
'''simple docstring'''



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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCamelCase__      :		str       							=  logging.getLogger(__name__)

@dataclass
class    _a				:





							"""simple docstring"""




							SCREAMING_SNAKE_CASE      =  field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
							SCREAMING_SNAKE_CASE      =  field(
							    default=_lowerCamelCase  ,					metadata={'help': 'Pretrained config name or path if not the same as model_name'})
							SCREAMING_SNAKE_CASE      =  field(
							    default=_lowerCamelCase  ,					metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
							SCREAMING_SNAKE_CASE      =  field(
							    default=_lowerCamelCase  ,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,					)
							SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Whether tp freeze the encoder.'})
							SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Whether  to freeze the embeddings.'})






@dataclass
class    _a				:





							"""simple docstring"""




							SCREAMING_SNAKE_CASE      =  field(
							    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
							SCREAMING_SNAKE_CASE      =  field(
							    default='summarization'  ,					metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}  ,					)
							SCREAMING_SNAKE_CASE      =  field(
							    default=10_24  ,					metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }  ,					)
							SCREAMING_SNAKE_CASE      =  field(
							    default=1_28  ,					metadata={
							        'help': (
							            'The maximum total sequence length for target text after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }  ,					)
							SCREAMING_SNAKE_CASE      =  field(
							    default=1_42  ,					metadata={
							        'help': (
							            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded. '
							            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
							            'during ``evaluate`` and ``predict``.'
							        )
							    }  ,					)
							SCREAMING_SNAKE_CASE      =  field(
							    default=1_42  ,					metadata={
							        'help': (
							            'The maximum total sequence length for test target text after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }  ,					)
							SCREAMING_SNAKE_CASE      =  field(default=-1  ,					metadata={'help': '# training examples. -1 means use all.'})
							SCREAMING_SNAKE_CASE      =  field(default=-1  ,					metadata={'help': '# validation examples. -1 means use all.'})
							SCREAMING_SNAKE_CASE      =  field(default=-1  ,					metadata={'help': '# test examples. -1 means use all.'})
							SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Source language id for translation.'})
							SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Target language id for translation.'})
							SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': '# num_beams to use for evaluation.'})
							SCREAMING_SNAKE_CASE      =  field(
							    default=_lowerCamelCase  ,					metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}  ,					)






def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




			"""simple docstring"""
			logger.info(F"***** {split} metrics *****"						)
			for key in sorted(metrics.keys()						):
						logger.info(F"  {key} = {metrics[key]}"						)
			save_json(SCREAMING_SNAKE_CASE_    ,      os.path.join(SCREAMING_SNAKE_CASE_    ,      F"{split}_results.json"						)						)
def    lowerCAmelCase_	(				)     ->     Optional[int]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)						)

			if len(sys.argv						) == 2 and sys.argv[1].endswith(""".json"""						):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
			else:
						_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				parser.parse_args_into_dataclasses()

			check_output_dir(SCREAMING_SNAKE_CASE_						)

			# 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.parallel_mode == ParallelMode.DISTRIBUTED						)    ,      training_args.fpaa    ,      )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()
			# 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()
			logger.info("""Training/evaluation parameters %s"""    ,      SCREAMING_SNAKE_CASE_						)

			# Set seed
			set_seed(training_args.seed						)

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

			_SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir    ,      )

			_SCREAMING_SNAKE_CASE      =				("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
			for p in extra_model_params:
						if getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
									assert hasattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						), F"({config.__class__.__name__}) doesn't have a `{p}` attribute"
									setattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)						)

			_SCREAMING_SNAKE_CASE      =				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    ,      )
			_SCREAMING_SNAKE_CASE      =				AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path    ,      from_tf=""".ckpt""" in model_args.model_name_or_path    ,      config=SCREAMING_SNAKE_CASE_    ,      cache_dir=model_args.cache_dir    ,      )

			# use task specific params
			use_task_specific_params(SCREAMING_SNAKE_CASE_    ,      data_args.task						)

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						_SCREAMING_SNAKE_CASE      =				model.config.num_beams

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(SCREAMING_SNAKE_CASE_    ,      (MBartTokenizer, MBartTokenizerFast)						):
						assert (
						    data_args.tgt_lang is not None and data_args.src_lang is not None
						), "mBart requires --tgt_lang and --src_lang"
						if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
									_SCREAMING_SNAKE_CASE      =				tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									_SCREAMING_SNAKE_CASE      =				tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

			if model_args.freeze_embeds:
						freeze_embeds(SCREAMING_SNAKE_CASE_						)
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder()						)
						assert_all_frozen(model.get_encoder()						)

			_SCREAMING_SNAKE_CASE      =				SeqaSeqDataset

			# Get datasets
			_SCREAMING_SNAKE_CASE      =				(
			    dataset_class(
			        SCREAMING_SNAKE_CASE_    ,      type_path="""train"""    ,      data_dir=data_args.data_dir    ,      n_obs=data_args.n_train    ,      max_target_length=data_args.max_target_length    ,      max_source_length=data_args.max_source_length    ,      prefix=model.config.prefix or """"""    ,      )
			    if training_args.do_train
			    else None
			)
			_SCREAMING_SNAKE_CASE      =				(
			    dataset_class(
			        SCREAMING_SNAKE_CASE_    ,      type_path="""val"""    ,      data_dir=data_args.data_dir    ,      n_obs=data_args.n_val    ,      max_target_length=data_args.val_max_target_length    ,      max_source_length=data_args.max_source_length    ,      prefix=model.config.prefix or """"""    ,      )
			    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
			    else None
			)
			_SCREAMING_SNAKE_CASE      =				(
			    dataset_class(
			        SCREAMING_SNAKE_CASE_    ,      type_path="""test"""    ,      data_dir=data_args.data_dir    ,      n_obs=data_args.n_test    ,      max_target_length=data_args.test_max_target_length    ,      max_source_length=data_args.max_source_length    ,      prefix=model.config.prefix or """"""    ,      )
			    if training_args.do_predict
			    else None
			)

			# Initialize our Trainer
			_SCREAMING_SNAKE_CASE      =				(
			    build_compute_metrics_fn(data_args.task    ,      SCREAMING_SNAKE_CASE_						) if training_args.predict_with_generate else None
			)
			_SCREAMING_SNAKE_CASE      =				SeqaSeqTrainer(
			    model=SCREAMING_SNAKE_CASE_    ,      args=SCREAMING_SNAKE_CASE_    ,      data_args=SCREAMING_SNAKE_CASE_    ,      train_dataset=SCREAMING_SNAKE_CASE_    ,      eval_dataset=SCREAMING_SNAKE_CASE_    ,      data_collator=SeqaSeqDataCollator(
			        SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      model.config.decoder_start_token_id    ,      training_args.tpu_num_cores						)    ,      compute_metrics=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      )

			_SCREAMING_SNAKE_CASE      =				{}
			# Training
			if training_args.do_train:
						logger.info("""*** Train ***"""						)

						_SCREAMING_SNAKE_CASE      =				trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
						_SCREAMING_SNAKE_CASE      =				train_result.metrics
						_SCREAMING_SNAKE_CASE      =				data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics("""train"""    ,      SCREAMING_SNAKE_CASE_    ,      training_args.output_dir						)
									all_metrics.update(SCREAMING_SNAKE_CASE_						)

									# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir    ,      """trainer_state.json"""						)						)

									# For convenience, we also re-save the tokenizer to the same directory,
									# so that you can share your model easily on huggingface.co/models =)
									tokenizer.save_pretrained(training_args.output_dir						)

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

						_SCREAMING_SNAKE_CASE      =				trainer.evaluate(metric_key_prefix="""val"""						)
						_SCREAMING_SNAKE_CASE      =				data_args.n_val
						_SCREAMING_SNAKE_CASE      =				round(metrics["""val_loss"""]    ,      4						)

						if trainer.is_world_process_zero():
									handle_metrics("""val"""    ,      SCREAMING_SNAKE_CASE_    ,      training_args.output_dir						)
									all_metrics.update(SCREAMING_SNAKE_CASE_						)

			if training_args.do_predict:
						logger.info("""*** Predict ***"""						)

						_SCREAMING_SNAKE_CASE      =				trainer.predict(test_dataset=SCREAMING_SNAKE_CASE_    ,      metric_key_prefix="""test"""						)
						_SCREAMING_SNAKE_CASE      =				test_output.metrics
						_SCREAMING_SNAKE_CASE      =				data_args.n_test

						if trainer.is_world_process_zero():
									_SCREAMING_SNAKE_CASE      =				round(metrics["""test_loss"""]    ,      4						)
									handle_metrics("""test"""    ,      SCREAMING_SNAKE_CASE_    ,      training_args.output_dir						)
									all_metrics.update(SCREAMING_SNAKE_CASE_						)

									if training_args.predict_with_generate:
												_SCREAMING_SNAKE_CASE      =				tokenizer.batch_decode(
												    test_output.predictions    ,      skip_special_tokens=SCREAMING_SNAKE_CASE_    ,      clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_						)
												_SCREAMING_SNAKE_CASE      =				lmap(str.strip    ,      SCREAMING_SNAKE_CASE_						)
												write_txt_file(SCREAMING_SNAKE_CASE_    ,      os.path.join(training_args.output_dir    ,      """test_generations.txt"""						)						)

			if trainer.is_world_process_zero():
						save_json(SCREAMING_SNAKE_CASE_    ,      os.path.join(training_args.output_dir    ,      """all_results.json"""						)						)

			return all_metrics
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[str]:




			"""simple docstring"""
			main()


if __name__ == "__main__":
					main()








'''simple docstring'''



import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				XCLIPTextConfig()

   # derive patch size from model name
   _SCREAMING_SNAKE_CASE      =				model_name.find("""patch"""						)
   _SCREAMING_SNAKE_CASE      =				int(model_name[start_idx + len("""patch"""						) : start_idx + len("""patch"""						) + 2]						)
   _SCREAMING_SNAKE_CASE      =				XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_    ,      num_frames=SCREAMING_SNAKE_CASE_						)

   if "large" in model_name:
      _SCREAMING_SNAKE_CASE      =				7_68
      _SCREAMING_SNAKE_CASE      =				30_72
      _SCREAMING_SNAKE_CASE      =				12

      _SCREAMING_SNAKE_CASE      =				10_24
      _SCREAMING_SNAKE_CASE      =				40_96
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				24
      _SCREAMING_SNAKE_CASE      =				7_68
      _SCREAMING_SNAKE_CASE      =				30_72

   if model_name == "xclip-large-patch14-16-frames":
      _SCREAMING_SNAKE_CASE      =				3_36

   _SCREAMING_SNAKE_CASE      =				XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   if "large" in model_name:
      _SCREAMING_SNAKE_CASE      =				7_68

   return config
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   # text encoder
   if name == "token_embedding.weight":
      _SCREAMING_SNAKE_CASE      =				name.replace("""token_embedding.weight"""    ,      """text_model.embeddings.token_embedding.weight"""						)
   if name == "positional_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""positional_embedding"""    ,      """text_model.embeddings.position_embedding.weight"""						)
   if "ln_1" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""ln_1"""    ,      """layer_norm1"""						)
   if "ln_2" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""ln_2"""    ,      """layer_norm2"""						)
   if "c_fc" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""c_fc"""    ,      """fc1"""						)
   if "c_proj" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""c_proj"""    ,      """fc2"""						)
   if name.startswith("""transformer.resblocks"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""transformer.resblocks"""    ,      """text_model.encoder.layers"""						)
   if "attn.out_proj" in name and "message" not in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""attn.out_proj"""    ,      """self_attn.out_proj"""						)
   if "ln_final" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""ln_final"""    ,      """text_model.final_layer_norm"""						)
   # visual encoder
   if name == "visual.class_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.class_embedding"""    ,      """vision_model.embeddings.class_embedding"""						)
   if name == "visual.positional_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.positional_embedding"""    ,      """vision_model.embeddings.position_embedding.weight"""						)
   if name.startswith("""visual.transformer.resblocks"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.transformer.resblocks"""    ,      """vision_model.encoder.layers"""						)
   if "visual.conv1" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.conv1"""    ,      """vision_model.embeddings.patch_embedding"""						)
   if "visual.ln_pre" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.ln_pre"""    ,      """vision_model.pre_layernorm"""						)
   if "visual.ln_post" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.ln_post"""    ,      """vision_model.post_layernorm"""						)
   if "visual.proj" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.proj"""    ,      """visual_projection.weight"""						)
   if "text_projection" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""text_projection"""    ,      """text_projection.weight"""						)
   # things on top
   if "prompts_visual_proj" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""prompts_visual_proj"""    ,      """prompts_visual_projection"""						)
   if "prompts_visual_ln" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""prompts_visual_ln"""    ,      """prompts_visual_layernorm"""						)
   # mit
   if name == "mit.positional_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""positional"""    ,      """position"""						)
   if name.startswith("""mit.resblocks"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""mit.resblocks"""    ,      """mit.encoder.layers"""						)
   # prompts generator
   if name.startswith("""prompts_generator.norm"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""prompts_generator.norm"""    ,      """prompts_generator.layernorm"""						)

   return name
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      _SCREAMING_SNAKE_CASE      =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_						)

      if "attn.in_proj" in key:
         _SCREAMING_SNAKE_CASE      =				key.split("""."""						)
         if key.startswith("""visual"""						):
            _SCREAMING_SNAKE_CASE      =				key_split[3]
            _SCREAMING_SNAKE_CASE      =				config.vision_config.hidden_size
            if "message_attn" in key:
               if "weight" in key:
                  _SCREAMING_SNAKE_CASE      =				val[
                      :dim, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      -dim:, :
                  ]
               else:
                  _SCREAMING_SNAKE_CASE      =				val[
                      :dim
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      -dim:
                  ]
            else:
               if "weight" in key:
                  _SCREAMING_SNAKE_CASE      =				val[
                      :dim, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      -dim:, :
                  ]
               else:
                  _SCREAMING_SNAKE_CASE      =				val[:dim]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[-dim:]
         elif key.startswith("""mit"""						):
            _SCREAMING_SNAKE_CASE      =				key_split[2]
            _SCREAMING_SNAKE_CASE      =				config.vision_config.mit_hidden_size
            if "weight" in key:
               _SCREAMING_SNAKE_CASE      =				val[:dim, :]
               _SCREAMING_SNAKE_CASE      =				val[dim : dim * 2, :]
               _SCREAMING_SNAKE_CASE      =				val[-dim:, :]
            else:
               _SCREAMING_SNAKE_CASE      =				val[:dim]
               _SCREAMING_SNAKE_CASE      =				val[dim : dim * 2]
               _SCREAMING_SNAKE_CASE      =				val[-dim:]
         else:
            _SCREAMING_SNAKE_CASE      =				key_split[2]
            _SCREAMING_SNAKE_CASE      =				config.text_config.hidden_size
            if "weight" in key:
               _SCREAMING_SNAKE_CASE      =				val[:dim, :]
               _SCREAMING_SNAKE_CASE      =				val[
                   dim : dim * 2, :
               ]
               _SCREAMING_SNAKE_CASE      =				val[-dim:, :]
            else:
               _SCREAMING_SNAKE_CASE      =				val[:dim]
               _SCREAMING_SNAKE_CASE      =				val[
                   dim : dim * 2
               ]
               _SCREAMING_SNAKE_CASE      =				val[-dim:]
      else:
         _SCREAMING_SNAKE_CASE      =				rename_key(SCREAMING_SNAKE_CASE_						)
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            _SCREAMING_SNAKE_CASE      =				val.T
         _SCREAMING_SNAKE_CASE      =				val

   return orig_state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if num_frames == 8:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti_8_frames.npy"""
   elif num_frames == 16:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti.npy"""
   elif num_frames == 32:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti_32_frames.npy"""
   _SCREAMING_SNAKE_CASE      =				hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""    ,      filename=SCREAMING_SNAKE_CASE_    ,      repo_type="""dataset"""    ,      )
   _SCREAMING_SNAKE_CASE      =				np.load(SCREAMING_SNAKE_CASE_						)
   return list(SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				{
       # fully supervised kinetics-400 checkpoints
       """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
       """xclip-base-patch32-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
       ),
       """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
       """xclip-base-patch16-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
       ),
       """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
       """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
       # fully supervised kinetics-600 checkpoints
       """xclip-base-patch16-kinetics-600""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
       ),
       """xclip-base-patch16-kinetics-600-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
       ),
       """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
       # few shot
       """xclip-base-patch16-hmdb-2-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
       ),
       """xclip-base-patch16-hmdb-4-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
       ),
       """xclip-base-patch16-hmdb-8-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
       ),
       """xclip-base-patch16-hmdb-16-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
       ),
       """xclip-base-patch16-ucf-2-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
       ),
       """xclip-base-patch16-ucf-4-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
       ),
       """xclip-base-patch16-ucf-8-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
       ),
       """xclip-base-patch16-ucf-16-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
       ),
       # zero shot
       """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
   }

   _SCREAMING_SNAKE_CASE      =				model_to_url[model_name]
   _SCREAMING_SNAKE_CASE      =				8
   if "16-frames" in model_name:
      _SCREAMING_SNAKE_CASE      =				16
   elif "shot" in model_name:
      _SCREAMING_SNAKE_CASE      =				32

   _SCREAMING_SNAKE_CASE      =				get_xclip_config(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				XCLIPModel(SCREAMING_SNAKE_CASE_						)
   model.eval()

   if "drive" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				"""pytorch_model.bin"""
      gdown.cached_download(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      quiet=SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)["""model"""]
   else:
      _SCREAMING_SNAKE_CASE      =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_						)["""model"""]

   _SCREAMING_SNAKE_CASE      =				convert_state_dict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				XCLIPModel(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   _SCREAMING_SNAKE_CASE      =				3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
   _SCREAMING_SNAKE_CASE      =				VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""						)
   _SCREAMING_SNAKE_CASE      =				CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""						)
   _SCREAMING_SNAKE_CASE      =				XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				prepare_video(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				processor(
       text=["""playing sports""", """eating spaghetti""", """go shopping"""]    ,      videos=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      padding=SCREAMING_SNAKE_CASE_						)

   print("""Shape of pixel values:"""    ,      inputs.pixel_values.shape						)

   with torch.no_grad():
      _SCREAMING_SNAKE_CASE      =				model(**SCREAMING_SNAKE_CASE_						)

   # Verify outputs
   _SCREAMING_SNAKE_CASE      =				outputs.logits_per_video
   _SCREAMING_SNAKE_CASE      =				logits_per_video.softmax(dim=1						)
   print("""Probs:"""    ,      SCREAMING_SNAKE_CASE_						)
   # kinetics-400
   if model_name == "xclip-base-patch32":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0019, 0.9951, 0.0030]]						)
   elif model_name == "xclip-base-patch32-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]						)
   elif model_name == "xclip-base-patch16":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0083, 0.9681, 0.0236]]						)
   elif model_name == "xclip-base-patch16-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]						)
   elif model_name == "xclip-large-patch14":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0062, 0.9864, 0.0075]]						)
   elif model_name == "xclip-large-patch14-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]						)
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0555, 0.8914, 0.0531]]						)
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]						)
   elif model_name == "xclip-large-patch14-kinetics-600":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0036, 0.9920, 0.0045]]						)
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0027, 0.9904, 0.0070]]						)
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]						)
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]						)
   else:
      raise ValueError(F"Model name {model_name} not supported"						)
   assert torch.allclose(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      atol=1e-3						)
   print("""Looks ok!"""						)

   if pytorch_dump_folder_path is not None:
      print(F"Saving model {model_name} to {pytorch_dump_folder_path}"						)
      model.save_pretrained(SCREAMING_SNAKE_CASE_						)

   if push_to_hub:
      print("""Pushing model, processor and slow tokenizer files to the hub..."""						)
      model.push_to_hub(SCREAMING_SNAKE_CASE_    ,      organization="""nielsr"""						)
      processor.push_to_hub(SCREAMING_SNAKE_CASE_    ,      organization="""nielsr"""						)
      slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_    ,      organization="""nielsr"""						)


if __name__ == "__main__":
     UpperCamelCase__      :		Union[str, Any]       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="xclip-base-patch32",
         type=str,
         help="Name of the model.",
     )
     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__      :		str       							=  parser.parse_args()
     convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






