





import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


__lowercase	: Optional[int]       =logging.get_logger(__name__)

# General docstring
__lowercase	: Tuple       ="""PoolFormerConfig"""

# Base docstring
__lowercase	: Union[str, Any]       ="""sail/poolformer_s12"""
__lowercase	: int       =[1, 512, 7, 7]

# Image classification docstring
__lowercase	: Tuple       ="""sail/poolformer_s12"""
__lowercase	: Optional[Any]       ="""tabby, tabby cat"""

__lowercase	: List[str]       =[
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]




def      a__			(       lowercase__	,     lowercase__ = 0.0	,     lowercase__ = False	):






   '''simple docstring'''






   if drop_prob == 0.0 or not training:
      return input
   UpperCAmelCase_           =1 - drop_prob
   UpperCAmelCase_           =(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
   UpperCAmelCase_           =keep_prob + torch.rand(lowercase__	,     dtype=input.dtype	,     device=input.device	)
   random_tensor.floor_()  # binarize
   UpperCAmelCase_           =input.div(lowercase__	) * random_tensor
   return output

class   A							(						nn.Module    ):



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    Optional[float] = None							)    ->     None:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =drop_prob



      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    torch.Tensor							)    ->     torch.Tensor:
         '''simple docstring'''

         return drop_path(_lowerCAmelCase		,	self.drop_prob		,	self.training							)






      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     str:
         '''simple docstring'''

         return "p={}".format(self.drop_prob							)

class   A							(						nn.Module    ):



      def __init__(						self:    str		,	_lowerCAmelCase:    Dict		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    Tuple		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    Optional[Any]=None							)    ->     List[str]:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =patch_size if isinstance(_lowerCAmelCase		,	collections.abc.Iterable							) else (patch_size, patch_size)
         UpperCAmelCase_           =stride if isinstance(_lowerCAmelCase		,	collections.abc.Iterable							) else (stride, stride)
         UpperCAmelCase_           =padding if isinstance(_lowerCAmelCase		,	collections.abc.Iterable							) else (padding, padding)

         UpperCAmelCase_           =nn.Convad(_lowerCAmelCase		,	_lowerCAmelCase		,	kernel_size=_lowerCAmelCase		,	stride=_lowerCAmelCase		,	padding=_lowerCAmelCase							)
         UpperCAmelCase_           =norm_layer(_lowerCAmelCase							) if norm_layer else nn.Identity()






      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    str							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =self.projection(_lowerCAmelCase							)
         UpperCAmelCase_           =self.norm(_lowerCAmelCase							)
         return embeddings

class   A							(						nn.GroupNorm    ):



      def __init__(						self:    int		,	_lowerCAmelCase:    int		,	**_lowerCAmelCase:    Any							)    ->     Tuple:
         '''simple docstring'''

         super().__init__(1		,	_lowerCAmelCase		,	**_lowerCAmelCase							)

class   A							(						nn.Module    ):



      def __init__(						self:    Tuple		,	_lowerCAmelCase:    int							)    ->     List[Any]:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =nn.AvgPoolad(_lowerCAmelCase		,	stride=1		,	padding=pool_size // 2		,	count_include_pad=_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    Optional[int]							)    ->     Optional[int]:
         '''simple docstring'''

         return self.pool(_lowerCAmelCase							) - hidden_states

class   A							(						nn.Module    ):



      def __init__(						self:    Any		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    List[str]							)    ->     Any:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =nn.Convad(_lowerCAmelCase		,	_lowerCAmelCase		,	1							)
         UpperCAmelCase_           =nn.Convad(_lowerCAmelCase		,	_lowerCAmelCase		,	1							)
         UpperCAmelCase_           =PoolFormerDropPath(_lowerCAmelCase							)
         if isinstance(config.hidden_act		,	_lowerCAmelCase							):
            UpperCAmelCase_           =ACTaFN[config.hidden_act]
         else:
            UpperCAmelCase_           =config.hidden_act






      def 						lowerCAmelCase__     (						self:    str		,	_lowerCAmelCase:    int							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.conva(_lowerCAmelCase							)
         UpperCAmelCase_           =self.act_fn(_lowerCAmelCase							)
         UpperCAmelCase_           =self.drop(_lowerCAmelCase							)
         UpperCAmelCase_           =self.conva(_lowerCAmelCase							)
         UpperCAmelCase_           =self.drop(_lowerCAmelCase							)

         return hidden_states

class   A							(						nn.Module    ):



      def __init__(						self:    str		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    Any		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    Any							)    ->     List[Any]:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =PoolFormerPooling(_lowerCAmelCase							)
         UpperCAmelCase_           =PoolFormerOutput(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =PoolFormerGroupNorm(_lowerCAmelCase							)
         UpperCAmelCase_           =PoolFormerGroupNorm(_lowerCAmelCase							)

         # Useful for training neural nets
         UpperCAmelCase_           =PoolFormerDropPath(_lowerCAmelCase							) if drop_path > 0.0 else nn.Identity()
         UpperCAmelCase_           =config.use_layer_scale
         if config.use_layer_scale:
            UpperCAmelCase_           =nn.Parameter(
                config.layer_scale_init_value * torch.ones((_lowerCAmelCase)							)		,	requires_grad=_lowerCAmelCase							)
            UpperCAmelCase_           =nn.Parameter(
                config.layer_scale_init_value * torch.ones((_lowerCAmelCase)							)		,	requires_grad=_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    List[str]							)    ->     List[Any]:
         '''simple docstring'''

         if self.use_layer_scale:
            UpperCAmelCase_           =self.pooling(self.before_norm(_lowerCAmelCase							)							)
            UpperCAmelCase_           =self.layer_scale_a.unsqueeze(-1							).unsqueeze(-1							) * pooling_output
            # First residual connection
            UpperCAmelCase_           =hidden_states + self.drop_path(_lowerCAmelCase							)
            UpperCAmelCase_           =()

            UpperCAmelCase_           =self.output(self.after_norm(_lowerCAmelCase							)							)
            UpperCAmelCase_           =self.layer_scale_a.unsqueeze(-1							).unsqueeze(-1							) * layer_output
            # Second residual connection
            UpperCAmelCase_           =hidden_states + self.drop_path(_lowerCAmelCase							)

            UpperCAmelCase_           =(output,) + outputs
            return outputs

         else:
            UpperCAmelCase_           =self.drop_path(self.pooling(self.before_norm(_lowerCAmelCase							)							)							)
            # First residual connection
            UpperCAmelCase_           =pooling_output + hidden_states
            UpperCAmelCase_           =()

            # Second residual connection inside the PoolFormerOutput block
            UpperCAmelCase_           =self.drop_path(self.output(self.after_norm(_lowerCAmelCase							)							)							)
            UpperCAmelCase_           =hidden_states + layer_output

            UpperCAmelCase_           =(output,) + outputs
            return outputs

class   A							(						nn.Module    ):



      def __init__(						self:    Union[str, Any]		,	_lowerCAmelCase:    Dict							)    ->     str:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =config
         # stochastic depth decay rule
         UpperCAmelCase_           =[x.item() for x in torch.linspace(0		,	config.drop_path_rate		,	sum(config.depths							)							)]

         # patch embeddings
         UpperCAmelCase_           =[]
         for i in range(config.num_encoder_blocks							):
            embeddings.append(
                PoolFormerEmbeddings(
                    patch_size=config.patch_sizes[i]		,	stride=config.strides[i]		,	padding=config.padding[i]		,	num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]		,	hidden_size=config.hidden_sizes[i]		,	)							)
         UpperCAmelCase_           =nn.ModuleList(_lowerCAmelCase							)

         # Transformer blocks
         UpperCAmelCase_           =[]
         UpperCAmelCase_           =0
         for i in range(config.num_encoder_blocks							):
            # each block consists of layers
            UpperCAmelCase_           =[]
            if i != 0:
               cur += config.depths[i - 1]
            for j in range(config.depths[i]							):
               layers.append(
                   PoolFormerLayer(
                       _lowerCAmelCase		,	num_channels=config.hidden_sizes[i]		,	pool_size=config.pool_size		,	hidden_size=config.hidden_sizes[i]		,	intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio							)		,	drop_path=dpr[cur + j]		,	)							)
            blocks.append(nn.ModuleList(_lowerCAmelCase							)							)

         UpperCAmelCase_           =nn.ModuleList(_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    Optional[Any]		,	_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    Optional[Any]=False		,	_lowerCAmelCase:    Dict=True							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =() if output_hidden_states else None

         UpperCAmelCase_           =pixel_values
         for idx, layers in enumerate(zip(self.patch_embeddings		,	self.block							)							):
            UpperCAmelCase_    ,							UpperCAmelCase_           =layers
            # Get patch embeddings from hidden_states
            UpperCAmelCase_           =embedding_layer(_lowerCAmelCase							)
            # Send the embeddings through the blocks
            for _, blk in enumerate(_lowerCAmelCase							):
               UpperCAmelCase_           =blk(_lowerCAmelCase							)
               UpperCAmelCase_           =layer_outputs[0]

            if output_hidden_states:
               UpperCAmelCase_           =all_hidden_states + (hidden_states,)

         if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None							)

         return BaseModelOutputWithNoAttention(last_hidden_state=_lowerCAmelCase		,	hidden_states=_lowerCAmelCase							)

class   A							(						__lowercase    ):
      _snake_case				  =PoolFormerConfig
      _snake_case				  ='''poolformer'''
      _snake_case				  ='''pixel_values'''
      _snake_case				  =True



      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    str							)    ->     Optional[Any]:
         '''simple docstring'''

         if isinstance(_lowerCAmelCase		,	(nn.Linear, nn.Convad)							):
            module.weight.data.normal_(mean=0.0		,	std=self.config.initializer_range							)
            if module.bias is not None:
               module.bias.data.zero_()
         elif isinstance(_lowerCAmelCase		,	nn.LayerNorm							):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0							)






      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    Tuple=False							)    ->     Optional[Any]:
         '''simple docstring'''

         if isinstance(_lowerCAmelCase		,	_lowerCAmelCase							):
            UpperCAmelCase_           =value

__lowercase	: Union[str, Any]       =R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

__lowercase	: Union[str, Any]       =R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""

@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''		,   __lowercase		,   )
class   A							(						__lowercase    ):



      def __init__(						self:    Any		,	_lowerCAmelCase:    List[Any]							)    ->     str:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase							)
         UpperCAmelCase_           =config

         UpperCAmelCase_           =PoolFormerEncoder(_lowerCAmelCase							)

         # Initialize weights and apply final processing
         self.post_init()



      def 						lowerCAmelCase__     (						self:    str							)    ->     int:
         '''simple docstring'''

         return self.embeddings.patch_embeddings






      @add_start_docstrings_to_model_forward(_lowerCAmelCase							)
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC		,	output_type=_lowerCAmelCase		,	config_class=_CONFIG_FOR_DOC		,	modality="vision"		,	expected_output=_EXPECTED_OUTPUT_SHAPE		,	)
      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    Optional[torch.FloatTensor] = None		,	_lowerCAmelCase:    Optional[bool] = None		,	_lowerCAmelCase:    Optional[bool] = None		,	)    ->     Union[Tuple, BaseModelOutputWithNoAttention]:
         '''simple docstring'''

         UpperCAmelCase_           =(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         UpperCAmelCase_           =return_dict if return_dict is not None else self.config.use_return_dict

         if pixel_values is None:
            raise ValueError("You have to specify pixel_values"							)

         UpperCAmelCase_           =self.encoder(
             _lowerCAmelCase		,	output_hidden_states=_lowerCAmelCase		,	return_dict=_lowerCAmelCase		,	)
         UpperCAmelCase_           =encoder_outputs[0]

         if not return_dict:
            return (sequence_output, None) + encoder_outputs[1:]

         return BaseModelOutputWithNoAttention(
             last_hidden_state=_lowerCAmelCase		,	hidden_states=encoder_outputs.hidden_states		,	)

class   A							(						nn.Module    ):



      def __init__(						self:    str		,	_lowerCAmelCase:    Dict							)    ->     Dict:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =nn.Linear(config.hidden_size		,	config.hidden_size							)






      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    Optional[Any]							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =self.dense(_lowerCAmelCase							)
         return output

@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''		,   __lowercase		,   )
class   A							(						__lowercase    ):



      def __init__(						self:    Union[str, Any]		,	_lowerCAmelCase:    Optional[int]							)    ->     Dict:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase							)
         UpperCAmelCase_           =config.num_labels
         UpperCAmelCase_           =PoolFormerModel(_lowerCAmelCase							)

         # Final norm
         UpperCAmelCase_           =PoolFormerGroupNorm(config.hidden_sizes[-1]							)
         # Classifier head
         UpperCAmelCase_           =(
             nn.Linear(config.hidden_sizes[-1]		,	config.num_labels							) if config.num_labels > 0 else nn.Identity()
         )

         # Initialize weights and apply final processing
         self.post_init()






      @add_start_docstrings_to_model_forward(_lowerCAmelCase							)
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT		,	output_type=_lowerCAmelCase		,	config_class=_CONFIG_FOR_DOC		,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,	)
      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    Optional[torch.FloatTensor] = None		,	_lowerCAmelCase:    Optional[torch.LongTensor] = None		,	_lowerCAmelCase:    Optional[bool] = None		,	_lowerCAmelCase:    Optional[bool] = None		,	)    ->     Union[Tuple, ImageClassifierOutputWithNoAttention]:
         '''simple docstring'''

         UpperCAmelCase_           =return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase_           =self.poolformer(
             _lowerCAmelCase		,	output_hidden_states=_lowerCAmelCase		,	return_dict=_lowerCAmelCase		,	)

         UpperCAmelCase_           =outputs[0]

         UpperCAmelCase_           =self.classifier(self.norm(_lowerCAmelCase							).mean([-2, -1]							)							)

         UpperCAmelCase_           =None
         if labels is not None:
            if self.config.problem_type is None:
               if self.num_labels == 1:
                  UpperCAmelCase_           ="regression"
               elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                  UpperCAmelCase_           ="single_label_classification"
               else:
                  UpperCAmelCase_           ="multi_label_classification"

            if self.config.problem_type == "regression":
               UpperCAmelCase_           =MSELoss()
               if self.num_labels == 1:
                  UpperCAmelCase_           =loss_fct(logits.squeeze()		,	labels.squeeze()							)
               else:
                  UpperCAmelCase_           =loss_fct(_lowerCAmelCase		,	_lowerCAmelCase							)
            elif self.config.problem_type == "single_label_classification":
               UpperCAmelCase_           =CrossEntropyLoss()
               UpperCAmelCase_           =loss_fct(logits.view(-1		,	self.num_labels							)		,	labels.view(-1							)							)
            elif self.config.problem_type == "multi_label_classification":
               UpperCAmelCase_           =BCEWithLogitsLoss()
               UpperCAmelCase_           =loss_fct(_lowerCAmelCase		,	_lowerCAmelCase							)

         if not return_dict:
            UpperCAmelCase_           =(logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

         return ImageClassifierOutputWithNoAttention(loss=_lowerCAmelCase		,	logits=_lowerCAmelCase		,	hidden_states=outputs.hidden_states							)







"""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   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[Any]				):



      """simple docstring"""






      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            A				  =		flax_key_tuple[:-1] + ("""weight""",)
            A				  =		torch.permute(_a     ,   (0, 2, 1)				)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_a				):
            # 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   _A					(	_a	:						Tuple     ,   _a	:						Any     ,   _a	:						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   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		rename_keys(_a				)
      A				  =		{}
      for k, v in current_block.items():
            A				  =		v
      A				  =		new_current_block
      torch.save(_a     ,   _a				)



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						int     ,   _a	:						Optional[int]     ,   _a	:						str = WEIGHTS_NAME				):



      """simple docstring"""






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

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

      A				  =		{}
      for layer in checkpoint_info.keys():
            A  ,			A  ,			A				  =		get_key_and_tensorstore_dict(
                _a     ,   _a     ,   _a				)
            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(_a				)
            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("""/"""				)				)     ,   _a				)
            A				  =		"""/""".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:
                  A				  =		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
                  A				  =		{}
                  A				  =		0

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

      # Add the last block
      A				  =		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
      A				  =		{}
      A				  =		{}
      for idx, shard in enumerate(_a				):
            A				  =		weights_name.replace(
                """.bin"""     ,   f'-{idx+1:05d}-of-{len(_a				):05d}.bin'				)  # len(sharded_state_dicts):05d}
            A				  =		os.path.join(_a     ,   weights_name.replace(""".bin"""     ,   f'-{idx+1:05d}-of-???.bin'				)				)
            os.rename(_a     ,   os.path.join(_a     ,   _a				)				)
            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(_a     ,   _a				)     ,   """w"""     ,   encoding="""utf-8"""				) as f:
            A				  =		json.dumps(_a     ,   indent=2     ,   sort_keys=_a				) + """\n"""
            f.write(_a				)

      return metadata, index


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





def   _A					(	):



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




