






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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    int:




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

							return flax_key_tuple, flax_tensor






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    int:




							if "metadata" in layer:
														_lowercase							:   Dict 							=   layer.split('metadata'				)
														_lowercase							:   Any 							=   ''.join(split_layer[0]				)[:-1]
														_lowercase							:   Tuple 							=   [tuple(('metadata' + split_layer[1]).split('/'				)				)]
							elif "kvstore" in layer:
														_lowercase							:   Tuple 							=   layer.split('kvstore'				)
														_lowercase							:   Union[str, Any] 							=   ''.join(split_layer[0]				)[:-1]
														_lowercase							:   Dict 							=   [tuple(('kvstore' + split_layer[1]).split('/'				)				)]

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

							if "kvstore/path" in layer:
														_lowercase							:   Dict 							=   F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
							elif "kvstore/driver" in layer:
														_lowercase							:   List[str] 							=   'file'
							else:
														_lowercase							:   Union[str, Any] 							=   checkpoint_info[layer]

							return curr_real_layer_name, split_layer, content






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Dict:
							_lowercase							:   str 							=   rename_keys(_SCREAMING_SNAKE_CASE				)
							_lowercase							:   List[str] 							=   {}
							for k, v in current_block.items():
														_lowercase							:   str 							=   v
							_lowercase							:   List[str] 							=   new_current_block
							torch.save(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = WEIGHTS_NAME				)     ->    Dict:
							_lowercase							:   Any 							=   convert_file_size_to_int(_SCREAMING_SNAKE_CASE				)
							_lowercase							:   Any 							=   []
							_lowercase							:   Any 							=   {}
							_lowercase							:   Optional[Any] 							=   0
							_lowercase							:   Union[str, Any] 							=   0

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

							_lowercase							:   int 							=   {}
							for layer in checkpoint_info.keys():
														_lowercase							,							_lowercase							,							_lowercase							:   Union[str, Any] 							=   get_key_and_tensorstore_dict(
														    _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE				)
														if curr_real_layer_name in all_layers:
																					_lowercase							:   Any 							=   content
														else:
																					_lowercase							:   Dict 							=   {split_layer[-1]: content}

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

														# use the renaming pattern from the small conversion scripts
														_lowercase							,							_lowercase							:   int 							=   rename_base_flax_keys(tuple(key.split('/'				)				)     ,     _SCREAMING_SNAKE_CASE				)
														_lowercase							:   str 							=   '/'.join(_SCREAMING_SNAKE_CASE				)

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

														_lowercase							:   Tuple 							=   raw_weights.to(getattr(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE				)				)
														current_block_size += weight_size
														total_size += weight_size

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

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

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

    # Add the metadata
							_lowercase							:   Any 							=   {'total_size': total_size}
							_lowercase							:   Tuple 							=   {'metadata': metadata, 'weight_map': weight_map}

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

							return metadata, index


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






def    __magic_name__    (	)     ->    Union[str, Any]:
							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

							_lowercase							:   Union[str, Any] 							=   SwitchTransformersConfig.from_pretrained('google/switch-base-8'				)
							config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'				)
							_lowercase							:   Optional[Any] 							=   SwitchTransformersForConditionalGeneration.from_pretrained(
							    '/home/arthur_huggingface_co/transformers/switch_converted'     ,     device_map='auto'				)

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

							_lowercase							:   Tuple 							=   tokenizer(_SCREAMING_SNAKE_CASE     ,     return_tensors='pt'				).input_ids
							_lowercase							:   Optional[int] 							=   model.generate(_SCREAMING_SNAKE_CASE     ,     decoder_start_token_id=0				)
							print(tokenizer.decode(out[0]				)				)








'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)
@is_pt_tf_cross_test
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModel.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModel.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Optional[int]:
						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[Any]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Dict:
						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			int	)      ->		List[str]:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)



