


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
								_SCREAMING_SNAKE_CASE			  =flax_key_tuple[:-1] + ('''weight''',)
								_SCREAMING_SNAKE_CASE			  =torch.permute(a__	,(0, 2, 1))
				elif flax_key_tuple[-1] == "kernel" and ".".join(a__):
								# linear layer
								_SCREAMING_SNAKE_CASE			  =flax_key_tuple[:-1] + ('''weight''',)
								_SCREAMING_SNAKE_CASE			  =flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								_SCREAMING_SNAKE_CASE			  =flax_key_tuple[:-1] + ('''weight''',)

				return flax_key_tuple, flax_tensor







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

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

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

				return curr_real_layer_name, split_layer, content







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =rename_keys(a__)
				_SCREAMING_SNAKE_CASE			  ={}
				for k, v in current_block.items():
								_SCREAMING_SNAKE_CASE			  =v
				_SCREAMING_SNAKE_CASE			  =new_current_block
				torch.save(a__	,a__)







def 						lowerCamelCase(   a__	,a__	,a__	,a__	,a__ = WEIGHTS_NAME):
				_SCREAMING_SNAKE_CASE			  =convert_file_size_to_int(a__)
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  ={}
				_SCREAMING_SNAKE_CASE			  =0
				_SCREAMING_SNAKE_CASE			  =0

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

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

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

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

								_SCREAMING_SNAKE_CASE			  =raw_weights.to(getattr(a__	,a__))
								current_block_size += weight_size
								total_size += weight_size

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

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

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

				return metadata, index


if __name__ == "__main__":
							snake_case_						:			List[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_						:			Dict													=							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

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

				_SCREAMING_SNAKE_CASE			  =TaTokenizer.from_pretrained('''t5-small''')
				_SCREAMING_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>.'''

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



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

snake_case_						:			Union[str, Any]													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__):
				if isinstance(a__	,(list, tuple)) and isinstance(videos[0]	,(list, tuple)) and is_valid_image(videos[0][0]):
								return videos

				elif isinstance(a__	,(list, tuple)) and is_valid_image(videos[0]):
								return [videos]

				elif is_valid_image(a__):
								return [[videos]]

				raise ValueError(f"Could not make batched video from {videos}")


class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["pixel_values"]




						def __init__(       self					:      Tuple				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      bool = True				,    _a					:      Union[int, float] = 1 / 255				,    _a					:      bool = True				,    _a					:      bool = True				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    **_a					:      Any				,    )							->     None:


										"""simple docstring"""





										super().__init__(**_a  )
										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''shortest_edge''': 256}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										_SCREAMING_SNAKE_CASE			  =do_resize
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_center_crop
										_SCREAMING_SNAKE_CASE			  =crop_size
										_SCREAMING_SNAKE_CASE			  =resample
										_SCREAMING_SNAKE_CASE			  =do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_SCREAMING_SNAKE_CASE			  =image_std if image_std is not None else IMAGENET_STANDARD_STD




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[Any]				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										if "shortest_edge" in size:
														_SCREAMING_SNAKE_CASE			  =get_resize_output_image_size(_a				,    size['''shortest_edge''']				,    default_to_square=_a  )
										elif "height" in size and "width" in size:
														_SCREAMING_SNAKE_CASE			  =(size['''height'''], size['''width'''])
										else:
														raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
										return resize(_a				,    size=_a				,    resample=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Dict				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a  )
										if "height" not in size or "width" not in size:
														raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
										return center_crop(_a				,    size=(size['''height'''], size['''width'''])				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      np.ndarray				,    _a					:      Union[int, float]				,    _a					:      bool = True				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[str]				,    )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =image.astype(np.floataa  )
										if offset:
														_SCREAMING_SNAKE_CASE			  =image - (scale / 2)
										return rescale(_a				,    scale=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      np.ndarray				,    _a					:      Union[float, List[float]]				,    _a					:      Union[float, List[float]]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Any				,    )							->     np.ndarray:


										"""simple docstring"""





										return normalize(_a				,    mean=_a				,    std=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[ChannelDimension] = ChannelDimension.FIRST				,    )							->     np.ndarray:


										"""simple docstring"""





										if do_resize and size is None or resample is None:
														raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

										if do_center_crop and crop_size is None:
														raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

										if do_rescale and rescale_factor is None:
														raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

										if offset and not do_rescale:
														raise ValueError('''For offset, do_rescale must also be set to True.'''  )

										# All transformations expect numpy arrays.
										_SCREAMING_SNAKE_CASE			  =to_numpy_array(_a  )

										if do_resize:
														_SCREAMING_SNAKE_CASE			  =self.resize(image=_a				,    size=_a				,    resample=_a  )

										if do_center_crop:
														_SCREAMING_SNAKE_CASE			  =self.center_crop(_a				,    size=_a  )

										if do_rescale:
														_SCREAMING_SNAKE_CASE			  =self.rescale(image=_a				,    scale=_a				,    offset=_a  )

										if do_normalize:
														_SCREAMING_SNAKE_CASE			  =self.normalize(image=_a				,    mean=_a				,    std=_a  )

										_SCREAMING_SNAKE_CASE			  =to_channel_dimension_format(_a				,    _a  )
										return image




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[str, TensorType]] = None				,    _a					:      ChannelDimension = ChannelDimension.FIRST				,    **_a					:      str				,    )							->     PIL.Image.Image:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE			  =resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE			  =do_center_crop if do_center_crop is not None else self.do_center_crop
										_SCREAMING_SNAKE_CASE			  =do_rescale if do_rescale is not None else self.do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor if rescale_factor is not None else self.rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset if offset is not None else self.offset
										_SCREAMING_SNAKE_CASE			  =do_normalize if do_normalize is not None else self.do_normalize
										_SCREAMING_SNAKE_CASE			  =image_mean if image_mean is not None else self.image_mean
										_SCREAMING_SNAKE_CASE			  =image_std if image_std is not None else self.image_std

										_SCREAMING_SNAKE_CASE			  =size if size is not None else self.size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else self.crop_size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										if not valid_images(_a  ):
														raise ValueError(
														    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
														    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

										_SCREAMING_SNAKE_CASE			  =make_batched(_a  )

										_SCREAMING_SNAKE_CASE			  =[
										    [
										        self._preprocess_image(
										            image=_a				,    do_resize=_a				,    size=_a				,    resample=_a				,    do_center_crop=_a				,    crop_size=_a				,    do_rescale=_a				,    rescale_factor=_a				,    offset=_a				,    do_normalize=_a				,    image_mean=_a				,    image_std=_a				,    data_format=_a				,    )
										        for img in video
										    ]
										    for video in videos
										]

										_SCREAMING_SNAKE_CASE			  ={'''pixel_values''': videos}
										return BatchFeature(data=_a				,    tensor_type=_a  )