





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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Tuple:
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        lowerCamelCase__					:   Any      				=	flax_key_tuple[:-1] + ('weight',)
        lowerCamelCase__					:   Optional[Any]      				=	torch.permute(_UpperCAmelCase		,			(0, 2, 1)				)
    elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase				):
        # linear layer
        lowerCamelCase__					:   List[Any]      				=	flax_key_tuple[:-1] + ('weight',)
        lowerCamelCase__					:   str      				=	flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        lowerCamelCase__					:   Tuple      				=	flax_key_tuple[:-1] + ('weight',)

    return flax_key_tuple, flax_tensor


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  str:
    if "metadata" in layer:
        lowerCamelCase__					:   Any      				=	layer.split('metadata'				)
        lowerCamelCase__					:   Optional[Any]      				=	''.join(split_layer[0]				)[:-1]
        lowerCamelCase__					:   Optional[Any]      				=	[tuple(('metadata' + split_layer[1]).split('/'				)				)]
    elif "kvstore" in layer:
        lowerCamelCase__					:   List[Any]      				=	layer.split('kvstore'				)
        lowerCamelCase__					:   Optional[int]      				=	''.join(split_layer[0]				)[:-1]
        lowerCamelCase__					:   str      				=	[tuple(('kvstore' + split_layer[1]).split('/'				)				)]

    else:
        lowerCamelCase__					:   Union[str, Any]      				=	layer.split('/'				)
        lowerCamelCase__					:   Union[str, Any]      				=	'/'.join(split_layer[:-1]				)
        lowerCamelCase__					:   int      				=	(split_layer[-1],)

    if "kvstore/path" in layer:
        lowerCamelCase__					:   Dict      				=	F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
    elif "kvstore/driver" in layer:
        lowerCamelCase__					:   List[Any]      				=	'file'
    else:
        lowerCamelCase__					:   Optional[Any]      				=	checkpoint_info[layer]

    return curr_real_layer_name, split_layer, content


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__					:   Optional[Any]      				=	rename_keys(_UpperCAmelCase				)
    lowerCamelCase__					:   List[Any]      				=	{}
    for k, v in current_block.items():
        lowerCamelCase__					:   int      				=	v
    lowerCamelCase__					:   Tuple      				=	new_current_block
    torch.save(_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase = WEIGHTS_NAME				)    ->  List[Any]:
    lowerCamelCase__					:   Union[str, Any]      				=	convert_file_size_to_int(_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	[]
    lowerCamelCase__					:   List[Any]      				=	{}
    lowerCamelCase__					:   Dict      				=	0
    lowerCamelCase__					:   Dict      				=	0

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

    lowerCamelCase__					:   Optional[Any]      				=	{}
    for layer in checkpoint_info.keys():
        lowerCamelCase__					:   Any      				=	get_key_and_tensorstore_dict(
            _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)
        if curr_real_layer_name in all_layers:
            lowerCamelCase__					:   str      				=	content
        else:
            lowerCamelCase__					:   Union[str, Any]      				=	{split_layer[-1]: content}

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

        # use the renaming pattern from the small conversion scripts
        lowerCamelCase__					:   Any      				=	rename_base_flax_keys(tuple(key.split('/'				)				)		,			_UpperCAmelCase				)
        lowerCamelCase__					:   Optional[Any]      				=	'/'.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:
            lowerCamelCase__					:   Optional[Any]      				=	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
            lowerCamelCase__					:   Optional[Any]      				=	{}
            lowerCamelCase__					:   Optional[Any]      				=	0

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

    # Add the last block
    lowerCamelCase__					:   Dict      				=	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
    lowerCamelCase__					:   Tuple      				=	{}
    lowerCamelCase__					:   Union[str, Any]      				=	{}
    for idx, shard in enumerate(_UpperCAmelCase				):
        lowerCamelCase__					:   List[Any]      				=	weights_name.replace(
            '.bin'		,			F"""-{idx+1:05d}-of-{len(_UpperCAmelCase				):05d}.bin"""				)  # len(sharded_state_dicts):05d}
        lowerCamelCase__					:   List[Any]      				=	os.path.join(_UpperCAmelCase		,			weights_name.replace('.bin'		,			F"""-{idx+1:05d}-of-???.bin"""				)				)
        os.rename(_UpperCAmelCase		,			os.path.join(_UpperCAmelCase		,			_UpperCAmelCase				)				)
        lowerCamelCase__					:   str      				=	shard
        for key in shard:
            lowerCamelCase__					:   List[Any]      				=	shard_file

    # Add the metadata
    lowerCamelCase__					:   Dict      				=	{'total_size': total_size}
    lowerCamelCase__					:   List[str]      				=	{'metadata': metadata, 'weight_map': weight_map}

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

    return metadata, index


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


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    lowerCamelCase__					:   List[Any]      				=	SwitchTransformersConfig.from_pretrained('google/switch-base-8'				)
    config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'				)
    lowerCamelCase__					:   int      				=	SwitchTransformersForConditionalGeneration.from_pretrained(
        '/home/arthur_huggingface_co/transformers/switch_converted'		,			device_map='auto'				)

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

    lowerCamelCase__					:   Union[str, Any]      				=	tokenizer(_UpperCAmelCase		,			return_tensors='pt'				).input_ids
    lowerCamelCase__					:   int      				=	model.generate(_UpperCAmelCase		,			decoder_start_token_id=0				)
    print(tokenizer.decode(out[0]				)				)












import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""image_processor""", """tokenizer"""]
 UpperCAmelCase__									=	"""OwlViTImageProcessor"""
 UpperCAmelCase__									=	("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   str=None    ,  UpperCAmelCase						:   Any=None    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Dict:
     lowerCamelCase__					:   Any      				=	None
     if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'    ,  UpperCAmelCase    ,  )
         lowerCamelCase__					:   Optional[Any]      				=	kwargs.pop('feature_extractor'			)

     lowerCamelCase__					:   Dict      				=	image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.'			)
     if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.'			)

     super().__init__(UpperCAmelCase    ,  UpperCAmelCase			)
 def __call__(   self						:   List[Any]    ,  UpperCAmelCase						:   List[Any]=None    ,  UpperCAmelCase						:   Tuple=None    ,  UpperCAmelCase						:   int=None    ,  UpperCAmelCase						:   Optional[int]="max_length"    ,  UpperCAmelCase						:   Dict="np"    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Union[str, Any]:

     if text is None and query_images is None and images is None:
         raise ValueError(
             'You have to specify at least one text or query image or image. All three cannot be none.'			)

     if text is not None:
         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			) or (isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and not isinstance(text[0]    ,  UpperCAmelCase			)):
             lowerCamelCase__					:   Any      				=	[self.tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)]

         elif isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and isinstance(text[0]    ,  UpperCAmelCase			):
             lowerCamelCase__					:   List[str]      				=	[]

             # Maximum number of queries across batch
             lowerCamelCase__					:   List[Any]      				=	max([len(UpperCAmelCase			) for t in text]			)

             # Pad all batch samples to max number of text queries
             for t in text:
                 if len(UpperCAmelCase			) != max_num_queries:
                     lowerCamelCase__					:   int      				=	t + [' '] * (max_num_queries - len(UpperCAmelCase			))

                 lowerCamelCase__					:   str      				=	self.tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)
                 encodings.append(UpperCAmelCase			)
         else:
             raise TypeError('Input text should be a string, a list of strings or a nested list of strings'			)

         if return_tensors == "np":
             lowerCamelCase__					:   Optional[Any]      				=	np.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   int      				=	np.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         elif return_tensors == "jax" and is_flax_available():
             import jax.numpy as jnp

             lowerCamelCase__					:   Optional[int]      				=	jnp.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Any      				=	jnp.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         elif return_tensors == "pt" and is_torch_available():
             import torch

             lowerCamelCase__					:   Union[str, Any]      				=	torch.cat([encoding['input_ids'] for encoding in encodings]    ,  dim=0			)
             lowerCamelCase__					:   Dict      				=	torch.cat([encoding['attention_mask'] for encoding in encodings]    ,  dim=0			)

         elif return_tensors == "tf" and is_tf_available():
             import tensorflow as tf

             lowerCamelCase__					:   Any      				=	tf.stack([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Optional[int]      				=	tf.stack([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         else:
             raise ValueError('Target return tensor type could not be returned'			)

         lowerCamelCase__					:   str      				=	BatchEncoding()
         lowerCamelCase__					:   int      				=	input_ids
         lowerCamelCase__					:   Any      				=	attention_mask

     if query_images is not None:
         lowerCamelCase__					:   Union[str, Any]      				=	BatchEncoding()
         lowerCamelCase__					:   Optional[Any]      				=	self.image_processor(
             UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			).pixel_values
         lowerCamelCase__					:   List[Any]      				=	query_pixel_values

     if images is not None:
         lowerCamelCase__					:   List[Any]      				=	self.image_processor(UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)

     if text is not None and images is not None:
         lowerCamelCase__					:   Tuple      				=	image_features.pixel_values
         return encoding
     elif query_images is not None and images is not None:
         lowerCamelCase__					:   Dict      				=	image_features.pixel_values
         return encoding
     elif text is not None or query_images is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**UpperCAmelCase			)    ,  tensor_type=UpperCAmelCase			)
 def   A_			(   self						:   str    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[Any]			)    ->			Any:
     return self.image_processor.post_process(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   int    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			str:
     return self.image_processor.post_process_object_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Tuple    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   List[Any]			)    ->			Tuple:
     return self.tokenizer.batch_decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[Any]			)    ->			List[str]:
     return self.tokenizer.decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 @property
 def   A_			(   self						:   List[str]			)    ->			Optional[Any]:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor_class







 @property
 def   A_			(   self						:   int			)    ->			int:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor





