


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__	:    Union[str, Any]							,				UpperCamelCase__	:    List[str]		):





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

       return flax_key_tuple, flax_tensor
def 				_UpperCAmelCase							(UpperCamelCase__	:    Dict							,				UpperCamelCase__	:    Optional[Any]							,				UpperCamelCase__	:    List[Any]		):





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

       else:
              _A :			int							       =					layer.split("/"		)
              _A :			int							       =					"/".join(split_layer[:-1]		)
              _A :			str							       =					(split_layer[-1],)

       if "kvstore/path" in layer:
              _A :			Union[str, Any]							       =					f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
       elif "kvstore/driver" in layer:
              _A :			List[str]							       =					"file"
       else:
              _A :			Optional[Any]							       =					checkpoint_info[layer]

       return curr_real_layer_name, split_layer, content
def 				_UpperCAmelCase							(UpperCamelCase__	:    List[str]							,				UpperCamelCase__	:    List[str]		):
       _A :			int							       =					rename_keys(_A		)
       _A :			List[Any]							       =					{}
       for k, v in current_block.items():
              _A :			List[str]							       =					v
       _A :			Optional[int]							       =					new_current_block
       torch.save(_A							,				_A		)
def 				_UpperCAmelCase							(UpperCamelCase__	:    Any							,				UpperCamelCase__	:    Union[str, Any]							,				UpperCamelCase__	:    Tuple							,				UpperCamelCase__	:    Tuple							,				UpperCamelCase__	:    Optional[Any] = WEIGHTS_NAME		):
       _A :			str							       =					convert_file_size_to_int(_A		)
       _A :			Optional[int]							       =					[]
       _A :			Optional[Any]							       =					{}
       _A :			List[Any]							       =					0
       _A :			List[Any]							       =					0

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

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

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

              # use the renaming pattern from the small conversion scripts
              _A       ,     _A :			Union[str, Any]							       =					rename_base_flax_keys(tuple(key.split("/"		)		)							,				_A		)
              _A :			Optional[Any]							       =					"/".join(_A		)

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

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

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

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

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

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

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

       return metadata, index


if __name__ == "__main__":
    lowerCAmelCase__ 						=					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.',
    )
    lowerCAmelCase__ 						=					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

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

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

       _A :			List[str]							       =					tokenizer(_A							,				return_tensors="pt"		).input_ids
       _A :			int							       =					model.generate(_A							,				decoder_start_token_id=0		)
       print(tokenizer.decode(out[0]		)		)






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Dict			,      __lowercase				:	Any			,      __lowercase				:	str=13			,      __lowercase				:	Optional[Any]=7			,      __lowercase				:	Tuple=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	List[str]=True			,      __lowercase				:	Dict=True			,      __lowercase				:	Any=99			,      __lowercase				:	Any=32			,      __lowercase				:	List[Any]=5			,      __lowercase				:	Any=4			,      __lowercase				:	List[str]=37			,      __lowercase				:	Union[str, Any]="gelu"			,      __lowercase				:	Union[str, Any]=0.1			,      __lowercase				:	str=0.1			,      __lowercase				:	List[str]=5_12			,      __lowercase				:	Tuple=16			,      __lowercase				:	List[str]=2			,      __lowercase				:	Tuple=0.02			,      __lowercase				:	Optional[Any]=False			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	Union[str, Any]="None"			,      __lowercase				:	Union[str, Any]=3			,      __lowercase				:	int=4			,      __lowercase				:	Tuple=None			,      ):






							"""simple docstring"""






							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	seq_length
							snake_case_						       =	is_training
							snake_case_						       =	use_input_mask
							snake_case_						       =	use_token_type_ids
							snake_case_						       =	use_labels
							snake_case_						       =	vocab_size
							snake_case_						       =	hidden_size
							snake_case_						       =	num_hidden_layers
							snake_case_						       =	num_attention_heads
							snake_case_						       =	intermediate_size
							snake_case_						       =	hidden_act
							snake_case_						       =	hidden_dropout_prob
							snake_case_						       =	attention_probs_dropout_prob
							snake_case_						       =	max_position_embeddings
							snake_case_						       =	type_vocab_size
							snake_case_						       =	type_sequence_label_size
							snake_case_						       =	initializer_range
							snake_case_						       =	num_labels
							snake_case_						       =	num_choices
							snake_case_						       =	relative_attention
							snake_case_						       =	position_biased_input
							snake_case_						       =	pos_att_type
							snake_case_						       =	scope






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size		)

							snake_case_						       =	None
							if self.use_input_mask:
								snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      vocab_size=2		)

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

							snake_case_						       =	None
							snake_case_						       =	None
							snake_case_						       =	None
							if self.use_labels:
								snake_case_						       =	ids_tensor([self.batch_size]			,      self.type_sequence_label_size		)
								snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.num_labels		)
								snake_case_						       =	ids_tensor([self.batch_size]			,      self.num_choices		)

							snake_case_						       =	self.get_config()

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






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							return DebertaConfig(
							    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			,      initializer_range=self.initializer_range			,      relative_attention=self.relative_attention			,      position_biased_input=self.position_biased_input			,      pos_att_type=self.pos_att_type			,      )






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.get_config()
							snake_case_						       =	3_00
							return config






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							self.parent.assertListEqual(list(result.loss.size()		)			,      []		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Tuple			,      __lowercase				:	Dict			,      __lowercase				:	str			,      __lowercase				:	Optional[int]			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	DebertaModel(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase		)[0]

							self.parent.assertListEqual(list(sequence_output.size()		)			,      [self.batch_size, self.seq_length, self.hidden_size]		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Any			,      __lowercase				:	Dict			,      __lowercase				:	Any			,      __lowercase				:	Any			,      __lowercase				:	Tuple			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	DebertaForMaskedLM(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)

							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size)		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	List[str]			,      __lowercase				:	Dict			,      __lowercase				:	str			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForSequenceClassification(__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertListEqual(list(result.logits.size()		)			,      [self.batch_size, self.num_labels]		)
							self.check_loss_output(__lowercase		)






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[str]			,      __lowercase				:	Any			,      __lowercase				:	List[Any]			,      __lowercase				:	str			,      __lowercase				:	List[str]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForTokenClassification(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels)		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	str			,      __lowercase				:	List[str]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	DebertaForQuestionAnswering(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(
							    __lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      start_positions=__lowercase			,      end_positions=__lowercase			,      )
							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)		)






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	config_and_inputs
							snake_case_						       =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (
						    (
						        DebertaModel,
						        DebertaForMaskedLM,
						        DebertaForSequenceClassification,
						        DebertaForTokenClassification,
						        DebertaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase_							=  (
						    {
						        '''feature-extraction''': DebertaModel,
						        '''fill-mask''': DebertaForMaskedLM,
						        '''question-answering''': DebertaForQuestionAnswering,
						        '''text-classification''': DebertaForSequenceClassification,
						        '''token-classification''': DebertaForTokenClassification,
						        '''zero-shot''': DebertaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase_							=  True
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	DebertaModelTester(self		)
							snake_case_						       =	ConfigTester(self			,      config_class=__lowercase			,      hidden_size=37		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*__lowercase		)






						@slow
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						       =	DebertaModel.from_pretrained(__lowercase		)
								self.assertIsNotNone(__lowercase		)







@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						@unittest.skip(reason="Model not available yet"		)
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							pass






						@slow
						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	DebertaModel.from_pretrained("microsoft/deberta-base"		)

							snake_case_						       =	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]		)
							snake_case_						       =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
							with torch.no_grad():
								snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase		)[0]
							# compare the actual values for a slice.
							snake_case_						       =	torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]		)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,      __lowercase			,      atol=1E-4		)			,      f"{output[:, 1:4, 1:4]}"		)
