




'''simple docstring'''






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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE = None				,      )    ->   Tuple:
      snake_case__       :   List[Any]	      =    {}

      if train_file is not None:
            snake_case__       :   Optional[int]	      =    [train_file]
      if eval_file is not None:
            snake_case__       :   Tuple	      =    [eval_file]
      if test_file is not None:
            snake_case__       :   Union[str, Any]	      =    [test_file]

      snake_case__       :   Dict	      =    datasets.load_dataset('csv'				,      data_files=__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Any	      =    list(ds[list(files.keys()		)[0]].features.keys()		)
      snake_case__       :   Dict	      =    features_name.pop(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Union[str, Any]	      =    list(set(ds[list(files.keys()		)[0]][label_name]		)		)
      snake_case__       :   List[Any]	      =    {label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE		)}
      snake_case__       :   List[Any]	      =    tokenizer.model_input_names
      snake_case__       :   Any	      =    {}

      if len(__SCREAMING_SNAKE_CASE		) == 1:
            for k in files.keys():
                  snake_case__       :   Tuple	      =    ds[k].map(
                      lambda __SCREAMING_SNAKE_CASE		: tokenizer.batch_encode_plus(
                          example[features_name[0]]				,      truncation=__SCREAMING_SNAKE_CASE				,      max_length=__SCREAMING_SNAKE_CASE				,      padding='max_length'		)				,      batched=__SCREAMING_SNAKE_CASE				,      )
      elif len(__SCREAMING_SNAKE_CASE		) == 2:
            for k in files.keys():
                  snake_case__       :   Optional[int]	      =    ds[k].map(
                      lambda __SCREAMING_SNAKE_CASE		: tokenizer.batch_encode_plus(
                          (example[features_name[0]], example[features_name[1]])				,      truncation=__SCREAMING_SNAKE_CASE				,      max_length=__SCREAMING_SNAKE_CASE				,      padding='max_length'				,      )				,      batched=__SCREAMING_SNAKE_CASE				,      )

      def gen_train():
            for ex in transformed_ds[datasets.Split.TRAIN]:
                  snake_case__       :   Optional[Any]	      =    {k: v for k, v in ex.items() if k in input_names}
                  snake_case__       :   int	      =    labelaid[ex[label_name]]
                  yield (d, label)

      def gen_val():
            for ex in transformed_ds[datasets.Split.VALIDATION]:
                  snake_case__       :   Tuple	      =    {k: v for k, v in ex.items() if k in input_names}
                  snake_case__       :   List[str]	      =    labelaid[ex[label_name]]
                  yield (d, label)

      def gen_test():
            for ex in transformed_ds[datasets.Split.TEST]:
                  snake_case__       :   Optional[Any]	      =    {k: v for k, v in ex.items() if k in input_names}
                  snake_case__       :   Tuple	      =    labelaid[ex[label_name]]
                  yield (d, label)

      snake_case__       :   Dict	      =    (
          tf.data.Dataset.from_generator(
              __SCREAMING_SNAKE_CASE				,      ({k: tf.intaa for k in input_names}, tf.intaa)				,      ({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))				,      )
          if datasets.Split.TRAIN in transformed_ds
          else None
      )

      if train_ds is not None:
            snake_case__       :   str	      =    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

      snake_case__       :   int	      =    (
          tf.data.Dataset.from_generator(
              __SCREAMING_SNAKE_CASE				,      ({k: tf.intaa for k in input_names}, tf.intaa)				,      ({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))				,      )
          if datasets.Split.VALIDATION in transformed_ds
          else None
      )

      if val_ds is not None:
            snake_case__       :   Union[str, Any]	      =    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

      snake_case__       :   Tuple	      =    (
          tf.data.Dataset.from_generator(
              __SCREAMING_SNAKE_CASE				,      ({k: tf.intaa for k in input_names}, tf.intaa)				,      ({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))				,      )
          if datasets.Split.TEST in transformed_ds
          else None
      )

      if test_ds is not None:
            snake_case__       :   str	      =    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]		)		)		)

      return train_ds, val_ds, test_ds, labelaid


A_			      =		logging.getLogger(__name__)






@dataclass
class        lowercase_      :
     A_											=			field(metadata={"help": "Which column contains the label"}							)
     A_											=			field(default=UpperCAmelCase__						,       metadata={"help": "The path of the training file"}							)
     A_											=			field(default=UpperCAmelCase__						,       metadata={"help": "The path of the development file"}							)
     A_											=			field(default=UpperCAmelCase__						,       metadata={"help": "The path of the test file"}							)
     A_											=			field(
         default=1_2_8						,       metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }						,       )
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={"help": "Overwrite the cached training and evaluation sets"}							)






@dataclass
class        lowercase_      :
     A_											=			field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
     A_											=			field(default=UpperCAmelCase__						,       metadata={"help": "Set this flag to use fast tokenization."}							)
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,       )






def 		UpperCamelCase__      (		)    ->   str:
      # 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.
      snake_case__       :   Dict	      =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
      snake_case__      ,							snake_case__      ,							snake_case__       :   Optional[int]	      =    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				,      )
      logger.info(
          f"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1		)}, "
          f"16-bits training: {training_args.fpaa}"		)
      logger.info(f"Training/evaluation parameters {training_args}"		)

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

      snake_case__       :   Optional[Any]	      =    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				,      )

      snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Dict	      =    get_tfds(
          train_file=data_args.train_file				,      eval_file=data_args.dev_file				,      test_file=data_args.test_file				,      tokenizer=__SCREAMING_SNAKE_CASE				,      label_column_id=data_args.label_column_id				,      max_seq_length=data_args.max_seq_length				,      )

      snake_case__       :   Dict	      =    AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path				,      num_labels=len(__SCREAMING_SNAKE_CASE		)				,      labelaid=__SCREAMING_SNAKE_CASE				,      idalabel={id: label for label, id in labelaid.items()}				,      finetuning_task='text-classification'				,      cache_dir=model_args.cache_dir				,      )

      with training_args.strategy.scope():
            snake_case__       :   List[str]	      =    TFAutoModelForSequenceClassification.from_pretrained(
                model_args.model_name_or_path				,      from_pt=bool('.bin' in model_args.model_name_or_path		)				,      config=__SCREAMING_SNAKE_CASE				,      cache_dir=model_args.cache_dir				,      )

      def compute_metrics(__SCREAMING_SNAKE_CASE		) -> Dict:
            snake_case__       :   str	      =    np.argmax(p.predictions				,      axis=1		)

            return {"acc": (preds == p.label_ids).mean()}

      # Initialize our Trainer
      snake_case__       :   int	      =    TFTrainer(
          model=__SCREAMING_SNAKE_CASE				,      args=__SCREAMING_SNAKE_CASE				,      train_dataset=__SCREAMING_SNAKE_CASE				,      eval_dataset=__SCREAMING_SNAKE_CASE				,      compute_metrics=__SCREAMING_SNAKE_CASE				,      )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir		)

      # Evaluation
      snake_case__       :   Optional[Any]	      =    {}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'		)
            snake_case__       :   Tuple	      =    trainer.evaluate()
            snake_case__       :   str	      =    os.path.join(training_args.output_dir				,      'eval_results.txt'		)

            with open(__SCREAMING_SNAKE_CASE				,      'w'		) as writer:
                  logger.info('***** Eval results *****'		)

                  for key, value in result.items():
                        logger.info(f"  {key} = {value}"		)
                        writer.write(f"{key} = {value}\n"		)

                  results.update(__SCREAMING_SNAKE_CASE		)

      return results


if __name__ == "__main__":
      main()






'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						lowerCamelCase__   (				a							):
						# vision encoder
						if "img_encoder.pos_embed" in name:
												__snake_case						 =   name.replace('img_encoder.pos_embed'    ,	'vision_model.embeddings.position_embeddings'							)
						if "img_encoder.patch_embed.proj" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.proj'    ,	'vision_model.embeddings.patch_embeddings.projection'							)
						if "img_encoder.patch_embed.norm" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.norm'    ,	'vision_model.embeddings.layernorm'							)
						if "img_encoder.layers" in name:
												__snake_case						 =   name.replace('img_encoder.layers'    ,	'vision_model.encoder.stages'							)
						if "blocks" in name and "res" not in name:
												__snake_case						 =   name.replace('blocks'    ,	'layers'							)
						if "attn" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('attn'    ,	'self_attn'							)
						if "proj" in name and "self_attn" in name and "text" not in name:
												__snake_case						 =   name.replace('proj'    ,	'out_proj'							)
						if "pre_assign_attn.attn.proj" in name:
												__snake_case						 =   name.replace('pre_assign_attn.attn.proj'    ,	'pre_assign_attn.attn.out_proj'							)
						if "norm1" in name:
												__snake_case						 =   name.replace('norm1'    ,	'layer_norm1'							)
						if "norm2" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('norm2'    ,	'layer_norm2'							)
						if "img_encoder.norm" in name:
												__snake_case						 =   name.replace('img_encoder.norm'    ,	'vision_model.layernorm'							)
						# text encoder
						if "text_encoder.token_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.token_embedding'    ,	'text_model.embeddings.token_embedding'							)
						if "text_encoder.positional_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.positional_embedding'    ,	'text_model.embeddings.position_embedding.weight'							)
						if "text_encoder.transformer.resblocks." in name:
												__snake_case						 =   name.replace('text_encoder.transformer.resblocks.'    ,	'text_model.encoder.layers.'							)
						if "ln_1" in name:
												__snake_case						 =   name.replace('ln_1'    ,	'layer_norm1'							)
						if "ln_2" in name:
												__snake_case						 =   name.replace('ln_2'    ,	'layer_norm2'							)
						if "c_fc" in name:
												__snake_case						 =   name.replace('c_fc'    ,	'fc1'							)
						if "c_proj" in name:
												__snake_case						 =   name.replace('c_proj'    ,	'fc2'							)
						if "text_encoder" in name:
												__snake_case						 =   name.replace('text_encoder'    ,	'text_model'							)
						if "ln_final" in name:
												__snake_case						 =   name.replace('ln_final'    ,	'final_layer_norm'							)
						# projection layers
						if "img_projector.linear_hidden." in name:
												__snake_case						 =   name.replace('img_projector.linear_hidden.'    ,	'visual_projection.'							)
						if "img_projector.linear_out." in name:
												__snake_case						 =   name.replace('img_projector.linear_out.'    ,	'visual_projection.3.'							)
						if "text_projector.linear_hidden" in name:
												__snake_case						 =   name.replace('text_projector.linear_hidden'    ,	'text_projection'							)
						if "text_projector.linear_out" in name:
												__snake_case						 =   name.replace('text_projector.linear_out'    ,	'text_projection.3'							)

						return name
def 						lowerCamelCase__   (				a    ,	a							):
						for key in orig_state_dict.copy().keys():
												__snake_case						 =   orig_state_dict.pop(a							)

												if "qkv" in key:
																		# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case ,       __snake_case						 =   int(key_split[2]							), int(key_split[4]							)
																		__snake_case						 =   config.vision_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[dim : dim * 2, :]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												elif "in_proj" in key:
																		# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case						 =   int(key_split[3]							)
																		__snake_case						 =   config.text_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[
																								    dim : dim * 2, :
																								]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												else:
																		__snake_case						 =   rename_key(a							)
																		# squeeze if necessary
																		if (
																		    "text_projection.0" in new_name
																		    or "text_projection.3" in new_name
																		    or "visual_projection.0" in new_name
																		    or "visual_projection.3" in new_name
																		):
																								__snake_case						 =   val.squeeze_()
																		else:
																								__snake_case						 =   val

						return orig_state_dict
def 						lowerCamelCase__   (				):
						__snake_case						 =   'http://images.cocodataset.org/val2017/000000039769.jpg'
						__snake_case						 =   Image.open(requests.get(a    ,	stream=a							).raw							)
						return im
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a="groupvit-gcc-yfcc"    ,	a=False							):
						__snake_case						 =   GroupViTConfig()
						__snake_case						 =   GroupViTModel(a							).eval()

						__snake_case						 =   torch.load(a    ,	map_location='cpu'							)['model']
						__snake_case						 =   convert_state_dict(a    ,	a							)
						__snake_case ,       __snake_case						 =   model.load_state_dict(a    ,	strict=a							)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a							) == 0)

						# verify result
						__snake_case						 =   CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'							)
						__snake_case						 =   prepare_img()
						__snake_case						 =   processor(text=['a photo of a cat', 'a photo of a dog']    ,	images=a    ,	padding=a    ,	return_tensors='pt'							)

						with torch.no_grad():
												__snake_case						 =   model(**a							)

						if model_name == "groupvit-gcc-yfcc":
												__snake_case						 =   torch.tensor([[13.3_523, 6.3_629]]							)
						elif model_name == "groupvit-gcc-redcaps":
												__snake_case						 =   torch.tensor([[16.1_873, 8.6_230]]							)
						else:
												raise ValueError(f'Model name {model_name} not supported.'							)
						assert torch.allclose(outputs.logits_per_image    ,	a    ,	atol=1E-3							)

						processor.save_pretrained(a							)
						model.save_pretrained(a							)
						print('Successfully saved processor and model to'    ,	a							)

						if push_to_hub:
												print('Pushing to the hub...'							)
												processor.push_to_hub(a    ,	organization='nielsr'							)
												model.push_to_hub(a    ,	organization='nielsr'							)


if __name__ == "__main__":
	_lowercase  =			argparse.ArgumentParser()
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
	)
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
	parser.add_argument(
	    """--model_name""",
	    default="""groupvit-gccy-fcc""",
	    type=str,
	    help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
	)
	_lowercase  =			parser.parse_args()

	convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

