SNAKE_CASE__				,				images=[image] * len(SCREAMING_SNAKE_CASE__      )				,				padding="max_length"				,				return_tensors="pt"      )

 with torch.no_grad():
  lowerCAmelCase__      :							str    =							model(**SCREAMING_SNAKE_CASE__      )

 # verify values
 lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([0.1_110, -0.1_882, 0.1_645]      )
 lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([0.2_692, -0.7_197, -0.1_328]      )
 if model_name == "clipseg-rd64-refined":
  lowerCAmelCase__      :							int    =							torch.tensor(
      [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]      )
 elif model_name == "clipseg-rd64":
  lowerCAmelCase__      :							str    =							torch.tensor(
      [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]      )
 elif model_name == "clipseg-rd16":
  lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor(
      [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]      )
 else:
  raise ValueError(F"""Model name {model_name} not supported."""      )

 assert torch.allclose(outputs.logits[0, :3, :3]				,				SCREAMING_SNAKE_CASE__				,				atol=1E-3      )
 assert torch.allclose(outputs.conditional_embeddings[0, :3]				,				SCREAMING_SNAKE_CASE__				,				atol=1E-3      )
 assert torch.allclose(outputs.pooled_output[0, :3]				,				SCREAMING_SNAKE_CASE__				,				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(SCREAMING_SNAKE_CASE__      )
  processor.save_pretrained(SCREAMING_SNAKE_CASE__      )

 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__":
    _UpperCamelCase		    =						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."""
    )

    _UpperCamelCase		    =						parser.parse_args()
    convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_UpperCamelCase		    =						logging.getLogger(__name__)






@dataclass
class  __a				:





       """simple docstring"""


       __UpperCamelCase				:			str          =	field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
       __UpperCamelCase				:			Optional[str]          =	field(
           default=__magic_name__					,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
       __UpperCamelCase				:			Optional[str]          =	field(
           default=__magic_name__					,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
       __UpperCamelCase				:			Optional[str]          =	field(
           default=__magic_name__					,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,  )






@dataclass
class  __a				:





       """simple docstring"""


       __UpperCamelCase				:			str          =	field(
           metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()					)}					)
       __UpperCamelCase				:			str          =	field(
           metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
       __UpperCamelCase				:			int          =	field(
           default=128					,  metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }					,  )
       __UpperCamelCase				:			bool          =	field(
           default=__magic_name__					,  metadata={'help': 'Overwrite the cached training and evaluation sets'}					)




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Dict[str, torch.Tensor]:
 lowerCAmelCase__      :							List[Any]    =							default_data_collator(lowercase__      )
 lowerCAmelCase__      :							Union[str, Any]    =							batch.pop("pairID"				,				lowercase__      )
 return batch



def 			SCREAMING_SNAKE_CASE       (   ) ->	int:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCAmelCase__      :							str    =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
 lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir      )
     and os.listdir(training_args.output_dir      )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      " --overwrite_output_dir to overcome."      )

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,				datefmt="%m/%d/%Y %H:%M:%S"				,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,				)
 logger.warning(
     "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"				,				training_args.local_rank				,				training_args.device				,				training_args.n_gpu				,				bool(training_args.local_rank != -1      )				,				training_args.fpaa				,				)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank      ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("Training/evaluation parameters %s"				,				lowercase__      )

 # Set seed
 set_seed(training_args.seed      )

 try:
  lowerCAmelCase__      :							Tuple    =							hans_tasks_num_labels[data_args.task_name]
 except KeyError:
  raise ValueError("Task not found: %s" % (data_args.task_name)      )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 lowerCAmelCase__      :							int    =							AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path				,				num_labels=lowercase__				,				finetuning_task=data_args.task_name				,				cache_dir=model_args.cache_dir				,				)
 lowerCAmelCase__      :							Optional[int]    =							AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,				cache_dir=model_args.cache_dir				,				)
 lowerCAmelCase__      :							Union[str, Any]    =							AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path				,				from_tf=bool(".ckpt" in model_args.model_name_or_path      )				,				config=lowercase__				,				cache_dir=model_args.cache_dir				,				)

 # Get datasets
 lowerCAmelCase__      :							Dict    =							(
     HansDataset(
         data_dir=data_args.data_dir				,				tokenizer=lowercase__				,				task=data_args.task_name				,				max_seq_length=data_args.max_seq_length				,				overwrite_cache=data_args.overwrite_cache				,				)
     if training_args.do_train
     else None
 )
 lowerCAmelCase__      :							Union[str, Any]    =							(
     HansDataset(
         data_dir=data_args.data_dir				,				tokenizer=lowercase__				,				task=data_args.task_name				,				max_seq_length=data_args.max_seq_length				,				overwrite_cache=data_args.overwrite_cache				,				evaluate=lowercase__				,				)
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 lowerCAmelCase__      :							Any    =							Trainer(
     model=lowercase__				,				args=lowercase__				,				train_dataset=lowercase__				,				eval_dataset=lowercase__				,				data_collator=lowercase__				,				)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
 if training_args.do_eval:
  logger.info("*** Evaluate ***"      )

  lowerCAmelCase__      :							Tuple    =							trainer.predict(lowercase__      )
  lowerCAmelCase__      :							Optional[Any]    =							output.predictions
  lowerCAmelCase__      :							Dict    =							np.argmax(lowercase__				,				axis=1      )

  lowerCAmelCase__      :							int    =							[ex.pairID for ex in eval_dataset]
  lowerCAmelCase__      :							Tuple    =							os.path.join(training_args.output_dir				,				"hans_predictions.txt"      )
  lowerCAmelCase__      :							Tuple    =							eval_dataset.get_labels()
  if trainer.is_world_master():
   with open(lowercase__				,				"w"      ) as writer:
    writer.write("pairID,gold_label\n"      )
    for pid, pred in zip(lowercase__				,				lowercase__      ):
     writer.write("ex" + str(lowercase__      ) + "," + label_list[int(lowercase__      )] + "\n"      )

  trainer._log(output.metrics      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Optional[Any]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
    main()






