




import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

lowerCamelCase    :		Any      =       {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any] = "dhaka"     ,							lowercase     :	Optional[Any] = 5					):


      '''simple docstring'''



      lowerCamelCase_							=   min(lowercase     ,							50					)  # Prevent abuse!
      lowerCamelCase_							=   {
          '''q''': query,
          '''tbm''': '''isch''',
          '''hl''': '''en''',
          '''ijn''': '''0''',
      }

      lowerCamelCase_							=   requests.get('https://www.google.com/search'     ,							params=lowercase     ,							headers=lowercase					)
      lowerCamelCase_							=   BeautifulSoup(html.text     ,							'html.parser'					)
      lowerCamelCase_							=   ''''''.join(
          re.findall(r'AF_initDataCallback\(([^<]+)\);'     ,							str(soup.select('script'					)					)					)					)

      lowerCamelCase_							=   json.dumps(lowercase					)
      lowerCamelCase_							=   json.loads(lowercase					)

      lowerCamelCase_							=   re.findall(
          r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'     ,							lowercase     ,							)
      if not matched_google_image_data:
            return 0

      lowerCamelCase_							=   re.sub(
          r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'     ,							''     ,							str(lowercase					)     ,							)

      lowerCamelCase_							=   re.findall(
          r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'     ,							lowercase     ,							)
      for index, fixed_full_res_image in enumerate(lowercase					):
            if index >= max_images:
                  return index
            lowerCamelCase_							=   bytes(lowercase     ,							'ascii'					).decode(
                'unicode-escape'					)
            lowerCamelCase_							=   bytes(lowercase     ,							'ascii'					).decode(
                'unicode-escape'					)
            lowerCamelCase_							=   urllib.request.build_opener()
            lowerCamelCase_							=   [
                (
                    '''User-Agent''',
                    '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
                    ''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582''',
                )
            ]
            urllib.request.install_opener(lowercase					)
            lowerCamelCase_							=   f"""query_{query.replace(" "     ,							"_"					)}"""
            if not os.path.exists(lowercase					):
                  os.makedirs(lowercase					)
            urllib.request.urlretrieve(  # noqa: S310
                lowercase     ,							f"""{path_name}/original_size_img_{index}.jpg"""					)
      return index


if __name__ == "__main__":
 try:
  lowerCamelCase    :		Optional[Any]      =       download_images_from_google_query(sys.argv[1])
  print(F"""{image_count} images were downloaded to disk.""")
 except IndexError:
  print("Please provide a search term.")
  raise









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    :		Tuple			   =       logging.get_logger(__name__)

lowerCamelCase    :		Union[str, Any]			   =       "Hello world! cécé herlolip"

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	bool					):


      '''simple docstring'''



      lowerCamelCase_							=   FairseqRobertaModel.from_pretrained(lowercase					)
      roberta.eval()  # disable dropout
      lowerCamelCase_							=   roberta.model.encoder.sentence_encoder
      lowerCamelCase_							=   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=5_14     ,							type_vocab_size=1     ,							layer_norm_eps=1e-5     ,							)
      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our RoBERTa config:'     ,							lowercase					)

      lowerCamelCase_							=   XLMRobertaXLForSequenceClassification(lowercase					) if classification_head else XLMRobertaXLForMaskedLM(lowercase					)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowerCamelCase_							=   roberta_sent_encoder.embed_tokens.weight
      lowerCamelCase_							=   roberta_sent_encoder.embed_positions.weight
      lowerCamelCase_							=   torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.

      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.weight
      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.bias

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

            lowerCamelCase_							=   layer.attention
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.bias

            # self attention
            lowerCamelCase_							=   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)					)
            )

            lowerCamelCase_							=   roberta_layer.self_attn.q_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.q_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.bias

            # self-attention output
            lowerCamelCase_							=   layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            lowerCamelCase_							=   roberta_layer.self_attn.out_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            lowerCamelCase_							=   roberta_layer.final_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.final_layer_norm.bias

            # intermediate
            lowerCamelCase_							=   layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCamelCase_							=   roberta_layer.fca.weight
            lowerCamelCase_							=   roberta_layer.fca.bias

            # output
            lowerCamelCase_							=   layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCamelCase_							=   roberta_layer.fca.weight
            lowerCamelCase_							=   roberta_layer.fca.bias
            # end of layer

      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].dense.weight
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].dense.bias
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.weight
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.bias
      else:
            # LM Head
            lowerCamelCase_							=   roberta.model.encoder.lm_head.dense.weight
            lowerCamelCase_							=   roberta.model.encoder.lm_head.dense.bias
            lowerCamelCase_							=   roberta.model.encoder.lm_head.layer_norm.weight
            lowerCamelCase_							=   roberta.model.encoder.lm_head.layer_norm.bias
            lowerCamelCase_							=   roberta.model.encoder.lm_head.weight
            lowerCamelCase_							=   roberta.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      lowerCamelCase_							=   roberta.encode(lowercase					).unsqueeze(0					)  # batch of size 1

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

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


if __name__ == "__main__":
 lowerCamelCase    :		Optional[Any]			   =       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    :		List[Any]			   =       parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )



