



'''simple docstring'''



import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
							raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
lowerCAmelCase_     :       List[str]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       int								=						'Hello world! cécé herlolip'
def 					lowerCAmelCase_   (						A  :				str					,		A  :				str					,		A  :				bool				):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			FairseqRobertaModel.from_pretrained(A				)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,		hidden_size=roberta.cfg.model.encoder_embed_dim					,		num_hidden_layers=roberta.cfg.model.encoder_layers					,		num_attention_heads=roberta.cfg.model.encoder_attention_heads					,		intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim					,		max_position_embeddings=514					,		type_vocab_size=1					,		layer_norm_eps=1e-5					,		)
							if classification_head:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

							print('''Our RoBERTa config:'''					,		A				)

							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			XLMRobertaXLForSequenceClassification(A				) if classification_head else XLMRobertaXLForMaskedLM(A				)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			roberta_sent_encoder.embed_tokens.weight
							SCREAMING_SNAKE_CASE					:      str     					=			roberta_sent_encoder.embed_positions.weight
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.

							SCREAMING_SNAKE_CASE					:      List[Any]     					=			roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_sent_encoder.layer_norm.bias

							for i in range(config.num_hidden_layers				):
														# Encoder: start of layer
														SCREAMING_SNAKE_CASE					:      BertLayer     					=			model.roberta.encoder.layer[i]
														SCREAMING_SNAKE_CASE					:      TransformerSentenceEncoderLayer     					=			roberta_sent_encoder.layers[i]

														SCREAMING_SNAKE_CASE					:      RobertaAttention     					=			layer.attention
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE					:      BertSelfAttention     					=			layer.attention.self
														assert (
														    roberta_layer.self_attn.k_proj.weight.data.shape
														    == roberta_layer.self_attn.q_proj.weight.data.shape
														    == roberta_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)				)
														)

														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE					:      int     					=			roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE					:      Dict     					=			roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE					:      int     					=			roberta_layer.self_attn.v_proj.bias

														# self-attention output
														SCREAMING_SNAKE_CASE					:      BertSelfOutput     					=			layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE					:      BertIntermediate     					=			layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE					:      BertOutput     					=			layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_layer.fca.bias
														# end of layer

							if classification_head:
														SCREAMING_SNAKE_CASE					:      Any     					=			roberta.model.classification_heads['''mnli'''].dense.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			roberta.model.classification_heads['''mnli'''].dense.bias
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			roberta.model.classification_heads['''mnli'''].out_proj.weight
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta.model.classification_heads['''mnli'''].out_proj.bias
							else:
														# LM Head
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta.model.encoder.lm_head.dense.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta.model.encoder.lm_head.dense.bias
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta.model.encoder.lm_head.layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			roberta.model.encoder.lm_head.layer_norm.bias
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta.model.encoder.lm_head.weight
														SCREAMING_SNAKE_CASE					:      int     					=			roberta.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							SCREAMING_SNAKE_CASE					:      torch.Tensor     					=			roberta.encode(A				).unsqueeze(0				)  # batch of size 1

							SCREAMING_SNAKE_CASE					:      str     					=			model(A				)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE					:      Any     					=			roberta.model.classification_heads['''mnli'''](roberta.extract_features(A				)				)
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			roberta.model(A				)[0]
							print(our_output.shape					,		their_output.shape				)
							SCREAMING_SNAKE_CASE					:      Any     					=			torch.max(torch.abs(our_output - their_output				)				).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
							SCREAMING_SNAKE_CASE					:      int     					=			torch.allclose(A					,		A					,		atol=1e-3				)
							print('''Do both models output the same tensors?'''					,		'''🔥''' if success else '''💩'''				)
							if not success:
														raise Exception('''Something went wRoNg'''				)

							pathlib.Path(A				).mkdir(parents=A					,		exist_ok=A				)
							print(F"""Saving model to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(A				)


if __name__ == "__main__":
							lowerCAmelCase_     :       int								=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
							)
							lowerCAmelCase_     :       int								=						parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)









'''simple docstring'''



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 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Optional[int]					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1.5
							SCREAMING_SNAKE_CASE					:      str     					=			int(factor * num_class_images				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ClipClient(
							    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1				)

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

							while True:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			client.query(text=A				)
														if len(A				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(factor * num_images				)
																					SCREAMING_SNAKE_CASE					:      str     					=			ClipClient(
																					    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1					,		)

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
							SCREAMING_SNAKE_CASE					:      Tuple     					=			tqdm(desc='''downloading real regularization images'''					,		total=A				)

							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:
																					SCREAMING_SNAKE_CASE					:      int     					=			class_images[count]
																					count += 1
																					try:
																												SCREAMING_SNAKE_CASE					:      int     					=			requests.get(images['''url''']				)
																												if img.status_code == 200:
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			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 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			argparse.ArgumentParser(''''''					,		add_help=A				)
							parser.add_argument('''--class_prompt'''					,		help='''text prompt to retrieve images'''					,		required=A					,		type=A				)
							parser.add_argument('''--class_data_dir'''					,		help='''path to save images'''					,		required=A					,		type=A				)
							parser.add_argument('''--num_class_images'''					,		help='''number of images to download'''					,		default=200					,		type=A				)
							return parser.parse_args()


if __name__ == "__main__":
							lowerCAmelCase_     :       List[str]								=						parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




