






'''simple docstring'''




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

import requests
from bsa import BeautifulSoup

__lowerCamelCase   :					Dict   							=						{
    '''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 	__UpperCAmelCase       ( __magic_name__ = "dhaka"     ,__magic_name__ = 5						)->    Union[str, Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       min(lowerCamelCase_     ,50						)  # Prevent abuse!
			snake_case_				:					Union[str, Any]   =       {
			    """q""": query,
			    """tbm""": """isch""",
			    """hl""": """en""",
			    """ijn""": """0""",
			}

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

			snake_case_				:					Optional[Any]   =       json.dumps(lowerCamelCase_						)
			snake_case_				:					Tuple   =       json.loads(lowerCamelCase_						)

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

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

			snake_case_				:					Any   =       re.findall(
			    r"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"     ,lowerCamelCase_     ,)
			for index, fixed_full_res_image in enumerate(lowerCamelCase_						):
						if index >= max_images:
									return index
						snake_case_				:					int   =       bytes(lowerCamelCase_     ,"ascii"						).decode(
						    "unicode-escape"						)
						snake_case_				:					Dict   =       bytes(lowerCamelCase_     ,"ascii"						).decode(
						    "unicode-escape"						)
						snake_case_				:					Union[str, Any]   =       urllib.request.build_opener()
						snake_case_				:					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""",
						    )
						]
						urllib.request.install_opener(lowerCamelCase_						)
						snake_case_				:					Optional[int]   =       F'''query_{query.replace(' '     ,'_'						)}'''
						if not os.path.exists(lowerCamelCase_						):
									os.makedirs(lowerCamelCase_						)
						urllib.request.urlretrieve(  # noqa: S310
						    lowerCamelCase_     ,F'''{path_name}/original_size_img_{index}.jpg'''						)
			return index


if __name__ == "__main__":
		try:
				__lowerCamelCase   :					List[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












'''simple docstring'''




import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase   :					str   							=					'''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCamelCase   :					Tuple   							=					[
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

			with open(os.path.join(__magic_name__     ,"utils"     ,"tf_ops"     ,"onnx.json"						)						) as f:
						snake_case_				:					Dict   =       json.load(__magic_name__						)["opsets"]

			for i in range(1     ,opset + 1						):
						onnx_ops.extend(onnx_opsets[str(__magic_name__						)]						)

			with open(__magic_name__     ,"rb"						) as f:
						saved_model.ParseFromString(f.read()						)

			snake_case_				:					Tuple   =       set()

			# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
			for meta_graph in saved_model.meta_graphs:
						# Add operations in the graph definition
						model_op_names.update(node.op for node in meta_graph.graph_def.node						)

						# Go through the functions in the graph definition
						for func in meta_graph.graph_def.library.function:
									# Add operations in each function
									model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
			snake_case_				:					str   =       sorted(__magic_name__						)
			snake_case_				:					Optional[int]   =       []

			for op in model_op_names:
						if op not in onnx_ops and op not in INTERNAL_OPS:
									incompatible_ops.append(__magic_name__						)

			if strict and len(__magic_name__						) > 0:
						raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
			elif len(__magic_name__						) > 0:
						print(F'''Found the following incompatible ops for the opset {opset}:'''						)
						print(*__magic_name__     ,sep="\n"						)
			else:
						print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
		parser.add_argument(
		    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
		)
		parser.add_argument(
		    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
		)
		parser.add_argument(
		    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)




