




"""simple docstring"""
import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int





def 						UpperCAmelCase				(			A__:    List[str]							,				A__:    Tuple     )  ->     int:
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						__lowerCamelCase					:	Dict	       =				flax_key_tuple[:-1] + ('weight',)
						__lowerCamelCase					:	Optional[int]	       =				torch.permute(A__							,				(0, 2, 1)     )
			elif flax_key_tuple[-1] == "kernel" and ".".join(A__     ):
						# linear layer
						__lowerCamelCase					:	int	       =				flax_key_tuple[:-1] + ('weight',)
						__lowerCamelCase					:	Tuple	       =				flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						__lowerCamelCase					:	List[str]	       =				flax_key_tuple[:-1] + ('weight',)

			return flax_key_tuple, flax_tensor





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    Union[str, Any]							,				A__:    Union[str, Any]     )  ->     List[Any]:
			if "metadata" in layer:
						__lowerCamelCase					:	Dict	       =				layer.split('metadata'     )
						__lowerCamelCase					:	Dict	       =				''.join(split_layer[0]     )[:-1]
						__lowerCamelCase					:	Tuple	       =				[tuple(('metadata' + split_layer[1]).split('/'     )     )]
			elif "kvstore" in layer:
						__lowerCamelCase					:	Optional[int]	       =				layer.split('kvstore'     )
						__lowerCamelCase					:	str	       =				''.join(split_layer[0]     )[:-1]
						__lowerCamelCase					:	Tuple	       =				[tuple(('kvstore' + split_layer[1]).split('/'     )     )]

			else:
						__lowerCamelCase					:	str	       =				layer.split('/'     )
						__lowerCamelCase					:	Optional[Any]	       =				'/'.join(split_layer[:-1]     )
						__lowerCamelCase					:	str	       =				(split_layer[-1],)

			if "kvstore/path" in layer:
						__lowerCamelCase					:	Union[str, Any]	       =				f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
			elif "kvstore/driver" in layer:
						__lowerCamelCase					:	int	       =				'file'
			else:
						__lowerCamelCase					:	Any	       =				checkpoint_info[layer]

			return curr_real_layer_name, split_layer, content





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    int     )  ->     Union[str, Any]:
			__lowerCamelCase					:	List[Any]	       =				rename_keys(A__     )
			__lowerCamelCase					:	Optional[Any]	       =				{}
			for k, v in current_block.items():
						__lowerCamelCase					:	List[Any]	       =				v
			__lowerCamelCase					:	Any	       =				new_current_block
			torch.save(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    List[str]							,				A__:    Tuple							,				A__:    Union[str, Any]							,				A__:    int							,				A__:    str = WEIGHTS_NAME     )  ->     List[Any]:
			__lowerCamelCase					:	Any	       =				convert_file_size_to_int(A__     )
			__lowerCamelCase					:	Tuple	       =				[]
			__lowerCamelCase					:	List[str]	       =				{}
			__lowerCamelCase					:	Tuple	       =				0
			__lowerCamelCase					:	str	       =				0

			os.makedirs(A__							,				exist_ok=A__     )
			with gfile.GFile(switch_checkpoint_path + '/checkpoint'							,				'rb'     ) as fp:
						__lowerCamelCase					:	Dict	       =				serialization.msgpack_restore(fp.read()     )['optimizer']['target']
						__lowerCamelCase					:	Optional[Any]	       =				flatten_dict(A__							,				sep='/'     )

			__lowerCamelCase					:	Any	       =				{}
			for layer in checkpoint_info.keys():
						__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				get_key_and_tensorstore_dict(
						    A__							,				A__							,				A__     )
						if curr_real_layer_name in all_layers:
									__lowerCamelCase					:	Optional[int]	       =				content
						else:
									__lowerCamelCase					:	Optional[Any]	       =				{split_layer[-1]: content}

			for key in all_layers.keys():
						# open tensorstore file
						__lowerCamelCase					:	Dict	       =				ts.open(unflatten_dict(all_layers[key]     )     ).result().read().result()
						__lowerCamelCase					:	str	       =				torch.tensor(A__     )
						__lowerCamelCase					:	List[str]	       =				raw_weights.numel() * dtype_byte_size(raw_weights.dtype     )

						# use the renaming pattern from the small conversion scripts
						__lowerCamelCase       ,      __lowerCamelCase					:	List[Any]	       =				rename_base_flax_keys(tuple(key.split('/'     )     )							,				A__     )
						__lowerCamelCase					:	str	       =				'/'.join(A__     )

						# If this weight is going to tip up over the maximal size, we split.
						if current_block_size + weight_size > max_shard_size:
									__lowerCamelCase					:	Tuple	       =				os.path.join(
									    A__							,				weights_name.replace('.bin'							,				f'''-{len(A__     )+1:05d}-of-???.bin'''     )     )
									rename_and_save_block(A__							,				A__     )
									sharded_state_dicts.append(current_block.keys()     )
									del current_block
									__lowerCamelCase					:	Optional[Any]	       =				{}
									__lowerCamelCase					:	Optional[Any]	       =				0

						__lowerCamelCase					:	Union[str, Any]	       =				raw_weights.to(getattr(A__							,				A__     )     )
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			__lowerCamelCase					:	List[str]	       =				os.path.join(A__							,				weights_name.replace('.bin'							,				f'''-{len(A__     )+1:05d}-of-???.bin'''     )     )
			rename_and_save_block(A__							,				A__     )
			sharded_state_dicts.append(current_block.keys()     )

			# If we only have one shard, we return it
			if len(A__     ) == 1:
						return {weights_name: sharded_state_dicts[0]}, None

			# Otherwise, let's build the index
			__lowerCamelCase					:	Union[str, Any]	       =				{}
			__lowerCamelCase					:	int	       =				{}
			for idx, shard in enumerate(A__     ):
						__lowerCamelCase					:	Tuple	       =				weights_name.replace(
						    '.bin'							,				f'''-{idx+1:05d}-of-{len(A__     ):05d}.bin'''     )  # len(sharded_state_dicts):05d}
						__lowerCamelCase					:	Union[str, Any]	       =				os.path.join(A__							,				weights_name.replace('.bin'							,				f'''-{idx+1:05d}-of-???.bin'''     )     )
						os.rename(A__							,				os.path.join(A__							,				A__     )     )
						__lowerCamelCase					:	Dict	       =				shard
						for key in shard:
									__lowerCamelCase					:	str	       =				shard_file

    # Add the metadata
			__lowerCamelCase					:	List[str]	       =				{'total_size': total_size}
			__lowerCamelCase					:	str	       =				{'metadata': metadata, 'weight_map': weight_map}

			with open(os.path.join(A__							,				A__     )							,				'w'							,				encoding='utf-8'     ) as f:
						__lowerCamelCase					:	Union[str, Any]	       =				json.dumps(A__							,				indent=2							,				sort_keys=A__     ) + '\n'
						f.write(A__     )

			return metadata, index


if __name__ == "__main__":
					a_				:    List[Any]										=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--switch_t5x_checkpoint_path''',
					    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
					    type=str,
					    required=False,
					    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
					)
					parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
					parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
					parser.add_argument(
					    '''--pytorch_dump_folder_path''',
					    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
					    type=str,
					    required=False,
					    help='''Path to the output pytorch model.''',
					)
					a_				:    Optional[Any]										=     parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)






def 						UpperCAmelCase				(			)  ->     List[Any]:
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

			__lowerCamelCase					:	str	       =				SwitchTransformersConfig.from_pretrained('google/switch-base-8'     )
			config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'     )
			__lowerCamelCase					:	str	       =				SwitchTransformersForConditionalGeneration.from_pretrained(
			    '/home/arthur_huggingface_co/transformers/switch_converted'							,				device_map='auto'     )

			__lowerCamelCase					:	Dict	       =				TaTokenizer.from_pretrained('t5-small'     )
			__lowerCamelCase					:	List[Any]	       =				'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

			__lowerCamelCase					:	Any	       =				tokenizer(A__							,				return_tensors='pt'     ).input_ids
			__lowerCamelCase					:	List[Any]	       =				model.generate(A__							,				decoder_start_token_id=0     )
			print(tokenizer.decode(out[0]     )     )









"""simple docstring"""
import requests

a_				:    Optional[Any]										=     ''''''  # <-- Put your OpenWeatherMap appid here!
a_				:    str										=     '''https://api.openweathermap.org/data/2.5/'''





def 						UpperCAmelCase				(			A__:    str = "Chicago"							,				A__:    str = APPID     )  ->     dict:
			return requests.get(URL_BASE + 'weather'							,				params=locals()     ).json()





def 						UpperCAmelCase				(			A__:    str = "Kolkata, India"							,				A__:    str = APPID     )  ->     dict:
			return requests.get(URL_BASE + 'forecast'							,				params=locals()     ).json()





def 						UpperCAmelCase				(			A__:    float = 55.68							,				A__:    float = 12.57							,				A__:    str = APPID     )  ->     dict:
			return requests.get(URL_BASE + 'onecall'							,				params=locals()     ).json()


if __name__ == "__main__":
					from pprint import pprint

					while True:
										a_				:    List[Any]										=     input('''Enter a location:''').strip()
										if location:
															pprint(current_weather(location))
										else:
															break



