
'''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__  ,      A__						):
	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		a_      				=						flax_key_tuple[:-1] + ("""weight""",)
		a_      				=						torch.permute(A__  ,      (0, 2, 1)						)
	elif flax_key_tuple[-1] == "kernel" and ".".join(A__						):
		# linear layer
		a_      				=						flax_key_tuple[:-1] + ("""weight""",)
		a_      				=						flax_tensor.T
	elif flax_key_tuple[-1] in ["scale", "embedding"]:
		a_      				=						flax_key_tuple[:-1] + ("""weight""",)

	return flax_key_tuple, flax_tensor






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	if "metadata" in layer:
		a_      				=						layer.split("""metadata"""						)
		a_      				=						"""""".join(split_layer[0]						)[:-1]
		a_      				=						[tuple(("""metadata""" + split_layer[1]).split("""/"""						)						)]
	elif "kvstore" in layer:
		a_      				=						layer.split("""kvstore"""						)
		a_      				=						"""""".join(split_layer[0]						)[:-1]
		a_      				=						[tuple(("""kvstore""" + split_layer[1]).split("""/"""						)						)]

	else:
		a_      				=						layer.split("""/"""						)
		a_      				=						"""/""".join(split_layer[:-1]						)
		a_      				=						(split_layer[-1],)

	if "kvstore/path" in layer:
		a_      				=						F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
	elif "kvstore/driver" in layer:
		a_      				=						"""file"""
	else:
		a_      				=						checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content






def      UpperCamelCase_				(      A__  ,      A__						):
	a_      				=						rename_keys(A__						)
	a_      				=						{}
	for k, v in current_block.items():
		a_      				=						v
	a_      				=						new_current_block
	torch.save(A__  ,      A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__ = WEIGHTS_NAME						):
	a_      				=						convert_file_size_to_int(A__						)
	a_      				=						[]
	a_      				=						{}
	a_      				=						0
	a_      				=						0

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

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

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

		# use the renaming pattern from the small conversion scripts
		a_				,  a_      				=						rename_base_flax_keys(tuple(key.split("""/"""						)						)  ,      A__						)
		a_      				=						"""/""".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:
			a_      				=						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
			a_      				=						{}
			a_      				=						0

		a_      				=						raw_weights.to(getattr(A__  ,      A__						)						)
		current_block_size += weight_size
		total_size += weight_size

	# Add the last block
	a_      				=						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
	a_      				=						{}
	a_      				=						{}
	for idx, shard in enumerate(A__						):
		a_      				=						weights_name.replace(
		    """.bin"""  ,      F'''-{idx+1:05d}-of-{len(A__						):05d}.bin'''						)  # len(sharded_state_dicts):05d}
		a_      				=						os.path.join(A__  ,      weights_name.replace(""".bin"""  ,      F'''-{idx+1:05d}-of-???.bin'''						)						)
		os.rename(A__  ,      os.path.join(A__  ,      A__						)						)
		a_      				=						shard
		for key in shard:
			a_      				=						shard_file

    # Add the metadata
	a_      				=						{"""total_size""": total_size}
	a_      				=						{"""metadata""": metadata, """weight_map""": weight_map}

	with open(os.path.join(A__  ,      A__						)  ,      """w"""  ,      encoding="""utf-8"""						) as f:
		a_      				=						json.dumps(A__  ,      indent=2  ,      sort_keys=A__						) + """\n"""
		f.write(A__						)

	return metadata, index


if __name__ == "__main__":
		lowercase__											=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.',
		)
		lowercase__											=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_				(      ):
	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

	a_      				=						SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""						)
	config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""						)
	a_      				=						SwitchTransformersForConditionalGeneration.from_pretrained(
	    """/home/arthur_huggingface_co/transformers/switch_converted"""  ,      device_map="""auto"""						)

	a_      				=						TaTokenizer.from_pretrained("""t5-small"""						)
	a_      				=						"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

	a_      				=						tokenizer(A__  ,      return_tensors="""pt"""						).input_ids
	a_      				=						model.generate(A__  ,      decoder_start_token_id=0						)
	print(tokenizer.decode(out[0]						)						)


'''simple docstring'''
import requests

lowercase__											=''  # <-- Put your OpenWeatherMap appid here!
lowercase__											='https://api.openweathermap.org/data/2.5/'






def      UpperCamelCase_				(      A__ = "Chicago"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """weather"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = "Kolkata, India"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """forecast"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = 55.68  ,      A__ = 12.57  ,      A__ = APPID						):
	return requests.get(URL_BASE + """onecall"""  ,      params=locals()						).json()


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

		while True:
				lowercase__											=input('Enter a location:').strip()
				if location:
						pprint(current_weather(location))
				else:
						break
