

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		__lowerCamelCase    :    Dict 	=			flax_key_tuple[:-1] + ('weight',)
		__lowerCamelCase    :    Any 	=			torch.permute(_A     ,			(0, 2, 1)      )
	elif flax_key_tuple[-1] == "kernel" and ".".join(_A      ):
		# linear layer
		__lowerCamelCase    :    Optional[Any] 	=			flax_key_tuple[:-1] + ('weight',)
		__lowerCamelCase    :    Union[str, Any] 	=			flax_tensor.T
	elif flax_key_tuple[-1] in ["scale", "embedding"]:
		__lowerCamelCase    :    Dict 	=			flax_key_tuple[:-1] + ('weight',)

	return flax_key_tuple, flax_tensor






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

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

	if "kvstore/path" in layer:
		__lowerCamelCase    :    str 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    Optional[int] 	=			rename_keys(_A      )
	__lowerCamelCase    :    Optional[Any] 	=			{}
	for k, v in current_block.items():
		__lowerCamelCase    :    Optional[int] 	=			v
	__lowerCamelCase    :    Union[str, Any] 	=			new_current_block
	torch.save(_A     ,			_A      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = WEIGHTS_NAME      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Any 	=			convert_file_size_to_int(_A      )
	__lowerCamelCase    :    Optional[int] 	=			[]
	__lowerCamelCase    :    Tuple 	=			{}
	__lowerCamelCase    :    Optional[Any] 	=			0
	__lowerCamelCase    :    int 	=			0

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

	__lowerCamelCase    :    Optional[int] 	=			{}
	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    :    List[Any] 	=			content
		else:
			__lowerCamelCase    :    Any 	=			{split_layer[-1]: content}

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

		# use the renaming pattern from the small conversion scripts
		__lowerCamelCase	,			__lowerCamelCase    :    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    :    Optional[Any] 	=			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    :    List[str] 	=			0

		__lowerCamelCase    :    Optional[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    :    int 	=			{}
	__lowerCamelCase    :    Tuple 	=			{}
	for idx, shard in enumerate(_A      ):
		__lowerCamelCase    :    List[Any] 	=			weights_name.replace(
		    '.bin'     ,			F"-{idx+1:05d}-of-{len(_A      ):05d}.bin"      )  # len(sharded_state_dicts):05d}
		__lowerCamelCase    :    int 	=			os.path.join(_A     ,			weights_name.replace('.bin'     ,			F"-{idx+1:05d}-of-???.bin"      )      )
		os.rename(_A     ,			os.path.join(_A     ,			_A      )      )
		__lowerCamelCase    :    Optional[int] 	=			shard
		for key in shard:
			__lowerCamelCase    :    Dict 	=			shard_file

    # Add the metadata
	__lowerCamelCase    :    Tuple 	=			{'total_size': total_size}
	__lowerCamelCase    :    Optional[int] 	=			{'metadata': metadata, 'weight_map': weight_map}

	with open(os.path.join(_A     ,			_A      )     ,			'w'     ,			encoding='utf-8'      ) as f:
		__lowerCamelCase    :    Tuple 	=			json.dumps(_A     ,			indent=2     ,			sort_keys=_A      ) + '\n'
		f.write(_A      )

	return metadata, index


if __name__ == "__main__":
					a :    Optional[int]		=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[int]		=parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)






def        SCREAMING_SNAKE_CASE__	(  )  ->					int:
	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    :    Optional[int] 	=			SwitchTransformersForConditionalGeneration.from_pretrained(
	    '/home/arthur_huggingface_co/transformers/switch_converted'     ,			device_map='auto'      )

	__lowerCamelCase    :    str 	=			TaTokenizer.from_pretrained('t5-small'      )
	__lowerCamelCase    :    Dict 	=			'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    :    str 	=			model.generate(_A     ,			decoder_start_token_id=0      )
	print(tokenizer.decode(out[0]      )      )







import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar






class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    List[Any] 	=			logging.get_logger()

								# the current default level is logging.WARNING
								__lowerCamelCase    :    Dict 	=			logging.get_verbosity()

								logging.set_verbosity_error()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_warning()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_info()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_debug()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			logging.get_verbosity()

								__lowerCamelCase    :    str 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Tuple 	=			'Testing 1, 2, 3'

								# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
								if level_origin <= logging.WARNING:
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')

								# this is setting the level for all of `transformers.*` loggers
								logging.set_verbosity_error()

								# should not be able to log warnings
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,'')

								# should be able to log warnings again
								logging.set_verbosity_warning()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,msg + '\n')

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							@mockenv(TRANSFORMERS_VERBOSITY='error')
							def    lowerCAmelCase					(		self		:      Tuple):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								# this action activates the env var
								__lowerCamelCase    :    int 	=			logging.get_logger('transformers.models.bart.tokenization_bart')

								__lowerCamelCase    :    int 	=			os.getenv('TRANSFORMERS_VERBOSITY'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			logging.log_levels[env_level_str]

								__lowerCamelCase    :    Tuple 	=			logging.get_verbosity()
								self.assertEqual(
								    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,F"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"       ,)

								# restore to the original level
								__lowerCamelCase    :    List[str] 	=			''
								transformers.utils.logging._reset_library_root_logger()
							@mockenv(TRANSFORMERS_VERBOSITY='super-error')
							def    lowerCAmelCase					(		self		:      List[Any]):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								__lowerCamelCase    :    List[str] 	=			logging.logging.getLogger()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									# this action activates the env var
									logging.get_logger('transformers.models.bart.tokenization_bart')
								self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'       ,cl.out)

								# no need to restore as nothing was changed


							def    lowerCAmelCase					(		self		:      Any):
								# testing `logger.warning_advice()`
								transformers.utils.logging._reset_library_root_logger()

								__lowerCamelCase    :    Tuple 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Optional[int] 	=			'Testing 1, 2, 3'

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'):
									# nothing should be logged as env var disables this method
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,'')

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''):
									# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')






def        SCREAMING_SNAKE_CASE__	(  )  ->					Any:
	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()




