






"""simple docstring"""



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


A__       :				List[Any] 					=					["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
A__       :				Dict 					=					{"""bart.large""": BartModel, """bart.large.mnli""": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
						raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
A__       :				Dict 					=					logging.get_logger(__name__)

A__       :				List[Any] 					=					""" Hello world! cécé herlolip"""

A__       :				Optional[Any] 					=					[
    ("""model.classification_heads.mnli.dense.weight""", """classification_head.dense.weight"""),
    ("""model.classification_heads.mnli.dense.bias""", """classification_head.dense.bias"""),
    ("""model.classification_heads.mnli.out_proj.weight""", """classification_head.out_proj.weight"""),
    ("""model.classification_heads.mnli.out_proj.bias""", """classification_head.out_proj.bias"""),
]

def  _snake_case				(     lowerCamelCase__	:    Optional[int]  )				->	Union[str, Any]:
							lowerCamelCase_       :							Union[str, Any]									=[
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "_float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase__				,						UpperCamelCase__  )

def  _snake_case				(     lowerCamelCase__	:    Any				,						lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Dict  )				->	List[Any]:
							lowerCamelCase_       :							Tuple									=dct.pop(UpperCamelCase__  )
							lowerCamelCase_       :							Optional[Any]									=val

def  _snake_case				(     lowerCamelCase__	:    Optional[Any]  )				->	Any:
							lowerCamelCase_       :							Tuple									=torch.load(UpperCamelCase__				,						map_location="cpu"  )
							lowerCamelCase_       :							List[str]									=torch.hub.load("pytorch/fairseq"				,						"bart.large.cnn"  ).eval()
							hub_interface.model.load_state_dict(sd["model"]  )
							return hub_interface

def  _snake_case				(     lowerCamelCase__	:    Any  )				->	Tuple:
							lowerCamelCase_							,						lowerCamelCase_       :							Any									=emb.weight.shape
							lowerCamelCase_       :							Optional[int]									=nn.Linear(UpperCamelCase__				,						UpperCamelCase__				,						bias=UpperCamelCase__  )
							lowerCamelCase_       :							Any									=emb.weight.data
							return lin_layer

@torch.no_grad()
def  _snake_case				(     lowerCamelCase__	:    Union[str, Any]				,						lowerCamelCase__	:    List[str]				,						lowerCamelCase__	:    Optional[int]=None  )				->	int:






							if not os.path.exists(UpperCamelCase__  ):
														lowerCamelCase_       :							List[Any]									=torch.hub.load("pytorch/fairseq"				,						UpperCamelCase__  ).eval()
							else:
														lowerCamelCase_       :							Optional[int]									=load_xsum_checkpoint(UpperCamelCase__  )

							bart.model.upgrade_state_dict(bart.model.state_dict()  )
							if hf_checkpoint_name is None:
														lowerCamelCase_       :							Optional[Any]									=checkpoint_path.replace("."				,						"-"  )
							lowerCamelCase_       :							Optional[Any]									=BartConfig.from_pretrained(UpperCamelCase__  )
							lowerCamelCase_       :							Any									=bart.encode(UpperCamelCase__  ).unsqueeze(0  )
							lowerCamelCase_       :							List[Any]									=BartTokenizer.from_pretrained(UpperCamelCase__  ).encode(UpperCamelCase__				,						return_tensors="pt"  ).unsqueeze(0  )
							if not torch.eq(UpperCamelCase__				,						UpperCamelCase__  ).all():
														raise ValueError(
														    F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""  )

							if checkpoint_path == "bart.large.mnli":
														lowerCamelCase_       :							Tuple									=bart.state_dict()
														remove_ignore_keys_(UpperCamelCase__  )
														lowerCamelCase_       :							List[Any]									=state_dict["model.decoder.embed_tokens.weight"]
														for src, dest in mnli_rename_keys:
																					rename_key(UpperCamelCase__				,						UpperCamelCase__				,						UpperCamelCase__  )
														lowerCamelCase_       :							Tuple									=BartForSequenceClassification(UpperCamelCase__  ).eval()
														model.load_state_dict(UpperCamelCase__  )
														lowerCamelCase_       :							Any									=bart.predict("mnli"				,						UpperCamelCase__				,						return_logits=UpperCamelCase__  )
														lowerCamelCase_       :							Tuple									=model(UpperCamelCase__  )[0]  # logits
							else:  # no classification heads to worry about
														lowerCamelCase_       :							Optional[Any]									=bart.model.state_dict()
														remove_ignore_keys_(UpperCamelCase__  )
														lowerCamelCase_       :							Optional[int]									=state_dict["decoder.embed_tokens.weight"]
														lowerCamelCase_       :							Any									=bart.extract_features(UpperCamelCase__  )
														if hf_checkpoint_name == "facebook/bart-large":
																					lowerCamelCase_       :							List[str]									=BartModel(UpperCamelCase__  ).eval()
																					model.load_state_dict(UpperCamelCase__  )
																					lowerCamelCase_       :							Dict									=model(UpperCamelCase__  ).model[0]
														else:
																					lowerCamelCase_       :							List[str]									=BartForConditionalGeneration(UpperCamelCase__  ).eval()  # an existing summarization ckpt
																					model.model.load_state_dict(UpperCamelCase__  )
																					if hasattr(UpperCamelCase__				,						"lm_head"  ):
																												lowerCamelCase_       :							Union[str, Any]									=make_linear_from_emb(model.model.shared  )
																					lowerCamelCase_       :							List[Any]									=model.model(UpperCamelCase__  )[0]

    # Check results
							if fairseq_output.shape != new_model_outputs.shape:
														raise ValueError(
														    F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""  )
							if (fairseq_output != new_model_outputs).any().item():
														raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"  )
							Path(UpperCamelCase__  ).mkdir(exist_ok=UpperCamelCase__  )
							model.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
						A__       :				Union[str, Any] 					=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
						)
						parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
						)
						A__       :				str 					=					parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)






import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    1.5
							snake_case_							=    int(factor * num_class_images      )
							snake_case_							=    ClipClient(
							    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1      )

							os.makedirs(F'''{class_data_dir}/images'''   ,							exist_ok=UpperCamelCase__      )
							if len(list(Path(F'''{class_data_dir}/images'''      ).iterdir()      )      ) >= num_class_images:
														return

							while True:
														snake_case_							=    client.query(text=UpperCamelCase__      )
														if len(UpperCamelCase__      ) >= factor * num_class_images or num_images > 1E4:
																					break
														else:
																					snake_case_							=    int(factor * num_images      )
																					snake_case_							=    ClipClient(
																					    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1   ,							)

							snake_case_							=    0
							snake_case_							=    0
							snake_case_							=    tqdm(desc='downloading real regularization images'   ,							total=UpperCamelCase__      )

							with open(F'''{class_data_dir}/caption.txt'''   ,							'w'      ) as fa, open(F'''{class_data_dir}/urls.txt'''   ,							'w'      ) as fa, open(
							    F'''{class_data_dir}/images.txt'''   ,							'w'      ) as fa:
														while total < num_class_images:
																					snake_case_							=    class_images[count]
																					count += 1
																					try:
																												snake_case_							=    requests.get(images['url']      )
																												if img.status_code == 200:
																																			snake_case_							=    Image.open(BytesIO(img.content      )      )
																																			with open(F'''{class_data_dir}/images/{total}.jpg'''   ,							'wb'      ) as f:
																																										f.write(img.content      )
																																			fa.write(images['caption'] + '\n'      )
																																			fa.write(images['url'] + '\n'      )
																																			fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'      )
																																			total += 1
																																			pbar.update(1      )
																												else:
																																			continue
																					except Exception:
																												continue
							return





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    argparse.ArgumentParser(''   ,							add_help=UpperCamelCase__      )
							parser.add_argument('--class_prompt'   ,							help='text prompt to retrieve images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--class_data_dir'   ,							help='path to save images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--num_class_images'   ,							help='number of images to download'   ,							default=200   ,							type=UpperCamelCase__      )
							return parser.parse_args()


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[int]										=       parse_args()
					retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




