


"""simple docstring"""







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

import requests
from bsa import BeautifulSoup

lowerCamelCase_   :   List[str]									=  {
    """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 _A						(						lowercase = "dhaka"       ,    lowercase = 5		):







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

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

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

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

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

    a 		=re.findall(
        R'''(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'''       ,    lowercase       ,    )
    for index, fixed_full_res_image in enumerate(lowercase		):
        if index >= max_images:
            return index
        a 		=bytes(lowercase       ,    '''ascii'''		).decode(
            '''unicode-escape'''		)
        a 		=bytes(lowercase       ,    '''ascii'''		).decode(
            '''unicode-escape'''		)
        a 		=urllib.request.build_opener()
        a 		=[
            (
                '''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		)
        a 		=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_   :   List[str]									=  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 os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

					lowercase							=						(
					    ('''layer.''', '''layer_'''),
					    ('''word_embeddings.weight''', '''word_embeddings'''),
					    ('''position_embeddings.weight''', '''position_embeddings'''),
					    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
					    ('''.''', '''/'''),
					    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
					    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
					    ('''weight''', '''kernel'''),
					)

					if not os.path.isdir(lowerCAmelCase__		):
										os.makedirs(lowerCAmelCase__		)

					lowercase							=						model.state_dict()

					def to_tf_var_name(lowerCAmelCase__		):
										for patt, repl in iter(lowerCAmelCase__		):
															lowercase							=						name.replace(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return f'bert/{name}'

					def create_tf_var(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
										lowercase							=						tf.dtypes.as_dtype(tensor.dtype		)
										lowercase							=						tf.get_variable(dtype=lowerCAmelCase__ ,			shape=tensor.shape ,			name=lowerCAmelCase__ ,			initializer=tf.zeros_initializer()		)
										session.run(tf.variables_initializer([tf_var]		)		)
										session.run(lowerCAmelCase__		)
										return tf_var

					tf.reset_default_graph()
					with tf.Session() as session:
										for var_name in state_dict:
															lowercase							=						to_tf_var_name(lowerCAmelCase__		)
															lowercase							=						state_dict[var_name].numpy()
															if any(x in var_name for x in tensors_to_transpose		):
																				lowercase							=						torch_tensor.T
															lowercase							=						create_tf_var(tensor=lowerCAmelCase__ ,			name=lowerCAmelCase__ ,			session=lowerCAmelCase__		)
															tf.keras.backend.set_value(lowerCAmelCase__ ,			lowerCAmelCase__		)
															lowercase							=						session.run(lowerCAmelCase__		)
															print(f'Successfully created {tf_name}: {np.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)}'		)

										lowercase							=						tf.train.Saver(tf.trainable_variables()		)
										saver.save(lowerCAmelCase__ ,			os.path.join(lowerCAmelCase__ ,			model_name.replace('''-''' ,			'''_'''		) + '''.ckpt'''		)		)



def        UpperCamelCase    (							lowerCAmelCase__=None		):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''model name e.g. bert-base-uncased'''		)
					parser.add_argument(
					    '''--cache_dir''' ,			type=lowerCAmelCase__ ,			default=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory containing pytorch model'''		)
					parser.add_argument('''--pytorch_model_path''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''/path/to/<pytorch-model-name>.bin'''		)
					parser.add_argument('''--tf_cache_dir''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory in which to save tensorflow model'''		)
					lowercase							=						parser.parse_args(lowerCAmelCase__		)

					lowercase							=						BertModel.from_pretrained(
					    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path		) ,			cache_dir=args.cache_dir ,			)

					convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name		)


if __name__ == "__main__":
							main()




