




'''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 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    str ):





							'''simple docstring'''


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

							return flax_key_tuple, flax_tensor
def 						snake_case_       (UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							if "metadata" in layer:
														_a      	=							layer.split('''metadata''' )
														_a      	=							''''''.join(split_layer[0] )[:-1]
														_a      	=							[tuple(('''metadata''' + split_layer[1]).split('''/''' ) )]
							elif "kvstore" in layer:
														_a      	=							layer.split('''kvstore''' )
														_a      	=							''''''.join(split_layer[0] )[:-1]
														_a      	=							[tuple(('''kvstore''' + split_layer[1]).split('''/''' ) )]

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

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

							return curr_real_layer_name, split_layer, content
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Optional[Any] ):





							'''simple docstring'''


							_a      	=							rename_keys(UpperCamelCase )
							_a      	=							{}
							for k, v in current_block.items():
														_a      	=							v
							_a      	=							new_current_block
							torch.save(UpperCamelCase					,		UpperCamelCase )
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    List[str]					,		UpperCamelCase			:    Tuple					,		UpperCamelCase			:    str = WEIGHTS_NAME ):





							'''simple docstring'''


							_a      	=							convert_file_size_to_int(UpperCamelCase )
							_a      	=							[]
							_a      	=							{}
							_a      	=							0
							_a      	=							0

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

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

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

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

														_a      	=							raw_weights.to(getattr(UpperCamelCase					,		UpperCamelCase ) )
														current_block_size += weight_size
														total_size += weight_size

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

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

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

							return metadata, index


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

def 						snake_case_       ():





							'''simple docstring'''


							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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







'''simple docstring'''


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							LEDTokenizer
							lowercase_							     =							LEDTokenizerFast
							lowercase_							     =							True







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														super().setUp()
														_a      	=							[
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''\u0120''',
														    '''\u0120l''',
														    '''\u0120n''',
														    '''\u0120lo''',
														    '''\u0120low''',
														    '''er''',
														    '''\u0120lowest''',
														    '''\u0120newer''',
														    '''\u0120wider''',
														    '''<unk>''',
														]
														_a      	=							dict(zip(lowerCAmelCase_				,     range(len(lowerCAmelCase_   )   )   )   )
														_a      	=							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
														_a      	=							{'''unk_token''': '''<unk>'''}

														_a      	=							os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES['''vocab_file''']   )
														_a      	=							os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES['''merges_file''']   )
														with open(self.vocab_file				,     '''w'''				,     encoding='''utf-8'''   ) as fp:
																					fp.write(json.dumps(lowerCAmelCase_   ) + '''\n'''   )
														with open(self.merges_file				,     '''w'''				,     encoding='''utf-8'''   ) as fp:
																					fp.write('''\n'''.join(lowerCAmelCase_   )   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     **lowerCAmelCase_			:    int   )     ->				Optional[int]:


														"""simple docstring"""


														kwargs.update(self.special_tokens_map   )
														return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Any   )     ->				int:


														"""simple docstring"""


														kwargs.update(self.special_tokens_map   )
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Dict   )     ->				List[str]:


														"""simple docstring"""


														return "lower newer", "lower newer"







							@cached_property
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''   )







							@cached_property
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Union[str, Any]:


														"""simple docstring"""


														return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    int   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
														_a      	=							[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     max_length=len(lowerCAmelCase_   )				,     padding=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )

																					self.assertEqual((2, 9)				,     batch.input_ids.shape   )
																					self.assertEqual((2, 9)				,     batch.attention_mask.shape   )
																					_a      	=							batch.input_ids.tolist()[0]
																					self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIn('''input_ids'''				,     lowerCAmelCase_   )
																					self.assertIn('''attention_mask'''				,     lowerCAmelCase_   )
																					self.assertNotIn('''labels'''				,     lowerCAmelCase_   )
																					self.assertNotIn('''decoder_attention_mask'''				,     lowerCAmelCase_   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				str:


														"""simple docstring"""


														_a      	=							[
														    '''Summary of the text.''',
														    '''Another summary.''',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(text_target=lowerCAmelCase_				,     max_length=32				,     padding='''max_length'''				,     return_tensors='''pt'''   )
																					self.assertEqual(32				,     targets['''input_ids'''].shape[1]   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Union[str, Any]:


														"""simple docstring"""


														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(
																					    ['''I am a small frog''' * 10_24, '''I am a small frog''']				,     padding=lowerCAmelCase_				,     truncation=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																					self.assertEqual(batch.input_ids.shape				,     (2, 51_22)   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''']
														_a      	=							[
														    '''Summary of the text.''',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     return_tensors='''pt'''   )
																					_a      	=							tokenizer(text_target=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					_a      	=							inputs['''input_ids''']
																					_a      	=							targets['''input_ids''']
																					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
																					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
																					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
																					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Union[str, Any]:


														"""simple docstring"""


														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							['''Summary of the text.''', '''Another summary.''']
																					_a      	=							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																					_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_   )
																					_a      	=							[[0] * len(lowerCAmelCase_   ) for x in encoded_output['''input_ids''']]
																					_a      	=							tokenizer.pad(lowerCAmelCase_   )
																					self.assertSequenceEqual(outputs['''global_attention_mask''']				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Dict:


														"""simple docstring"""


														pass







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																												_a      	=							self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																												_a      	=							self.tokenizer_class.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																												_a      	=							'''A, <mask> AllenNLP sentence.'''
																												_a      	=							tokenizer_r.encode_plus(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_   )
																												_a      	=							tokenizer_p.encode_plus(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_   )
																												self.assertEqual(sum(tokens_r['''token_type_ids''']   )				,     sum(tokens_p['''token_type_ids''']   )   )
																												self.assertEqual(
																												    sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )				,     sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )				,     )

																												_a      	=							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
																												_a      	=							tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )
																												self.assertSequenceEqual(tokens_p['''input_ids''']				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
																												self.assertSequenceEqual(tokens_r['''input_ids''']				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

																												self.assertSequenceEqual(
																												    lowerCAmelCase_				,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
																												self.assertSequenceEqual(
																												    lowerCAmelCase_				,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )

