

"""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  (  UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Tuple)     ->						Optional[Any]:
   '''simple docstring'''


   if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
      # expert layer
      __lowercase								=						flax_key_tuple[:-1] + ("weight",)
      __lowercase								=						torch.permute(UpperCamelCase_,      (0, 2, 1))
   elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase_):
      # linear layer
      __lowercase								=						flax_key_tuple[:-1] + ("weight",)
      __lowercase								=						flax_tensor.T
   elif flax_key_tuple[-1] in ["scale", "embedding"]:
      __lowercase								=						flax_key_tuple[:-1] + ("weight",)

   return flax_key_tuple, flax_tensor




def   _A  (  UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Tuple)     ->						List[str]:
   '''simple docstring'''


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

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

   if "kvstore/path" in layer:
      __lowercase								=						F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
   elif "kvstore/driver" in layer:
      __lowercase								=						"file"
   else:
      __lowercase								=						checkpoint_info[layer]

   return curr_real_layer_name, split_layer, content




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	str,      UpperCamelCase_					:	str = WEIGHTS_NAME)     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						convert_file_size_to_int(UpperCamelCase_)
   __lowercase								=						[]
   __lowercase								=						{}
   __lowercase								=						0
   __lowercase								=						0

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

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

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

      # use the renaming pattern from the small conversion scripts
      __lowercase		,__lowercase								=						rename_base_flax_keys(tuple(key.split("/")),      UpperCamelCase_)
      __lowercase								=						"/".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:
         __lowercase								=						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
         __lowercase								=						{}
         __lowercase								=						0

      __lowercase								=						raw_weights.to(getattr(UpperCamelCase_,      UpperCamelCase_))
      current_block_size += weight_size
      total_size += weight_size

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

    # Add the metadata
   __lowercase								=						{"total_size": total_size}
   __lowercase								=						{"metadata": metadata, "weight_map": weight_map}

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

   return metadata, index


if __name__ == "__main__":
   _a							=							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.',
   )
   _a							=							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  (  )     ->						Union[str, Any]:
   '''simple docstring'''


   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








"""simple docstring"""





from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)




@flax.struct.dataclass
class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : jnp.ndarray
 __UpperCAmelCase     : jnp.ndarray







class  _lowerCAmelCase  (     nn.Module       ):







 """simple docstring"""







 __UpperCAmelCase     : int
 __UpperCAmelCase     : Tuple[int]       		=     (1_6, 3_2, 9_6, 2_5_6)
 __UpperCAmelCase     : jnp.dtype       		=     jnp.floataa


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						nn.Conv(
        self.block_out_channels[0], kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, )

    __lowercase								=						[]
    for i in range(len(self.block_out_channels     ) - 1     ):
       __lowercase								=						self.block_out_channels[i]
       __lowercase								=						self.block_out_channels[i + 1]
       __lowercase								=						nn.Conv(
           UpperCAmelCase__, kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, )
       blocks.append(UpperCAmelCase__     )
       __lowercase								=						nn.Conv(
           UpperCAmelCase__, kernel_size=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)), dtype=self.dtype, )
       blocks.append(UpperCAmelCase__     )
    __lowercase								=						blocks

    __lowercase								=						nn.Conv(
        self.conditioning_embedding_channels, kernel_size=(3, 3), padding=((1, 1), (1, 1)), kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )



 def __call__(      self							:      Any, UpperCAmelCase__							:      Union[str, Any]     ):
    __lowercase								=						self.conv_in(UpperCAmelCase__     )
    __lowercase								=						nn.silu(UpperCAmelCase__     )

    for block in self.blocks:
       __lowercase								=						block(UpperCAmelCase__     )
       __lowercase								=						nn.silu(UpperCAmelCase__     )

    __lowercase								=						self.conv_out(UpperCAmelCase__     )

    return embedding







@flax_register_to_config
class  _lowerCAmelCase  (     nn.Module					,lowercase					,lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : int       		=     3_2
 __UpperCAmelCase     : int       		=     4
 __UpperCAmelCase     : Tuple[str]       		=     (
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 __UpperCAmelCase     : Union[bool, Tuple[bool]]       		=     False
 __UpperCAmelCase     : Tuple[int]       		=     (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
 __UpperCAmelCase     : int       		=     2
 __UpperCAmelCase     : Union[int, Tuple[int]]       		=     8
 __UpperCAmelCase     : Optional[Union[int, Tuple[int]]]       		=     None
 __UpperCAmelCase     : int       		=     1_2_8_0
 __UpperCAmelCase     : float       		=     0.0
 __UpperCAmelCase     : bool       		=     False
 __UpperCAmelCase     : jnp.dtype       		=     jnp.floataa
 __UpperCAmelCase     : bool       		=     True
 __UpperCAmelCase     : int       		=     0
 __UpperCAmelCase     : str       		=     "rgb"
 __UpperCAmelCase     : Tuple[int]       		=     (1_6, 3_2, 9_6, 2_5_6)


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      jax.random.KeyArray     ):
    # init input tensors
    __lowercase								=						(1, self.in_channels, self.sample_size, self.sample_size)
    __lowercase								=						jnp.zeros(UpperCAmelCase__, dtype=jnp.floataa     )
    __lowercase								=						jnp.ones((1,), dtype=jnp.intaa     )
    __lowercase								=						jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.floataa     )
    __lowercase								=						(1, 3, self.sample_size * 8, self.sample_size * 8)
    __lowercase								=						jnp.zeros(UpperCAmelCase__, dtype=jnp.floataa     )

    __lowercase		,__lowercase								=						jax.random.split(UpperCAmelCase__     )
    __lowercase								=						{"params": params_rng, "dropout": dropout_rng}

    return self.init(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )["params"]


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						self.block_out_channels
    __lowercase								=						block_out_channels[0] * 4

    # If `num_attention_heads` is not defined (which is the case for most models)
    # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
    # The reason for this behavior is to correct for incorrectly named variables that were introduced
    # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
    # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
    # which is why we correct for the naming here.
    __lowercase								=						self.num_attention_heads or self.attention_head_dim

    # input
    __lowercase								=						nn.Conv(
        block_out_channels[0], kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, )

    # time
    __lowercase								=						FlaxTimesteps(
        block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift     )
    __lowercase								=						FlaxTimestepEmbedding(UpperCAmelCase__, dtype=self.dtype     )

    __lowercase								=						FlaxControlNetConditioningEmbedding(
        conditioning_embedding_channels=block_out_channels[0], block_out_channels=self.conditioning_embedding_out_channels, )

    __lowercase								=						self.only_cross_attention
    if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
       __lowercase								=						(only_cross_attention,) * len(self.down_block_types     )

    if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
       __lowercase								=						(num_attention_heads,) * len(self.down_block_types     )

    # down
    __lowercase								=						[]
    __lowercase								=						[]

    __lowercase								=						block_out_channels[0]

    __lowercase								=						nn.Conv(
        UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
    controlnet_down_blocks.append(UpperCAmelCase__     )

    for i, down_block_type in enumerate(self.down_block_types     ):
       __lowercase								=						output_channel
       __lowercase								=						block_out_channels[i]
       __lowercase								=						i == len(UpperCAmelCase__     ) - 1

       if down_block_type == "CrossAttnDownBlock2D":
          __lowercase								=						FlaxCrossAttnDownBlockaD(
              in_channels=UpperCAmelCase__, out_channels=UpperCAmelCase__, dropout=self.dropout, num_layers=self.layers_per_block, num_attention_heads=num_attention_heads[i], add_downsample=not is_final_block, use_linear_projection=self.use_linear_projection, only_cross_attention=only_cross_attention[i], dtype=self.dtype, )
       else:
          __lowercase								=						FlaxDownBlockaD(
              in_channels=UpperCAmelCase__, out_channels=UpperCAmelCase__, dropout=self.dropout, num_layers=self.layers_per_block, add_downsample=not is_final_block, dtype=self.dtype, )

       down_blocks.append(UpperCAmelCase__     )

       for _ in range(self.layers_per_block     ):
          __lowercase								=						nn.Conv(
              UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
          controlnet_down_blocks.append(UpperCAmelCase__     )

       if not is_final_block:
          __lowercase								=						nn.Conv(
              UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
          controlnet_down_blocks.append(UpperCAmelCase__     )

    __lowercase								=						down_blocks
    __lowercase								=						controlnet_down_blocks

    # mid
    __lowercase								=						block_out_channels[-1]
    __lowercase								=						FlaxUNetMidBlockaDCrossAttn(
        in_channels=UpperCAmelCase__, dropout=self.dropout, num_attention_heads=num_attention_heads[-1], use_linear_projection=self.use_linear_projection, dtype=self.dtype, )

    __lowercase								=						nn.Conv(
        UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )



 def __call__(      self							:      str, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      float = 1.0, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      bool = False, ):
    __lowercase								=						self.controlnet_conditioning_channel_order
    if channel_order == "bgr":
       __lowercase								=						jnp.flip(UpperCAmelCase__, axis=1     )

    # 1. time
    if not isinstance(UpperCAmelCase__, jnp.ndarray     ):
       __lowercase								=						jnp.array([timesteps], dtype=jnp.intaa     )
    elif isinstance(UpperCAmelCase__, jnp.ndarray     ) and len(timesteps.shape     ) == 0:
       __lowercase								=						timesteps.astype(dtype=jnp.floataa     )
       __lowercase								=						jnp.expand_dims(UpperCAmelCase__, 0     )

    __lowercase								=						self.time_proj(UpperCAmelCase__     )
    __lowercase								=						self.time_embedding(UpperCAmelCase__     )

    # 2. pre-process
    __lowercase								=						jnp.transpose(UpperCAmelCase__, (0, 2, 3, 1)     )
    __lowercase								=						self.conv_in(UpperCAmelCase__     )

    __lowercase								=						jnp.transpose(UpperCAmelCase__, (0, 2, 3, 1)     )
    __lowercase								=						self.controlnet_cond_embedding(UpperCAmelCase__     )
    sample += controlnet_cond

    # 3. down
    __lowercase								=						(sample,)
    for down_block in self.down_blocks:
       if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
          __lowercase		,__lowercase								=						down_block(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, deterministic=not train     )
       else:
          __lowercase		,__lowercase								=						down_block(UpperCAmelCase__, UpperCAmelCase__, deterministic=not train     )
       down_block_res_samples += res_samples

    # 4. mid
    __lowercase								=						self.mid_block(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, deterministic=not train     )

    # 5. contronet blocks
    __lowercase								=						()
    for down_block_res_sample, controlnet_block in zip(UpperCAmelCase__, self.controlnet_down_blocks     ):
       __lowercase								=						controlnet_block(UpperCAmelCase__     )
       controlnet_down_block_res_samples += (down_block_res_sample,)

    __lowercase								=						controlnet_down_block_res_samples

    __lowercase								=						self.controlnet_mid_block(UpperCAmelCase__     )

    # 6. scaling
    __lowercase								=						[sample * conditioning_scale for sample in down_block_res_samples]
    mid_block_res_sample *= conditioning_scale

    if not return_dict:
       return (down_block_res_samples, mid_block_res_sample)

    return FlaxControlNetOutput(
        down_block_res_samples=UpperCAmelCase__, mid_block_res_sample=UpperCAmelCase__     )




