
"""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				(		snake_case__   :     Optional[int]	,      snake_case__   :     Tuple     ):
	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		A						   =   flax_key_tuple[:-1] + ('weight',)
		A						   =   torch.permute(snake_case__	,      (0, 2, 1)     )
	elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__     ):
		# 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				(		snake_case__   :     List[str]	,      snake_case__   :     List[str]	,      snake_case__   :     Dict     ):
	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				(		snake_case__   :     List[Any]	,      snake_case__   :     List[str]     ):
	A						   =   rename_keys(snake_case__     )
	A						   =   {}
	for k, v in current_block.items():
		A						   =   v
	A						   =   new_current_block
	torch.save(snake_case__	,      snake_case__     )






def       _snake_case				(		snake_case__   :     Optional[Any]	,      snake_case__   :     int	,      snake_case__   :     Dict	,      snake_case__   :     Optional[Any]	,      snake_case__   :     str = WEIGHTS_NAME     ):
	A						   =   convert_file_size_to_int(snake_case__     )
	A						   =   []
	A						   =   {}
	A						   =   0
	A						   =   0

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

	A						   =   {}
	for layer in checkpoint_info.keys():
		A   ,      A   ,      A						   =   get_key_and_tensorstore_dict(
		    snake_case__	,      snake_case__	,      snake_case__     )
		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(snake_case__     )
		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('/'     )     )	,      snake_case__     )
		A						   =   '/'.join(snake_case__     )

		# 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(
			    snake_case__	,      weights_name.replace('.bin'	,      F'-{len(snake_case__     )+1:05d}-of-???.bin'     )     )
			rename_and_save_block(snake_case__	,      snake_case__     )
			sharded_state_dicts.append(current_block.keys()     )
			del current_block
			A						   =   {}
			A						   =   0

		A						   =   raw_weights.to(getattr(snake_case__	,      snake_case__     )     )
		current_block_size += weight_size
		total_size += weight_size

	# Add the last block
	A						   =   os.path.join(snake_case__	,      weights_name.replace('.bin'	,      F'-{len(snake_case__     )+1:05d}-of-???.bin'     )     )
	rename_and_save_block(snake_case__	,      snake_case__     )
	sharded_state_dicts.append(current_block.keys()     )

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

	# Otherwise, let's build the index
	A						   =   {}
	A						   =   {}
	for idx, shard in enumerate(snake_case__     ):
		A						   =   weights_name.replace(
		    '.bin'	,      F'-{idx+1:05d}-of-{len(snake_case__     ):05d}.bin'     )  # len(sharded_state_dicts):05d}
		A						   =   os.path.join(snake_case__	,      weights_name.replace('.bin'	,      F'-{idx+1:05d}-of-???.bin'     )     )
		os.rename(snake_case__	,      os.path.join(snake_case__	,      snake_case__     )     )
		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(snake_case__	,      snake_case__     )	,      'w'	,      encoding='utf-8'     ) as f:
		A						   =   json.dumps(snake_case__	,      indent=2	,      sort_keys=snake_case__     ) + '\n'
		f.write(snake_case__     )

	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       _snake_case				(		):
	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(snake_case__	,      return_tensors='pt'     ).input_ids
	A						   =   model.generate(snake_case__	,      decoder_start_token_id=0     )
	print(tokenizer.decode(out[0]     )     )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPTokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPFeatureExtractor''']
		_lowercase							   =				['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)