



"""simple docstring"""
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				if "token" in model_name_or_path:
								return "rag_token"
				if "sequence" in model_name_or_path:
								return "rag_sequence"
				if "bart" in model_name_or_path:
								return "bart"
				return None



def 				lowerCAmelCase_(      lowercase_   :							int  ,      lowercase_   :							List[Any]  ,      lowercase_   :							Dict							)		->				Union[str, Any]:
				return max(metric_fn(lowercase_  ,      lowercase_							) for gt in ground_truths							)



def 				lowerCAmelCase_(      lowercase_   :							List[Any]  ,      lowercase_   :							List[str]  ,      lowercase_   :							Optional[int]							)		->				List[Any]:
				_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]
				_lowerCamelCase					     = []

				if args.gold_data_mode == "qa":
								_lowerCamelCase					     = pd.read_csv(lowercase_  ,      sep='''\t'''  ,      header=lowercase_							)
								for answer_list in data[1]:
												_lowerCamelCase					     = ast.literal_eval(lowercase_							)
												answers.append(lowercase_							)
				else:
								_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]
								_lowerCamelCase					     = [[reference] for reference in references]

				_lowerCamelCase					     = _lowerCamelCase					     = _lowerCamelCase					     = 0
				for prediction, ground_truths in zip(lowercase_  ,      lowercase_							):
								total += 1
								em += metric_max_over_ground_truths(lowercase_  ,      lowercase_  ,      lowercase_							)
								fa += metric_max_over_ground_truths(lowercase_  ,      lowercase_  ,      lowercase_							)

				_lowerCamelCase					     = 1_0_0.0 * em / total
				_lowerCamelCase					     = 1_0_0.0 * fa / total

				logger.info(F"""F1: {fa:.2f}"""							)
				logger.info(F"""EM: {em:.2f}"""							)



def 				lowerCAmelCase_(      lowercase_   :							int  ,      lowercase_   :							Optional[int]  ,      lowercase_   :							Optional[Any]							)		->				Optional[int]:
				_lowerCamelCase					     = args.k
				_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]
				_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]

				_lowerCamelCase					     = _lowerCamelCase					     = 0
				for hypo, reference in zip(lowercase_  ,      lowercase_							):
								_lowerCamelCase					     = set(hypo.split('''\t'''							)[:k]							)
								_lowerCamelCase					     = set(reference.split('''\t'''							)							)
								total += 1
								em += len(hypo_provenance & ref_provenance							) / k

				_lowerCamelCase					     = 1_0_0.0 * em / total
				logger.info(F"""Precision@{k}: {em: .2f}"""							)



def 				lowerCAmelCase_(      lowercase_   :							Union[str, Any]  ,      lowercase_   :							Tuple  ,      lowercase_   :							Dict							)		->				Any:
				def strip_title(lowercase_   :							str							):
								if title.startswith('''"'''							):
												_lowerCamelCase					     = title[1:]
								if title.endswith('''"'''							):
												_lowerCamelCase					     = title[:-1]
								return title

				_lowerCamelCase					     = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowercase_  ,      return_tensors='''pt'''  ,      padding=lowercase_  ,      truncation=lowercase_  ,      )['''input_ids'''].to(args.device							)

				_lowerCamelCase					     = rag_model.rag.question_encoder(lowercase_							)
				_lowerCamelCase					     = question_enc_outputs[0]

				_lowerCamelCase					     = rag_model.retriever(
				    lowercase_  ,      question_enc_pool_output.cpu().detach().to(torch.floataa							).numpy()  ,      prefix=rag_model.rag.generator.config.prefix  ,      n_docs=rag_model.config.n_docs  ,      return_tensors='''pt'''  ,      )
				_lowerCamelCase					     = rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
				_lowerCamelCase					     = []
				for docs in all_docs:
								_lowerCamelCase					     = [strip_title(lowercase_							) for title in docs['''title''']]
								provenance_strings.append('''\t'''.join(lowercase_							)							)
				return provenance_strings



def 				lowerCAmelCase_(      lowercase_   :							Tuple  ,      lowercase_   :							Optional[Any]  ,      lowercase_   :							str							)		->				Tuple:
				with torch.no_grad():
								_lowerCamelCase					     = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowercase_  ,      return_tensors='''pt'''  ,      padding=lowercase_  ,      truncation=lowercase_							)

								_lowerCamelCase					     = inputs_dict.input_ids.to(args.device							)
								_lowerCamelCase					     = inputs_dict.attention_mask.to(args.device							)
								_lowerCamelCase					     = rag_model.generate(  # rag_model overwrites generate
								    lowercase_  ,      attention_mask=lowercase_  ,      num_beams=args.num_beams  ,      min_length=args.min_length  ,      max_length=args.max_length  ,      early_stopping=lowercase_  ,      num_return_sequences=1  ,      bad_words_ids=[[0, 0]]  ,      )
								_lowerCamelCase					     = rag_model.retriever.generator_tokenizer.batch_decode(lowercase_  ,      skip_special_tokens=lowercase_							)

								if args.print_predictions:
												for q, a in zip(lowercase_  ,      lowercase_							):
																logger.info('''Q: {} - A: {}'''.format(lowercase_  ,      lowercase_							)							)

								return answers



def 				lowerCAmelCase_(      )		->				int:
				_lowerCamelCase					     = argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_type'''  ,      choices=['''rag_sequence''', '''rag_token''', '''bart''']  ,      type=lowercase_  ,      help=(
				        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
				        ''' model_name_or_path'''
				    )  ,      )
				parser.add_argument(
				    '''--index_name'''  ,      default=lowercase_  ,      choices=['''exact''', '''compressed''', '''legacy''']  ,      type=lowercase_  ,      help='''RAG model retriever type'''  ,      )
				parser.add_argument(
				    '''--index_path'''  ,      default=lowercase_  ,      type=lowercase_  ,      help='''Path to the retrieval index'''  ,      )
				parser.add_argument('''--n_docs'''  ,      default=5  ,      type=lowercase_  ,      help='''Number of retrieved docs'''							)
				parser.add_argument(
				    '''--model_name_or_path'''  ,      default=lowercase_  ,      type=lowercase_  ,      required=lowercase_  ,      help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''  ,      )
				parser.add_argument(
				    '''--eval_mode'''  ,      choices=['''e2e''', '''retrieval''']  ,      default='''e2e'''  ,      type=lowercase_  ,      help=(
				        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
				        ''' precision@k.'''
				    )  ,      )
				parser.add_argument('''--k'''  ,      default=1  ,      type=lowercase_  ,      help='''k for the precision@k calculation'''							)
				parser.add_argument(
				    '''--evaluation_set'''  ,      default=lowercase_  ,      type=lowercase_  ,      required=lowercase_  ,      help='''Path to a file containing evaluation samples'''  ,      )
				parser.add_argument(
				    '''--gold_data_path'''  ,      default=lowercase_  ,      type=lowercase_  ,      required=lowercase_  ,      help='''Path to a tab-separated file with gold samples'''  ,      )
				parser.add_argument(
				    '''--gold_data_mode'''  ,      default='''qa'''  ,      type=lowercase_  ,      choices=['''qa''', '''ans''']  ,      help=(
				        '''Format of the gold data file'''
				        '''qa - a single line in the following format: question [tab] answer_list'''
				        '''ans - a single line of the gold file contains the expected answer string'''
				    )  ,      )
				parser.add_argument(
				    '''--predictions_path'''  ,      type=lowercase_  ,      default='''predictions.txt'''  ,      help='''Name of the predictions file, to be stored in the checkpoints directory'''  ,      )
				parser.add_argument(
				    '''--eval_all_checkpoints'''  ,      action='''store_true'''  ,      help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''  ,      )
				parser.add_argument(
				    '''--eval_batch_size'''  ,      default=8  ,      type=lowercase_  ,      help='''Batch size per GPU/CPU for evaluation.'''  ,      )
				parser.add_argument(
				    '''--recalculate'''  ,      help='''Recalculate predictions even if the prediction file exists'''  ,      action='''store_true'''  ,      )
				parser.add_argument(
				    '''--num_beams'''  ,      default=4  ,      type=lowercase_  ,      help='''Number of beams to be used when generating answers'''  ,      )
				parser.add_argument('''--min_length'''  ,      default=1  ,      type=lowercase_  ,      help='''Min length of the generated answers'''							)
				parser.add_argument('''--max_length'''  ,      default=50  ,      type=lowercase_  ,      help='''Max length of the generated answers'''							)

				parser.add_argument(
				    '''--print_predictions'''  ,      action='''store_true'''  ,      help='''If True, prints predictions while evaluating.'''  ,      )
				parser.add_argument(
				    '''--print_docs'''  ,      action='''store_true'''  ,      help='''If True, prints docs retried while generating.'''  ,      )
				_lowerCamelCase					     = parser.parse_args()
				_lowerCamelCase					     = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)
				return args



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				int:
				_lowerCamelCase					     = {}
				if args.model_type is None:
								_lowerCamelCase					     = infer_model_type(args.model_name_or_path							)
								assert args.model_type is not None
				if args.model_type.startswith('''rag'''							):
								_lowerCamelCase					     = RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
								_lowerCamelCase					     = args.n_docs
								if args.index_name is not None:
												_lowerCamelCase					     = args.index_name
								if args.index_path is not None:
												_lowerCamelCase					     = args.index_path
				else:
								_lowerCamelCase					     = BartForConditionalGeneration

				_lowerCamelCase					     = (
				    [f.path for f in os.scandir(args.model_name_or_path							) if f.is_dir()]
				    if args.eval_all_checkpoints
				    else [args.model_name_or_path]
				)

				logger.info('''Evaluate the following checkpoints: %s'''  ,      lowercase_							)

				_lowerCamelCase					     = get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
				_lowerCamelCase					     = evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

				for checkpoint in checkpoints:
								if os.path.exists(args.predictions_path							) and (not args.recalculate):
												logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path							)							)
												score_fn(lowercase_  ,      args.predictions_path  ,      args.gold_data_path							)
												continue

								logger.info('''***** Running evaluation for {} *****'''.format(lowercase_							)							)
								logger.info('''  Batch size = %d'''  ,      args.eval_batch_size							)
								logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path							)							)

								if args.model_type.startswith('''rag'''							):
												_lowerCamelCase					     = RagRetriever.from_pretrained(lowercase_  ,      **lowercase_							)
												_lowerCamelCase					     = model_class.from_pretrained(lowercase_  ,      retriever=lowercase_  ,      **lowercase_							)
												model.retriever.init_retrieval()
								else:
												_lowerCamelCase					     = model_class.from_pretrained(lowercase_  ,      **lowercase_							)
								model.to(args.device							)

								with open(args.evaluation_set  ,      '''r'''							) as eval_file, open(args.predictions_path  ,      '''w'''							) as preds_file:
												_lowerCamelCase					     = []
												for line in tqdm(lowercase_							):
																questions.append(line.strip()							)
																if len(lowercase_							) == args.eval_batch_size:
																				_lowerCamelCase					     = evaluate_batch_fn(lowercase_  ,      lowercase_  ,      lowercase_							)
																				preds_file.write('''\n'''.join(lowercase_							) + '''\n'''							)
																				preds_file.flush()
																				_lowerCamelCase					     = []
												if len(lowercase_							) > 0:
																_lowerCamelCase					     = evaluate_batch_fn(lowercase_  ,      lowercase_  ,      lowercase_							)
																preds_file.write('''\n'''.join(lowercase_							)							)
																preds_file.flush()

												score_fn(lowercase_  ,      args.predictions_path  ,      args.gold_data_path							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  str										=					get_args()
			main(args)

'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__     :			Optional[Any]      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]   ):



     """simple docstring"""





     UpperCAmelCase_							:   Dict   =      ConvNextConfig()

     if "tiny" in checkpoint_url:
          UpperCAmelCase_							:   Any   =      [3, 3, 9, 3]
          UpperCAmelCase_							:   Any   =      [96, 192, 384, 768]
     if "small" in checkpoint_url:
          UpperCAmelCase_							:   int   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Optional[int]   =      [96, 192, 384, 768]
     if "base" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Dict   =      [128, 256, 512, 1024]
     if "large" in checkpoint_url:
          UpperCAmelCase_							:   Optional[Any]   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   List[Any]   =      [192, 384, 768, 1536]
     if "xlarge" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Any   =      [256, 512, 1024, 2048]

     if "1k" in checkpoint_url:
          UpperCAmelCase_							:   int   =      1000
          UpperCAmelCase_							:   Any   =      'imagenet-1k-id2label.json'
          UpperCAmelCase_							:   str   =      (1, 1000)
     else:
          UpperCAmelCase_							:   Dict   =      21841
          UpperCAmelCase_							:   Optional[Any]   =      'imagenet-22k-id2label.json'
          UpperCAmelCase_							:   Optional[Any]   =      (1, 21841)

     UpperCAmelCase_							:   int   =      'huggingface/label-files'
     UpperCAmelCase_							:   Any   =      num_labels
     UpperCAmelCase_							:   Dict   =      json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='dataset'   )	,     'r'   )   )
     UpperCAmelCase_							:   List[Any]   =      {int(lowerCamelCase_   ): v for k, v in idalabel.items()}
     if "1k" not in checkpoint_url:
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[15027]
     UpperCAmelCase_							:   Optional[Any]   =      idalabel
     UpperCAmelCase_							:   Dict   =      {v: k for k, v in idalabel.items()}
     UpperCAmelCase_							:   Tuple   =      hidden_sizes
     UpperCAmelCase_							:   List[str]   =      depths

     return config, expected_shape


def 					_lowerCamelCase    (      lowerCamelCase_					:							int   ):



     """simple docstring"""





     if "downsample_layers.0.0" in name:
          UpperCAmelCase_							:   List[Any]   =      name.replace('downsample_layers.0.0'	,     'embeddings.patch_embeddings'   )
     if "downsample_layers.0.1" in name:
          UpperCAmelCase_							:   List[Any]   =      name.replace('downsample_layers.0.1'	,     'embeddings.norm'   )  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          UpperCAmelCase_							:   int   =      name.replace('downsample_layers.1.0'	,     'stages.1.downsampling_layer.0'   )
     if "downsample_layers.1.1" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('downsample_layers.1.1'	,     'stages.1.downsampling_layer.1'   )
     if "downsample_layers.2.0" in name:
          UpperCAmelCase_							:   str   =      name.replace('downsample_layers.2.0'	,     'stages.2.downsampling_layer.0'   )
     if "downsample_layers.2.1" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('downsample_layers.2.1'	,     'stages.2.downsampling_layer.1'   )
     if "downsample_layers.3.0" in name:
          UpperCAmelCase_							:   Optional[int]   =      name.replace('downsample_layers.3.0'	,     'stages.3.downsampling_layer.0'   )
     if "downsample_layers.3.1" in name:
          UpperCAmelCase_							:   Dict   =      name.replace('downsample_layers.3.1'	,     'stages.3.downsampling_layer.1'   )
     if "stages" in name and "downsampling_layer" not in name:
          # stages.0.0. for instance should be renamed to stages.0.layers.0.
          UpperCAmelCase_							:   str   =      name[: len('stages.0'   )] + '.layers' + name[len('stages.0'   ) :]
     if "stages" in name:
          UpperCAmelCase_							:   int   =      name.replace('stages'	,     'encoder.stages'   )
     if "norm" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('norm'	,     'layernorm'   )
     if "gamma" in name:
          UpperCAmelCase_							:   Any   =      name.replace('gamma'	,     'layer_scale_parameter'   )
     if "head" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('head'	,     'classifier'   )

     return name


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[str]   =      'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_							:   Optional[int]   =      Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_   ).raw   )
     return im


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							int	,     lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[Any]   =      get_convnext_config(lowerCamelCase_   )
     # load original state_dict from URL
     UpperCAmelCase_							:   Dict   =      torch.hub.load_state_dict_from_url(lowerCamelCase_   )['model']
     # rename keys
     for key in state_dict.copy().keys():
          UpperCAmelCase_							:   Optional[int]   =      state_dict.pop(lowerCamelCase_   )
          UpperCAmelCase_							:   Tuple   =      val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          UpperCAmelCase_							:   int   =      state_dict.pop(lowerCamelCase_   )
          if not key.startswith('classifier'   ):
               UpperCAmelCase_							:   int   =      'convnext.' + key
          UpperCAmelCase_							:   Tuple   =      val

     # load HuggingFace model
     UpperCAmelCase_							:   Tuple   =      ConvNextForImageClassification(lowerCamelCase_   )
     model.load_state_dict(lowerCamelCase_   )
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     UpperCAmelCase_							:   Tuple   =      224 if '224' in checkpoint_url else 384
     UpperCAmelCase_							:   Optional[Any]   =      ConvNextImageProcessor(size=lowerCamelCase_   )
     UpperCAmelCase_							:   Tuple   =      image_processor(images=prepare_img()	,     return_tensors='pt'   ).pixel_values

     UpperCAmelCase_							:   Dict   =      model(lowerCamelCase_   ).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          UpperCAmelCase_							:   Tuple   =      torch.tensor([-0.1210, -0.6605, 0.1918]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          UpperCAmelCase_							:   str   =      torch.tensor([-0.4473, -0.1847, -0.6365]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          UpperCAmelCase_							:   Tuple   =      torch.tensor([0.4525, 0.7539, 0.0308]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          UpperCAmelCase_							:   List[Any]   =      torch.tensor([0.3561, 0.6350, -0.0384]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          UpperCAmelCase_							:   List[str]   =      torch.tensor([0.4174, -0.0989, 0.1489]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          UpperCAmelCase_							:   Any   =      torch.tensor([0.2513, -0.1349, -0.1613]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          UpperCAmelCase_							:   Dict   =      torch.tensor([1.2980, 0.3631, -0.1198]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([1.2963, 0.1227, 0.1723]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          UpperCAmelCase_							:   Optional[Any]   =      torch.tensor([1.7956, 0.8390, 0.2820]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          UpperCAmelCase_							:   Tuple   =      torch.tensor([-0.2822, -0.0502, -0.0878]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([-0.5672, -0.0730, -0.4348]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          UpperCAmelCase_							:   Optional[Any]   =      torch.tensor([0.2681, 0.2365, 0.6246]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          UpperCAmelCase_							:   Optional[int]   =      torch.tensor([-0.2642, 0.3931, 0.5116]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([-0.6677, -0.1873, -0.8379]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          UpperCAmelCase_							:   str   =      torch.tensor([-0.7749, -0.2967, -0.6444]   )
     else:
          raise ValueError(F'''Unknown URL: {checkpoint_url}'''   )

     assert torch.allclose(logits[0, :3]	,     lowerCamelCase_	,     atol=1e-3   )
     assert logits.shape == expected_shape

     Path(lowerCamelCase_   ).mkdir(exist_ok=lowerCamelCase_   )
     print(F'''Saving model to {pytorch_dump_folder_path}'''   )
     model.save_pretrained(lowerCamelCase_   )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
     image_processor.save_pretrained(lowerCamelCase_   )

     print('Pushing model to the hub...'   )
     UpperCAmelCase_							:   Union[str, Any]   =      'convnext'
     if "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "small" in checkpoint_url:
          model_name += "-small"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
     elif "large" in checkpoint_url:
          model_name += "-large"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"

     model.push_to_hub(
         repo_path_or_name=Path(lowerCamelCase_	,     lowerCamelCase_   )	,     organization='nielsr'	,     commit_message='Add model'	,     )


if __name__ == "__main__":
 snake_case__     :			List[Any]      			=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
     type=str,
     help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the output PyTorch model directory.''',
 )

 snake_case__     :			int      			=  parser.parse_args()
 convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






