






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 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								SCREAMING_SNAKE_CASE_   :	Tuple													=   flax_key_tuple[:-1] + ('weight',)
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.permute(lowerCAmelCase__    ,    (0, 2, 1)      )
				elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__      ):
								# linear layer
								SCREAMING_SNAKE_CASE_   :	List[str]													=   flax_key_tuple[:-1] + ('weight',)
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								SCREAMING_SNAKE_CASE_   :	int													=   flax_key_tuple[:-1] + ('weight',)

				return flax_key_tuple, flax_tensor



def 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if "metadata" in layer:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   layer.split('metadata'      )
								SCREAMING_SNAKE_CASE_   :	str													=   ''.join(split_layer[0]      )[:-1]
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   [tuple(('metadata' + split_layer[1]).split('/'      )      )]
				elif "kvstore" in layer:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   layer.split('kvstore'      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ''.join(split_layer[0]      )[:-1]
								SCREAMING_SNAKE_CASE_   :	int													=   [tuple(('kvstore' + split_layer[1]).split('/'      )      )]

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

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

				return curr_real_layer_name, split_layer, content



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   rename_keys(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	str													=   {}
				for k, v in current_block.items():
								SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	str													=   new_current_block
				torch.save(lowerCAmelCase__    ,    lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		Optional[int] = WEIGHTS_NAME      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	int													=   convert_file_size_to_int(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Any													=   []
				SCREAMING_SNAKE_CASE_   :	int													=   {}
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   0
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   0

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

				SCREAMING_SNAKE_CASE_   :	Any													=   {}
				for layer in checkpoint_info.keys():
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_key_and_tensorstore_dict(
								    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								if curr_real_layer_name in all_layers:
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   content
								else:
												SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {split_layer[-1]: content}

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

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

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

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

				# Add the last block
				SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(lowerCAmelCase__    ,    weights_name.replace('.bin'    ,    F'''-{len(lowerCAmelCase__      )+1:05d}-of-???.bin'''      )      )
				rename_and_save_block(lowerCAmelCase__    ,    lowerCAmelCase__      )
				sharded_state_dicts.append(current_block.keys()      )

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

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

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

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

				return metadata, index


if __name__ == "__main__":
					A        =			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        =			parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)



def 					_lowerCamelCase(			):





				'''simple docstring'''







				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   SwitchTransformersConfig.from_pretrained('google/switch-base-8'      )
				config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   SwitchTransformersForConditionalGeneration.from_pretrained(
				    '/home/arthur_huggingface_co/transformers/switch_converted'    ,    device_map='auto'      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   TaTokenizer.from_pretrained('t5-small'      )
				SCREAMING_SNAKE_CASE_   :	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>.'

				SCREAMING_SNAKE_CASE_   :	Tuple													=   tokenizer(lowerCAmelCase__    ,    return_tensors='pt'      ).input_ids
				SCREAMING_SNAKE_CASE_   :	Tuple													=   model.generate(lowerCAmelCase__    ,    decoder_start_token_id=0      )
				print(tokenizer.decode(out[0]      )      )







from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class 							__a							(  __A						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   SMALL_MODEL_IDENTIFIER
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   'pt'
						SCREAMING_SNAKE_CASE_   :	Tuple													=   'tf'


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   AutoModel.from_pretrained(self.test_model       )
						model_pt.save_pretrained(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   TFAutoModel.from_pretrained(self.test_model ,    from_pt=UpperCamelCase__       )
						model_tf.save_pretrained(UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   'mock_framework'

						# Framework provided - return whatever the user provides
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FeaturesManager.determine_framework(self.test_model ,    UpperCamelCase__       )
						self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
										self._setup_pt_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Dict													=   FeaturesManager.determine_framework(UpperCamelCase__ ,    UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
										self._setup_tf_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	List[str]													=   FeaturesManager.determine_framework(UpperCamelCase__ ,    UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
										self._setup_pt_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   FeaturesManager.determine_framework(UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_pt       )

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
										self._setup_tf_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FeaturesManager.determine_framework(UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_tf       )

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
										with self.assertRaises(UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	Any													=   FeaturesManager.determine_framework(UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_tf_available' ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	str													=   FeaturesManager.determine_framework(self.test_model       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_pt       )

						# PyTorch not in environment -> use TensorFlow
						SCREAMING_SNAKE_CASE_   :	List[str]													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_torch_available' ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   FeaturesManager.determine_framework(self.test_model       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_tf       )

						# Both in environment -> use PyTorch
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MagicMock(return_value=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_tf_available' ,    UpperCamelCase__       ), patch(
						    'transformers.onnx.features.is_torch_available' ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FeaturesManager.determine_framework(self.test_model       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_pt       )

						# Both not in environment -> raise error
						SCREAMING_SNAKE_CASE_   :	Tuple													=   MagicMock(return_value=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_tf_available' ,    UpperCamelCase__       ), patch(
						    'transformers.onnx.features.is_torch_available' ,    UpperCamelCase__       ):
										with self.assertRaises(UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   FeaturesManager.determine_framework(self.test_model       )