




"""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							)			->			Union[str, Any]:


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

			return flax_key_tuple, flax_tensor

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[str]:


			"""simple docstring"""
			if "metadata" in layer:
						__UpperCAmelCase	: Any		=					layer.split("metadata"							)
						__UpperCAmelCase	: Dict		=					"".join(split_layer[0]							)[:-1]
						__UpperCAmelCase	: str		=					[tuple(("metadata" + split_layer[1]).split("/"							)							)]
			elif "kvstore" in layer:
						__UpperCAmelCase	: Tuple		=					layer.split("kvstore"							)
						__UpperCAmelCase	: Dict		=					"".join(split_layer[0]							)[:-1]
						__UpperCAmelCase	: List[Any]		=					[tuple(("kvstore" + split_layer[1]).split("/"							)							)]

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

			if "kvstore/path" in layer:
						__UpperCAmelCase	: Any		=					f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
			elif "kvstore/driver" in layer:
						__UpperCAmelCase	: Tuple		=					"file"
			else:
						__UpperCAmelCase	: Dict		=					checkpoint_info[layer]

			return curr_real_layer_name, split_layer, content

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			__UpperCAmelCase	: Any		=					rename_keys(UpperCamelCase							)
			__UpperCAmelCase	: Optional[int]		=					{}
			for k, v in current_block.items():
						__UpperCAmelCase	: Optional[Any]		=					v
			__UpperCAmelCase	: Union[str, Any]		=					new_current_block
			torch.save(UpperCamelCase				,       UpperCamelCase							)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase = WEIGHTS_NAME							)			->			Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					convert_file_size_to_int(UpperCamelCase							)
			__UpperCAmelCase	: str		=					[]
			__UpperCAmelCase	: str		=					{}
			__UpperCAmelCase	: Optional[int]		=					0
			__UpperCAmelCase	: Dict		=					0

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

			__UpperCAmelCase	: List[Any]		=					{}
			for layer in checkpoint_info.keys():
						__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: List[Any]		=					get_key_and_tensorstore_dict(
						    UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						if curr_real_layer_name in all_layers:
									__UpperCAmelCase	: Dict		=					content
						else:
									__UpperCAmelCase	: Dict		=					{split_layer[-1]: content}

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

						# use the renaming pattern from the small conversion scripts
						__UpperCAmelCase	,     __UpperCAmelCase	: int		=					rename_base_flax_keys(tuple(key.split("/"							)							)				,       UpperCamelCase							)
						__UpperCAmelCase	: int		=					"/".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	: List[str]		=					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	: Dict		=					{}
									__UpperCAmelCase	: List[str]		=					0

						__UpperCAmelCase	: Any		=					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	: List[Any]		=					{}
			__UpperCAmelCase	: List[str]		=					{}
			for idx, shard in enumerate(UpperCamelCase							):
						__UpperCAmelCase	: List[Any]		=					weights_name.replace(
						    ".bin"				,       f"-{idx+1:05d}-of-{len(UpperCamelCase							):05d}.bin"							)  # len(sharded_state_dicts):05d}
						__UpperCAmelCase	: Tuple		=					os.path.join(UpperCamelCase				,       weights_name.replace(".bin"				,       f"-{idx+1:05d}-of-???.bin"							)							)
						os.rename(UpperCamelCase				,       os.path.join(UpperCamelCase				,       UpperCamelCase							)							)
						__UpperCAmelCase	: Any		=					shard
						for key in shard:
									__UpperCAmelCase	: Union[str, Any]		=					shard_file

    # Add the metadata
			__UpperCAmelCase	: List[str]		=					{"total_size": total_size}
			__UpperCAmelCase	: Dict		=					{"metadata": metadata, "weight_map": weight_map}

			with open(os.path.join(UpperCamelCase				,       UpperCamelCase							)				,       "w"				,       encoding="utf-8"							) as f:
						__UpperCAmelCase	: Optional[int]		=					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    (			)			->			List[Any]:


			"""simple docstring"""
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

			__UpperCAmelCase	: List[Any]		=					tokenizer(UpperCamelCase				,       return_tensors="pt"							).input_ids
			__UpperCAmelCase	: Union[str, Any]		=					model.generate(UpperCamelCase				,       decoder_start_token_id=0							)
			print(tokenizer.decode(out[0]							)							)








"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					"laion/clap-htsat-unfused"
						__UpperCAmelCase	: List[str]		=					tempfile.mkdtemp()


			def       a_     (      self      :    Optional[Any]      ,  **UpperCamelCase_      :    Union[str, Any]):


						"""simple docstring"""




						return RobertaTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_tokenizer()
						__UpperCAmelCase	: str		=					self.get_feature_extractor()

						__UpperCAmelCase	: List[Any]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer.get_vocab())
						self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase_)

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor())
						processor.save_pretrained(self.tmpdirname)

						__UpperCAmelCase	: int		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")
						__UpperCAmelCase	: Any		=					self.get_feature_extractor(do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						__UpperCAmelCase	: Tuple		=					ClapProcessor.from_pretrained(
						    self.tmpdirname      ,  bos_token="(BOS)"      ,  eos_token="(EOS)"      ,  do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer_add_kwargs.get_vocab())
						self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase_)

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor_add_kwargs.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					floats_list((3, 1000))

						__UpperCAmelCase	: Tuple		=					feature_extractor(UpperCamelCase_      ,  return_tensors="np")
						__UpperCAmelCase	: Any		=					processor(audios=UpperCamelCase_      ,  return_tensors="np")

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,  input_processor[key].sum()      ,  delta=1e-2)


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_feature_extractor()
						__UpperCAmelCase	: Optional[int]		=					self.get_tokenizer()

						__UpperCAmelCase	: str		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					"This is a test string"

						__UpperCAmelCase	: List[Any]		=					processor(text=UpperCamelCase_)

						__UpperCAmelCase	: Dict		=					tokenizer(UpperCamelCase_)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]      ,  encoded_processor[key])


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.get_feature_extractor()
						__UpperCAmelCase	: List[str]		=					self.get_tokenizer()

						__UpperCAmelCase	: List[str]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__UpperCAmelCase	: int		=					processor.batch_decode(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					tokenizer.batch_decode(UpperCamelCase_)

						self.assertListEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: int		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						self.assertListEqual(
						    processor.model_input_names[2:]      ,  feature_extractor.model_input_names      ,  msg="`processor` and `feature_extractor` model input names do not match"      ,  )


