




'''simple docstring'''






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__						):
			__UpperCamelCase      :			Dict									=   AutoConfig.from_pretrained(snake_case__						)
			__UpperCamelCase      :			Optional[Any]									=   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__						)
			__UpperCamelCase      :			List[str]									=   checkpoints.load_tax_checkpoint(snake_case__						)

			__UpperCamelCase      :			List[Any]									=   "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

			if config.model_type == "t5":
						__UpperCamelCase      :			Tuple									=   "SelfAttention"
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						__UpperCamelCase      :			Any									=   "LocalSelfAttention"
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__UpperCamelCase      :			str									=   "TransientGlobalSelfAttention"
			else:
						raise ValueError(
						    "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
						    " attribute with a value from ['local', 'transient-global]."						)

			# Encoder
			for layer_index in range(config.num_layers						):
						__UpperCamelCase      :			int									=   F"layers_{str(snake_case__						)}"

						# Self-Attention
						__UpperCamelCase      :			Any									=   tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
						__UpperCamelCase      :			str									=   tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
						__UpperCamelCase      :			Union[str, Any]									=   tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
						__UpperCamelCase      :			List[str]									=   tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__UpperCamelCase      :			Optional[Any]									=   tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

						# Layer Normalization
						__UpperCamelCase      :			List[Any]									=   tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

						if split_mlp_wi:
									__UpperCamelCase      :			str									=   tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									__UpperCamelCase      :			Optional[Any]									=   tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									__UpperCamelCase      :			Optional[Any]									=   tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

						__UpperCamelCase      :			List[str]									=   tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						__UpperCamelCase      :			Optional[int]									=   tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						__UpperCamelCase      :			Optional[int]									=   flax_model.params["encoder"]["block"][str(snake_case__						)]["layer"]
						__UpperCamelCase      :			Tuple									=   tax_attention_key
						__UpperCamelCase      :			Optional[int]									=   tax_attention_out
						__UpperCamelCase      :			Optional[int]									=   tax_attention_query
						__UpperCamelCase      :			Optional[Any]									=   tax_attention_value

						__UpperCamelCase      :			Any									=   tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__UpperCamelCase      :			Optional[int]									=   tax_global_layer_norm

						if split_mlp_wi:
									__UpperCamelCase      :			Any									=   tax_mlp_wi_a
									__UpperCamelCase      :			Union[str, Any]									=   tax_mlp_wi_a
						else:
									__UpperCamelCase      :			Any									=   tax_mlp_wi

						__UpperCamelCase      :			Any									=   tax_mlp_wo
						__UpperCamelCase      :			int									=   tax_mlp_layer_norm

						__UpperCamelCase      :			Optional[int]									=   flax_model_encoder_layer_block

			# Only for layer 0:
			__UpperCamelCase      :			int									=   tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
			__UpperCamelCase      :			int									=   tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__UpperCamelCase      :			str									=   tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
						__UpperCamelCase      :			Optional[int]									=   tax_encoder_global_rel_embedding

			# Assigning
			__UpperCamelCase      :			List[str]									=   tax_model["target"]["encoder"]["encoder_norm"]["scale"]
			__UpperCamelCase      :			Optional[int]									=   tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers						):
						__UpperCamelCase      :			Any									=   F"layers_{str(snake_case__						)}"

						# Self-Attention
						__UpperCamelCase      :			List[Any]									=   tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
						__UpperCamelCase      :			Optional[int]									=   tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
						__UpperCamelCase      :			str									=   tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
						__UpperCamelCase      :			List[str]									=   tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

						# Layer Normalization
						__UpperCamelCase      :			List[Any]									=   tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
						    "scale"
						]

						# Encoder-Decoder-Attention
						__UpperCamelCase      :			Dict									=   tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
						__UpperCamelCase      :			str									=   tax_enc_dec_attention_module["key"]["kernel"]
						__UpperCamelCase      :			Dict									=   tax_enc_dec_attention_module["out"]["kernel"]
						__UpperCamelCase      :			Tuple									=   tax_enc_dec_attention_module["query"]["kernel"]
						__UpperCamelCase      :			str									=   tax_enc_dec_attention_module["value"]["kernel"]

						# Layer Normalization
						__UpperCamelCase      :			List[Any]									=   tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

						# MLP
						if split_mlp_wi:
									__UpperCamelCase      :			int									=   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									__UpperCamelCase      :			Optional[int]									=   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									__UpperCamelCase      :			List[Any]									=   tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

						__UpperCamelCase      :			Optional[Any]									=   tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						__UpperCamelCase      :			Optional[int]									=   tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						__UpperCamelCase      :			List[Any]									=   flax_model.params["decoder"]["block"][str(snake_case__						)]["layer"]
						__UpperCamelCase      :			List[str]									=   tax_attention_key
						__UpperCamelCase      :			int									=   tax_attention_out
						__UpperCamelCase      :			Dict									=   tax_attention_query
						__UpperCamelCase      :			Optional[Any]									=   tax_attention_value

						__UpperCamelCase      :			int									=   tax_pre_attention_layer_norm

						__UpperCamelCase      :			str									=   tax_enc_dec_attention_key
						__UpperCamelCase      :			Optional[int]									=   tax_enc_dec_attention_out
						__UpperCamelCase      :			Union[str, Any]									=   tax_enc_dec_attention_query
						__UpperCamelCase      :			Optional[int]									=   tax_enc_dec_attention_value

						__UpperCamelCase      :			Tuple									=   tax_cross_layer_norm

						if split_mlp_wi:
									__UpperCamelCase      :			List[str]									=   tax_mlp_wi_a
									__UpperCamelCase      :			Union[str, Any]									=   tax_mlp_wi_a
						else:
									__UpperCamelCase      :			Dict									=   tax_mlp_wi

						__UpperCamelCase      :			List[str]									=   tax_mlp_wo

						__UpperCamelCase      :			Tuple									=   txa_mlp_layer_norm

						__UpperCamelCase      :			Union[str, Any]									=   flax_model_decoder_layer_block

			# Decoder Normalization
			__UpperCamelCase      :			int									=   tax_model["target"]["decoder"]["decoder_norm"]["scale"]
			__UpperCamelCase      :			Union[str, Any]									=   txa_decoder_norm

			# Only for layer 0:
			__UpperCamelCase      :			Union[str, Any]									=   tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
			__UpperCamelCase      :			Union[str, Any]									=   tax_decoder_rel_embedding

			# Token Embeddings
			__UpperCamelCase      :			Tuple									=   tax_model["target"]["token_embedder"]["embedding"]
			__UpperCamelCase      :			Tuple									=   txa_token_embeddings

			# LM Head (only in v1.1 and LongT5 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						__UpperCamelCase      :			List[str]									=   tax_model["target"]["decoder"]["logits_dense"]["kernel"]

			flax_model.save_pretrained(snake_case__						)
			print("T5X Model was sucessfully converted!"						)


if __name__ == "__main__":
				_lowerCAmelCase      	=					argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
				)
				parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
				parser.add_argument(
				    '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
				)
				_lowerCAmelCase      	=					parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







"""simple docstring"""



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

import requests
from bsa import BeautifulSoup

__snake_case   			=    {
    """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   __lowerCAmelCase						(						lowercase  :  str = "dhaka"						,							lowercase  :  int = 5 )	->						int:


    """simple docstring"""





    snake_case    :      List[Any]      					=    min(lowercase						,							50 )  # Prevent abuse!
    snake_case    :      Optional[Any]      					=    {
        "q": query,
        "tbm": "isch",
        "hl": "en",
        "ijn": "0",
    }

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

    snake_case    :      Optional[Any]      					=    json.dumps(lowercase )
    snake_case    :      str      					=    json.loads(lowercase )

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

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

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





