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






						'''simple docstring'''

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

						return flax_key_tuple, flax_tensor



def 		UpperCamelCase     (       _UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   Tuple ,			_UpperCAmelCase  :   List[str]  )   ->			Dict:






						'''simple docstring'''

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

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

						if "kvstore/path" in layer:
												_lowercase       :	Optional[int]           =   f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
						elif "kvstore/driver" in layer:
												_lowercase       :	Tuple           =   "file"
						else:
												_lowercase       :	int           =   checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Union[str, Any]  )   ->			List[str]:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   rename_keys(_UpperCAmelCase  )
						_lowercase       :	Any           =   {}
						for k, v in current_block.items():
												_lowercase       :	Optional[int]           =   v
						_lowercase       :	List[str]           =   new_current_block
						torch.save(_UpperCAmelCase ,			_UpperCAmelCase  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   int ,			_UpperCAmelCase  :   str = WEIGHTS_NAME  )   ->			int:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   convert_file_size_to_int(_UpperCAmelCase  )
						_lowercase       :	str           =   []
						_lowercase       :	Optional[Any]           =   {}
						_lowercase       :	Any           =   0
						_lowercase       :	Any           =   0

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

						_lowercase       :	Optional[int]           =   {}
						for layer in checkpoint_info.keys():
												_lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   get_key_and_tensorstore_dict(
												    _UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )
												if curr_real_layer_name in all_layers:
																		_lowercase       :	List[str]           =   content
												else:
																		_lowercase       :	str           =   {split_layer[-1]: content}

						for key in all_layers.keys():
												# open tensorstore file
												_lowercase       :	Tuple           =   ts.open(unflatten_dict(all_layers[key]  )  ).result().read().result()
												_lowercase       :	Union[str, Any]           =   torch.tensor(_UpperCAmelCase  )
												_lowercase       :	Dict           =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype  )

												# use the renaming pattern from the small conversion scripts
												_lowercase      ,    _lowercase       :	Any           =   rename_base_flax_keys(tuple(key.split("/"  )  ) ,			_UpperCAmelCase  )
												_lowercase       :	List[str]           =   "/".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:
																		_lowercase       :	Optional[Any]           =   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
																		_lowercase       :	Tuple           =   {}
																		_lowercase       :	Tuple           =   0

												_lowercase       :	int           =   raw_weights.to(getattr(_UpperCAmelCase ,			_UpperCAmelCase  )  )
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						_lowercase       :	Dict           =   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
						_lowercase       :	str           =   {}
						_lowercase       :	List[Any]           =   {}
						for idx, shard in enumerate(_UpperCAmelCase  ):
												_lowercase       :	Tuple           =   weights_name.replace(
												    ".bin" ,			f"""-{idx+1:05d}-of-{len(_UpperCAmelCase  ):05d}.bin"""  )  # len(sharded_state_dicts):05d}
												_lowercase       :	str           =   os.path.join(_UpperCAmelCase ,			weights_name.replace(".bin" ,			f"""-{idx+1:05d}-of-???.bin"""  )  )
												os.rename(_UpperCAmelCase ,			os.path.join(_UpperCAmelCase ,			_UpperCAmelCase  )  )
												_lowercase       :	Optional[int]           =   shard
												for key in shard:
																		_lowercase       :	str           =   shard_file

    # Add the metadata
						_lowercase       :	List[Any]           =   {"total_size": total_size}
						_lowercase       :	Tuple           =   {"metadata": metadata, "weight_map": weight_map}

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

						return metadata, index


if __name__ == "__main__":
							UpperCamelCase_						:     Optional[Any]	       =							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_						:     List[Any]	       =							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     (       )   ->			int:






						'''simple docstring'''

						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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






from __future__ import annotations

import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import numpy
							import tensorflow as tf

							from transformers import (
							    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    BertConfig,
							    DPRConfig,
							    TFDPRContextEncoder,
							    TFDPRQuestionEncoder,
							    TFDPRReader,
							)




class   __lowercase						:






	def __init__(self				: Dict      ,	snake_case				: Dict      ,	snake_case				: List[Any]=13      ,	snake_case				: int=7      ,	snake_case				: List[str]=True      ,	snake_case				: List[Any]=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Dict=True      ,	snake_case				: Optional[Any]=99      ,	snake_case				: str=32      ,	snake_case				: Any=2      ,	snake_case				: Optional[Any]=4      ,	snake_case				: int=37      ,	snake_case				: Dict="gelu"      ,	snake_case				: Dict=0.1      ,	snake_case				: Dict=0.1      ,	snake_case				: Optional[int]=512      ,	snake_case				: Dict=16      ,	snake_case				: Union[str, Any]=2      ,	snake_case				: Dict=0.02      ,	snake_case				: str=3      ,	snake_case				: str=4      ,	snake_case				: Optional[Any]=None      ,	snake_case				: str=0      ,	) ->			Optional[Any]:
							_lowercase       :	int           =   parent
							_lowercase       :	Tuple           =   batch_size
							_lowercase       :	str           =   seq_length
							_lowercase       :	Optional[Any]           =   is_training
							_lowercase       :	List[Any]           =   use_input_mask
							_lowercase       :	Tuple           =   use_token_type_ids
							_lowercase       :	List[Any]           =   use_labels
							_lowercase       :	List[str]           =   vocab_size
							_lowercase       :	Dict           =   hidden_size
							_lowercase       :	str           =   num_hidden_layers
							_lowercase       :	Tuple           =   num_attention_heads
							_lowercase       :	Dict           =   intermediate_size
							_lowercase       :	List[Any]           =   hidden_act
							_lowercase       :	Optional[Any]           =   hidden_dropout_prob
							_lowercase       :	Dict           =   attention_probs_dropout_prob
							_lowercase       :	int           =   max_position_embeddings
							_lowercase       :	Tuple           =   type_vocab_size
							_lowercase       :	List[str]           =   type_sequence_label_size
							_lowercase       :	str           =   initializer_range
							_lowercase       :	Tuple           =   num_labels
							_lowercase       :	List[Any]           =   num_choices
							_lowercase       :	Dict           =   scope
							_lowercase       :	Optional[Any]           =   projection_dim






	def       _a(self				: Tuple    ) ->			List[str]:
							_lowercase       :	Optional[int]           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size    )

							_lowercase       :	Optional[int]           =   None
							if self.use_input_mask:
													# follow test_modeling_tf_ctrl.py
													_lowercase       :	List[Any]           =   random_attention_mask([self.batch_size, self.seq_length]    )

							_lowercase       :	List[Any]           =   None
							if self.use_token_type_ids:
													_lowercase       :	List[Any]           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size    )

							_lowercase       :	Tuple           =   None
							_lowercase       :	Optional[Any]           =   None
							_lowercase       :	Optional[Any]           =   None
							if self.use_labels:
													_lowercase       :	Optional[Any]           =   ids_tensor([self.batch_size]      ,	self.type_sequence_label_size    )
													_lowercase       :	Union[str, Any]           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.num_labels    )
													_lowercase       :	List[str]           =   ids_tensor([self.batch_size]      ,	self.num_choices    )

							_lowercase       :	Union[str, Any]           =   BertConfig(
							    vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	type_vocab_size=self.type_vocab_size      ,	is_decoder=snake_case      ,	initializer_range=self.initializer_range      ,	)
							_lowercase       :	Any           =   DPRConfig(projection_dim=self.projection_dim      ,	**config.to_dict()    )

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






	def       _a(self				: Union[str, Any]      ,	snake_case				: Any      ,	snake_case				: Tuple      ,	snake_case				: str      ,	snake_case				: str      ,	snake_case				: Union[str, Any]      ,	snake_case				: Tuple      ,	snake_case				: str    ) ->			List[str]:
							_lowercase       :	List[Any]           =   TFDPRContextEncoder(config=snake_case    )
							_lowercase       :	Dict           =   model(snake_case      ,	attention_mask=snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	Optional[int]           =   model(snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	Union[str, Any]           =   model(snake_case    )
							self.parent.assertEqual(result.pooler_output.shape      ,	(self.batch_size, self.projection_dim or self.hidden_size)    )






	def       _a(self				: List[Any]      ,	snake_case				: List[str]      ,	snake_case				: Tuple      ,	snake_case				: Any      ,	snake_case				: int      ,	snake_case				: Optional[int]      ,	snake_case				: Any      ,	snake_case				: Union[str, Any]    ) ->			int:
							_lowercase       :	Optional[Any]           =   TFDPRQuestionEncoder(config=snake_case    )
							_lowercase       :	int           =   model(snake_case      ,	attention_mask=snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	str           =   model(snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	str           =   model(snake_case    )
							self.parent.assertEqual(result.pooler_output.shape      ,	(self.batch_size, self.projection_dim or self.hidden_size)    )






	def       _a(self				: Union[str, Any]      ,	snake_case				: Any      ,	snake_case				: int      ,	snake_case				: List[str]      ,	snake_case				: Union[str, Any]      ,	snake_case				: Tuple      ,	snake_case				: Optional[Any]      ,	snake_case				: Union[str, Any]    ) ->			Any:
							_lowercase       :	Any           =   TFDPRReader(config=snake_case    )
							_lowercase       :	Optional[Any]           =   model(snake_case      ,	attention_mask=snake_case    )

							self.parent.assertEqual(result.start_logits.shape      ,	(self.batch_size, self.seq_length)    )
							self.parent.assertEqual(result.end_logits.shape      ,	(self.batch_size, self.seq_length)    )
							self.parent.assertEqual(result.relevance_logits.shape      ,	(self.batch_size,)    )






	def       _a(self				: int    ) ->			Optional[Any]:
							_lowercase       :	Union[str, Any]           =   self.prepare_config_and_inputs()
							(
							    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    
							)       :	int           =   config_and_inputs
							_lowercase       :	int           =   {"input_ids": input_ids}
							return config, inputs_dict




@require_tf
class   __lowercase						(						__snake_case   , __snake_case   , unittest.TestCase       ):
	_A       =   (
	    (
	        TFDPRContextEncoder,
	        TFDPRQuestionEncoder,
	        TFDPRReader,
	    )
	    if is_tf_available()
	    else ()
	)
	_A       =   {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   False






	def       _a(self				: str    ) ->			List[str]:
							_lowercase       :	List[Any]           =   TFDPRModelTester(self    )
							_lowercase       :	Any           =   ConfigTester(self      ,	config_class=snake_case      ,	hidden_size=37    )






	def       _a(self				: str    ) ->			Union[str, Any]:
							self.config_tester.run_common_tests()






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	List[str]           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_context_encoder(*snake_case    )






	def       _a(self				: Dict    ) ->			Optional[Any]:
							_lowercase       :	str           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_question_encoder(*snake_case    )






	def       _a(self				: List[Any]    ) ->			Any:
							_lowercase       :	Optional[Any]           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_reader(*snake_case    )






	@slow
	def       _a(self				: int    ) ->			str:
							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	str           =   TFDPRContextEncoder.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )

							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	List[str]           =   TFDPRContextEncoder.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )

							for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	int           =   TFDPRQuestionEncoder.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )

							for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	Any           =   TFDPRReader.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )




@require_tf
class   __lowercase						(						unittest.TestCase       ):






	@slow
	def       _a(self				: Dict    ) ->			Any:
							_lowercase       :	Any           =   TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"    )

							_lowercase       :	Optional[int]           =   tf.constant(
							    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]    )  # [CLS] hello, is my dog cute? [SEP]
							_lowercase       :	List[Any]           =   model(snake_case    )[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							_lowercase       :	Optional[Any]           =   tf.constant(
							    [
							        [
							            0.03_23_62_53,
							            0.12_75_33_35,
							            0.16_81_85_09,
							            0.00_27_97_86,
							            0.3_89_69_33,
							            0.24_26_49_45,
							            0.2_17_89_71,
							            -0.02_33_52_27,
							            -0.08_48_19_59,
							            -0.14_32_41_17,
							        ]
							    ]    )
							self.assertTrue(numpy.allclose(output[:, :10].numpy()      ,	expected_slice.numpy()      ,	atol=1e-4    )    )




