


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

			return flax_key_tuple, flax_tensor







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			if "metadata" in layer:
						UpperCAmelCase__					:   int       				=       layer.split("""metadata"""   )
						UpperCAmelCase__					:   Any       				=       """""".join(split_layer[0]   )[:-1]
						UpperCAmelCase__					:   List[Any]       				=       [tuple(("""metadata""" + split_layer[1]).split("""/"""   )   )]
			elif "kvstore" in layer:
						UpperCAmelCase__					:   Union[str, Any]       				=       layer.split("""kvstore"""   )
						UpperCAmelCase__					:   Optional[Any]       				=       """""".join(split_layer[0]   )[:-1]
						UpperCAmelCase__					:   Dict       				=       [tuple(("""kvstore""" + split_layer[1]).split("""/"""   )   )]

			else:
						UpperCAmelCase__					:   List[Any]       				=       layer.split("""/"""   )
						UpperCAmelCase__					:   Optional[int]       				=       """/""".join(split_layer[:-1]   )
						UpperCAmelCase__					:   List[Any]       				=       (split_layer[-1],)

			if "kvstore/path" in layer:
						UpperCAmelCase__					:   Tuple       				=       f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
			elif "kvstore/driver" in layer:
						UpperCAmelCase__					:   int       				=       """file"""
			else:
						UpperCAmelCase__					:   List[str]       				=       checkpoint_info[layer]

			return curr_real_layer_name, split_layer, content







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Any       				=       rename_keys(UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       {}
			for k, v in current_block.items():
						UpperCAmelCase__					:   Optional[int]       				=       v
			UpperCAmelCase__					:   int       				=       new_current_block
			torch.save(UpperCamelCase__	, UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__ = WEIGHTS_NAME   ):
			UpperCAmelCase__					:   List[Any]       				=       convert_file_size_to_int(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       []
			UpperCAmelCase__					:   str       				=       {}
			UpperCAmelCase__					:   Optional[int]       				=       0
			UpperCAmelCase__					:   Any       				=       0

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

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

			for key in all_layers.keys():
						# open tensorstore file
						UpperCAmelCase__					:   Any       				=       ts.open(unflatten_dict(all_layers[key]   )   ).result().read().result()
						UpperCAmelCase__					:   Optional[int]       				=       torch.tensor(UpperCamelCase__   )
						UpperCAmelCase__					:   Any       				=       raw_weights.numel() * dtype_byte_size(raw_weights.dtype   )

						# use the renaming pattern from the small conversion scripts
						UpperCAmelCase__					:   int       				=       rename_base_flax_keys(tuple(key.split("""/"""   )   )	, UpperCamelCase__   )
						UpperCAmelCase__					:   Tuple       				=       """/""".join(UpperCamelCase__   )

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

						UpperCAmelCase__					:   str       				=       raw_weights.to(getattr(UpperCamelCase__	, UpperCamelCase__   )   )
						current_block_size += weight_size
						total_size += weight_size

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

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

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

    # Add the metadata
			UpperCAmelCase__					:   Union[str, Any]       				=       {"""total_size""": total_size}
			UpperCAmelCase__					:   Optional[Any]       				=       {"""metadata""": metadata, """weight_map""": weight_map}

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

			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       _UpperCamelCase (						):
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

			UpperCAmelCase__					:   Dict       				=       SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""   )
			config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       SwitchTransformersForConditionalGeneration.from_pretrained(
			    """/home/arthur_huggingface_co/transformers/switch_converted"""	, device_map="""auto"""   )

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

			UpperCAmelCase__					:   Optional[int]       				=       tokenizer(UpperCamelCase__	, return_tensors="""pt"""   ).input_ids
			UpperCAmelCase__					:   Dict       				=       model.generate(UpperCamelCase__	, decoder_start_token_id=0   )
			print(tokenizer.decode(out[0]   )   )



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import tensorflow as tf

		from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM


@require_tf
@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer("""Hello there"""							,							return_tensors="""tf""").input_ids
								UpperCAmelCase__					:   Tuple       				=       tokenizer("""Hi I am"""							,							return_tensors="""tf""").input_ids

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)