'''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 (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							Tuple:
      '''simple docstring'''


      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            lowercase_     =      flax_key_tuple[:-1] + ("weight",)
            lowercase_     =      torch.permute(__lowerCamelCase ,		(0, 2, 1)				)
      elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase				):
            # 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 (	__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							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 (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						Optional[int]				)	->							str:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						str = WEIGHTS_NAME				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      convert_file_size_to_int(__lowerCamelCase				)
      lowercase_     =      []
      lowercase_     =      {}
      lowercase_     =      0
      lowercase_     =      0

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

      lowercase_     =      {}
      for layer in checkpoint_info.keys():
            lowercase_ ,							lowercase_ ,							lowercase_     =      get_key_and_tensorstore_dict(
                __lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
            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(__lowerCamelCase				)
            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("/"				)				) ,		__lowerCamelCase				)
            lowercase_     =      "/".join(__lowerCamelCase				)

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

            lowercase_     =      raw_weights.to(getattr(__lowerCamelCase ,		__lowerCamelCase				)				)
            current_block_size += weight_size
            total_size += weight_size

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

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

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

      return metadata, index


if __name__ == "__main__":
       lowerCAmelCase_ : str						  =      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.",
       )
       lowerCAmelCase_ : 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 __a (	)	->							Optional[int]:
      '''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(__lowerCamelCase ,		return_tensors="pt"				).input_ids
      lowercase_     =      model.generate(__lowerCamelCase ,		decoder_start_token_id=0				)
      print(tokenizer.decode(out[0]				)				)







'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard





@slow
@require_flax
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "bird"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png")
         lowercase_     =      pipe.prepare_image_inputs([canny_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "Chef in the kitchen"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png")
         lowercase_     =      pipe.prepare_image_inputs([pose_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






