




'''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 						lowerCamelCase__   (				a    ,	a							):
						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												__snake_case						 =   flax_key_tuple[:-1] + ('weight',)
												__snake_case						 =   torch.permute(a    ,	(0, 2, 1)							)
						elif flax_key_tuple[-1] == "kernel" and ".".join(a							):
												# linear layer
												__snake_case						 =   flax_key_tuple[:-1] + ('weight',)
												__snake_case						 =   flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												__snake_case						 =   flax_key_tuple[:-1] + ('weight',)

						return flax_key_tuple, flax_tensor
def 						lowerCamelCase__   (				a    ,	a    ,	a							):
						if "metadata" in layer:
												__snake_case						 =   layer.split('metadata'							)
												__snake_case						 =   ''.join(split_layer[0]							)[:-1]
												__snake_case						 =   [tuple(('metadata' + split_layer[1]).split('/'							)							)]
						elif "kvstore" in layer:
												__snake_case						 =   layer.split('kvstore'							)
												__snake_case						 =   ''.join(split_layer[0]							)[:-1]
												__snake_case						 =   [tuple(('kvstore' + split_layer[1]).split('/'							)							)]

						else:
												__snake_case						 =   layer.split('/'							)
												__snake_case						 =   '/'.join(split_layer[:-1]							)
												__snake_case						 =   (split_layer[-1],)

						if "kvstore/path" in layer:
												__snake_case						 =   f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
						elif "kvstore/driver" in layer:
												__snake_case						 =   'file'
						else:
												__snake_case						 =   checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   rename_keys(a							)
						__snake_case						 =   {}
						for k, v in current_block.items():
												__snake_case						 =   v
						__snake_case						 =   new_current_block
						torch.save(a    ,	a							)
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a = WEIGHTS_NAME							):
						__snake_case						 =   convert_file_size_to_int(a							)
						__snake_case						 =   []
						__snake_case						 =   {}
						__snake_case						 =   0
						__snake_case						 =   0

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

						__snake_case						 =   {}
						for layer in checkpoint_info.keys():
												__snake_case ,       __snake_case ,       __snake_case						 =   get_key_and_tensorstore_dict(
												    a    ,	a    ,	a							)
												if curr_real_layer_name in all_layers:
																		__snake_case						 =   content
												else:
																		__snake_case						 =   {split_layer[-1]: content}

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

												# use the renaming pattern from the small conversion scripts
												__snake_case ,       __snake_case						 =   rename_base_flax_keys(tuple(key.split('/'							)							)    ,	a							)
												__snake_case						 =   '/'.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:
																		__snake_case						 =   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
																		__snake_case						 =   {}
																		__snake_case						 =   0

												__snake_case						 =   raw_weights.to(getattr(a    ,	a							)							)
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						__snake_case						 =   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
						__snake_case						 =   {}
						__snake_case						 =   {}
						for idx, shard in enumerate(a							):
												__snake_case						 =   weights_name.replace(
												    '.bin'    ,	f'-{idx+1:05d}-of-{len(a							):05d}.bin'							)  # len(sharded_state_dicts):05d}
												__snake_case						 =   os.path.join(a    ,	weights_name.replace('.bin'    ,	f'-{idx+1:05d}-of-???.bin'							)							)
												os.rename(a    ,	os.path.join(a    ,	a							)							)
												__snake_case						 =   shard
												for key in shard:
																		__snake_case						 =   shard_file

    # Add the metadata
						__snake_case						 =   {'total_size': total_size}
						__snake_case						 =   {'metadata': metadata, 'weight_map': weight_map}

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

						return metadata, index


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






def 						lowerCamelCase__   (				):
						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

						__snake_case						 =   SwitchTransformersConfig.from_pretrained('google/switch-base-8'							)
						config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'							)
						__snake_case						 =   SwitchTransformersForConditionalGeneration.from_pretrained(
						    '/home/arthur_huggingface_co/transformers/switch_converted'    ,	device_map='auto'							)

						__snake_case						 =   TaTokenizer.from_pretrained('t5-small'							)
						__snake_case						 =   'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

						__snake_case						 =   tokenizer(a    ,	return_tensors='pt'							).input_ids
						__snake_case						 =   model.generate(a    ,	decoder_start_token_id=0							)
						print(tokenizer.decode(out[0]							)							)







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowercase  =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
_lowercase  =			"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""
class 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir       ,		'models/bert/'      )      )
							__snake_case						 =   self.transformer_dir
							shutil.copy(
							    os.path.join(__lowerCAmelCase       ,		'src/transformers/models/bert/modeling_bert.py'      )       ,		os.path.join(self.transformer_dir       ,		'models/bert/modeling_bert.py'      )       ,		)




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'src/transformers'
							shutil.rmtree(self.transformer_dir      )




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[str]=None      ):
							__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + class_code
							if overwrite_result is not None:
													__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
							__snake_case						 =   black.Mode(target_versions={black.TargetVersion.PYaa}       ,		line_length=1_1_9      )
							__snake_case						 =   black.format_str(__lowerCAmelCase       ,		mode=__lowerCAmelCase      )
							__snake_case						 =   os.path.join(self.transformer_dir       ,		'new_code.py'      )
							with open(__lowerCAmelCase       ,		'w'       ,		newline='\n'      ) as f:
													f.write(__lowerCAmelCase      )
							if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(__lowerCAmelCase      )      ) == 0      )
							else:
													check_copies.is_copy_consistent(f.name       ,		overwrite=__lowerCAmelCase      )
													with open(__lowerCAmelCase       ,		'r'      ) as f:
																			self.assertTrue(f.read()       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							# Base copy consistency
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,		'BertLMPredictionHead'       ,		REFERENCE_CODE + '\n'       ,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,		'BertLMPredictionHead'       ,		__lowerCAmelCase       ,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,		'TestModelLMPredictionHead'       ,		re.sub('Bert'       ,		'TestModel'       ,		__lowerCAmelCase      )       ,		)

							# Copy consistency with a really long name
							__snake_case						 =   'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'       ,		F'{long_class_name}LMPredictionHead'       ,		re.sub('Bert'       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )       ,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,		'TestModelLMPredictionHead'       ,		__lowerCAmelCase       ,		overwrite_result=re.sub('Bert'       ,		'TestModel'       ,		__lowerCAmelCase      )       ,		)




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   check_copies.LOCALIZED_READMES['README_zh-hans.md']

							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
							    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
							    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
							    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
							    ' Luong, Quoc V. Le, Christopher D. Manning.'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
							    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
							    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
							    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
							    ' Christopher D. Manning 发布。\n'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							self.assertFalse(__lowerCAmelCase      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							# Check whether the number of models is equal to README.md after conversion.
							self.assertTrue(__lowerCAmelCase      )

							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
							    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							# Check if the model link is synchronized.
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )

