



"""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 			_lowercase				(  __snake_case					,__snake_case	)						->   Optional[int]:
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						__lowerCAmelCase      :  Union[str, Any]												=       flax_key_tuple[:-1] + ("weight",)
						__lowerCAmelCase      :  Optional[Any]												=       torch.permute(__snake_case					,(0, 2, 1)	)
			elif flax_key_tuple[-1] == "kernel" and ".".join(__snake_case	):
						# linear layer
						__lowerCAmelCase      :  List[Any]												=       flax_key_tuple[:-1] + ("weight",)
						__lowerCAmelCase      :  Union[str, Any]												=       flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						__lowerCAmelCase      :  List[Any]												=       flax_key_tuple[:-1] + ("weight",)

			return flax_key_tuple, flax_tensor


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   List[str]:
			if "metadata" in layer:
						__lowerCAmelCase      :  Tuple												=       layer.split("metadata"	)
						__lowerCAmelCase      :  Any												=       "".join(split_layer[0]	)[:-1]
						__lowerCAmelCase      :  Optional[int]												=       [tuple(("metadata" + split_layer[1]).split("/"	)	)]
			elif "kvstore" in layer:
						__lowerCAmelCase      :  Tuple												=       layer.split("kvstore"	)
						__lowerCAmelCase      :  Tuple												=       "".join(split_layer[0]	)[:-1]
						__lowerCAmelCase      :  Tuple												=       [tuple(("kvstore" + split_layer[1]).split("/"	)	)]

			else:
						__lowerCAmelCase      :  str												=       layer.split("/"	)
						__lowerCAmelCase      :  Tuple												=       "/".join(split_layer[:-1]	)
						__lowerCAmelCase      :  List[str]												=       (split_layer[-1],)

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

			return curr_real_layer_name, split_layer, content


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Tuple:
			__lowerCAmelCase      :  Tuple												=       rename_keys(__snake_case	)
			__lowerCAmelCase      :  Dict												=       {}
			for k, v in current_block.items():
						__lowerCAmelCase      :  int												=       v
			__lowerCAmelCase      :  Optional[int]												=       new_current_block
			torch.save(__snake_case					,__snake_case	)


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case					,__snake_case = WEIGHTS_NAME	)						->   Any:
			__lowerCAmelCase      :  str												=       convert_file_size_to_int(__snake_case	)
			__lowerCAmelCase      :  Any												=       []
			__lowerCAmelCase      :  Any												=       {}
			__lowerCAmelCase      :  List[Any]												=       0
			__lowerCAmelCase      :  Dict												=       0

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

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

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

						# use the renaming pattern from the small conversion scripts
						__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       rename_base_flax_keys(tuple(key.split("/"	)	)					,__snake_case	)
						__lowerCAmelCase      :  Tuple												=       "/".join(__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:
									__lowerCAmelCase      :  Optional[int]												=       os.path.join(
									    __snake_case					,weights_name.replace(".bin"					,F"""-{len(__snake_case	)+1:05d}-of-???.bin"""	)	)
									rename_and_save_block(__snake_case					,__snake_case	)
									sharded_state_dicts.append(current_block.keys()	)
									del current_block
									__lowerCAmelCase      :  Optional[Any]												=       {}
									__lowerCAmelCase      :  Dict												=       0

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

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

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

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

    # Add the metadata
			__lowerCAmelCase      :  Tuple												=       {"total_size": total_size}
			__lowerCAmelCase      :  int												=       {"metadata": metadata, "weight_map": weight_map}

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

			return metadata, index


if __name__ == "__main__":
	__snake_case							:   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.',
	)
	__snake_case							:   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 			_lowercase				(  )						->   str:
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

			__lowerCAmelCase      :  Union[str, Any]												=       tokenizer(__snake_case					,return_tensors="pt"	).input_ids
			__lowerCAmelCase      :  Tuple												=       model.generate(__snake_case					,decoder_start_token_id=0	)
			print(tokenizer.decode(out[0]	)	)




"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'  ,		'False'							)							) is not True  ,		reason='Skipping test because should only be run when releasing minor transformers version'  ,		)
@pytest.mark.usefixtures('sm_env'							)
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]							)
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

				"""simple docstring"""

				if self.framework == "pytorch":
							subprocess.run(
							    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()       ,			encoding="utf-8"       ,			check=_SCREAMING_SNAKE_CASE       ,			)
				assert hasattr(self       ,			"env")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Any=1)				->       Dict:

				"""simple docstring"""

				return HuggingFace(
				    entry_point=self.script       ,			source_dir=self.env.test_path       ,			role=self.env.role       ,			image_uri=self.env.image_uri       ,			base_job_name=F"""{self.env.base_job_name}-single"""       ,			instance_count=_SCREAMING_SNAKE_CASE       ,			instance_type=self.instance_type       ,			debugger_hook_config=_SCREAMING_SNAKE_CASE       ,			hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,			metric_definitions=self.env.metric_definitions       ,			py_version="py36"       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator()

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Tuple												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Optional[Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
				__lowerCAmelCase      :  Union[str, Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__lowerCAmelCase      :  Tuple												=       (
				    Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds"       ,			99_9999)
				)

				# assert kpis
				assert train_runtime <= self.results["train_runtime"]
				assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
				assert all(t <= self.results["eval_loss"] for t in eval_loss)

				# dump tests result into json file to share in PR
				with open(F"""{estimator.latest_training_job.name}.json"""       ,			"w") as outfile:
							json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}       ,			_SCREAMING_SNAKE_CASE)