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 				UpperCamelCase						(       __lowercase						: Optional[int]						,__lowercase						: Tuple	):
		'''simple docstring'''




		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				A_		:				Union[str, Any]     =      flax_key_tuple[:-1] + ("weight",)
				A_		:				int     =      torch.permute(SCREAMING_SNAKE_CASE_						,(0, 2, 1)	)
		elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_	):
				# linear layer
				A_		:				str     =      flax_key_tuple[:-1] + ("weight",)
				A_		:				Dict     =      flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				A_		:				str     =      flax_key_tuple[:-1] + ("weight",)

		return flax_key_tuple, flax_tensor




def 				UpperCamelCase						(       __lowercase						: int						,__lowercase						: Tuple						,__lowercase						: List[str]	):
		'''simple docstring'''




		if "metadata" in layer:
				A_		:				Optional[int]     =      layer.split('metadata'	)
				A_		:				Tuple     =      "".join(split_layer[0]	)[:-1]
				A_		:				Tuple     =      [tuple(('metadata' + split_layer[1]).split('/'	)	)]
		elif "kvstore" in layer:
				A_		:				int     =      layer.split('kvstore'	)
				A_		:				Dict     =      "".join(split_layer[0]	)[:-1]
				A_		:				Optional[Any]     =      [tuple(('kvstore' + split_layer[1]).split('/'	)	)]

		else:
				A_		:				Optional[Any]     =      layer.split('/'	)
				A_		:				List[Any]     =      "/".join(split_layer[:-1]	)
				A_		:				Optional[Any]     =      (split_layer[-1],)

		if "kvstore/path" in layer:
				A_		:				Any     =      f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
		elif "kvstore/driver" in layer:
				A_		:				int     =      "file"
		else:
				A_		:				List[str]     =      checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content




def 				UpperCamelCase						(       __lowercase						: Optional[Any]						,__lowercase						: Dict	):
		'''simple docstring'''




		A_		:				List[Any]     =      rename_keys(SCREAMING_SNAKE_CASE_	)
		A_		:				int     =      {}
		for k, v in current_block.items():
				A_		:				Optional[Any]     =      v
		A_		:				Optional[Any]     =      new_current_block
		torch.save(SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_	)




def 				UpperCamelCase						(       __lowercase						: str						,__lowercase						: Dict						,__lowercase						: Tuple						,__lowercase						: Optional[int]						,__lowercase						: Optional[int] = WEIGHTS_NAME	):
		'''simple docstring'''




		A_		:				Dict     =      convert_file_size_to_int(SCREAMING_SNAKE_CASE_	)
		A_		:				Tuple     =      []
		A_		:				Optional[int]     =      {}
		A_		:				Optional[Any]     =      0
		A_		:				Any     =      0

		os.makedirs(SCREAMING_SNAKE_CASE_						,exist_ok=SCREAMING_SNAKE_CASE_	)
		with gfile.GFile(switch_checkpoint_path + '/checkpoint'						,'rb'	) as fp:
				A_		:				Dict     =      serialization.msgpack_restore(fp.read()	)["optimizer"]["target"]
				A_		:				Optional[Any]     =      flatten_dict(SCREAMING_SNAKE_CASE_						,sep='/'	)

		A_		:				Any     =      {}
		for layer in checkpoint_info.keys():
				A_		:				List[Any]     =      get_key_and_tensorstore_dict(
				    SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_	)
				if curr_real_layer_name in all_layers:
						A_		:				List[Any]     =      content
				else:
						A_		:				Dict     =      {split_layer[-1]: content}

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

				# use the renaming pattern from the small conversion scripts
				A_		:				List[Any]     =      rename_base_flax_keys(tuple(key.split('/'	)	)						,SCREAMING_SNAKE_CASE_	)
				A_		:				Optional[int]     =      "/".join(SCREAMING_SNAKE_CASE_	)

				# If this weight is going to tip up over the maximal size, we split.
				if current_block_size + weight_size > max_shard_size:
						A_		:				str     =      os.path.join(
						    SCREAMING_SNAKE_CASE_						,weights_name.replace('.bin'						,f'''-{len(SCREAMING_SNAKE_CASE_	)+1:05d}-of-???.bin'''	)	)
						rename_and_save_block(SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_	)
						sharded_state_dicts.append(current_block.keys()	)
						del current_block
						A_		:				str     =      {}
						A_		:				List[str]     =      0

				A_		:				Optional[Any]     =      raw_weights.to(getattr(SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_	)	)
				current_block_size += weight_size
				total_size += weight_size

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

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

		# Otherwise, let's build the index
		A_		:				List[Any]     =      {}
		A_		:				str     =      {}
		for idx, shard in enumerate(SCREAMING_SNAKE_CASE_	):
				A_		:				List[Any]     =      weights_name.replace(
				    '.bin'						,f'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_	):05d}.bin'''	)  # len(sharded_state_dicts):05d}
				A_		:				List[Any]     =      os.path.join(SCREAMING_SNAKE_CASE_						,weights_name.replace('.bin'						,f'''-{idx+1:05d}-of-???.bin'''	)	)
				os.rename(SCREAMING_SNAKE_CASE_						,os.path.join(SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_	)	)
				A_		:				List[Any]     =      shard
				for key in shard:
						A_		:				List[Any]     =      shard_file

				# Add the metadata
		A_		:				Tuple     =      {"total_size": total_size}
		A_		:				Dict     =      {"metadata": metadata, "weight_map": weight_map}

		with open(os.path.join(SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_	)						,'w'						,encoding='utf-8'	) as f:
				A_		:				Optional[Any]     =      json.dumps(SCREAMING_SNAKE_CASE_						,indent=2						,sort_keys=SCREAMING_SNAKE_CASE_	) + "\n"
				f.write(SCREAMING_SNAKE_CASE_	)

		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 				UpperCamelCase						(       ):
		'''simple docstring'''




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

		A_		:				str     =      tokenizer(SCREAMING_SNAKE_CASE_						,return_tensors='pt'	).input_ids
		A_		:				Optional[int]     =      model.generate(SCREAMING_SNAKE_CASE_						,decoder_start_token_id=0	)
		print(tokenizer.decode(out[0]	)	)










import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=[]	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				size[0] - overlap_pixels * 2
  lowerCamelCase :					int    =				size[1] - overlap_pixels * 2
  for letter in ["l", "r"]:
    if letter in remove_borders:
      size_x += overlap_pixels
  for letter in ["t", "b"]:
    if letter in remove_borders:
      size_y += overlap_pixels
  lowerCamelCase :					Tuple    =				np.ones((size_y, size_x)      ,						dtype=np.uinta	) * 255
  lowerCamelCase :					List[Any]    =				np.pad(SCREAMING_SNAKE_CASE_      ,						mode="linear_ramp"      ,						pad_width=SCREAMING_SNAKE_CASE_      ,						end_values=0	)

  if "l" in remove_borders:
    lowerCamelCase :					Optional[Any]    =				mask[:, overlap_pixels : mask.shape[1]]
  if "r" in remove_borders:
    lowerCamelCase :					List[Any]    =				mask[:, 0 : mask.shape[1] - overlap_pixels]
  if "t" in remove_borders:
    lowerCamelCase :					List[Any]    =				mask[overlap_pixels : mask.shape[0], :]
  if "b" in remove_borders:
    lowerCamelCase :					Tuple    =				mask[0 : mask.shape[0] - overlap_pixels, :]
  return mask






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return max(SCREAMING_SNAKE_CASE_      ,						min(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return (
      clamp(rect[0]      ,						min[0]      ,						max[0]	),
      clamp(rect[1]      ,						min[1]      ,						max[1]	),
      clamp(rect[2]      ,						min[0]      ,						max[0]	),
      clamp(rect[3]      ,						min[1]      ,						max[1]	),
  )






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				list(SCREAMING_SNAKE_CASE_	)
  rect[0] -= overlap
  rect[1] -= overlap
  rect[2] += overlap
  rect[3] += overlap
  lowerCamelCase :					Any    =				clamp_rect(SCREAMING_SNAKE_CASE_      ,						[0, 0]      ,						[image_size[0], image_size[1]]	)
  return rect






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				Image.new("RGB"      ,						(tile.size[0] + original_slice, tile.size[1])	)
  result.paste(
      original_image.resize((tile.size[0], tile.size[1])      ,						Image.BICUBIC	).crop(
          (slice_x, 0, slice_x + original_slice, tile.size[1])	)      ,						(0, 0)      ,						)
  result.paste(SCREAMING_SNAKE_CASE_      ,						(original_slice, 0)	)
  return result






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
  lowerCamelCase :					int    =				tile.crop(SCREAMING_SNAKE_CASE_	)
  return tile






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				n % d
  return n - divisor
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A = 350						,				):






     """simple docstring"""





     super().__init__(
         vae=__A						,				text_encoder=__A						,				tokenizer=__A						,				unet=__A						,				low_res_scheduler=__A						,				scheduler=__A						,				max_noise_level=__A						,				)






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				**__A ):






     """simple docstring"""





     torch.manual_seed(0 )
     lowerCamelCase :					Tuple    =				(
         min(image.size[0] - (tile_size + original_image_slice)						,				x * tile_size ),
         min(image.size[1] - (tile_size + original_image_slice)						,				y * tile_size ),
         min(image.size[0]						,				(x + 1) * tile_size ),
         min(image.size[1]						,				(y + 1) * tile_size ),
     )
     lowerCamelCase :					Union[str, Any]    =				add_overlap_rect(__A						,				__A						,				image.size )
     lowerCamelCase :					List[str]    =				image.crop(__A )
     lowerCamelCase :					Optional[int]    =				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     lowerCamelCase :					int    =				translated_slice_x - (original_image_slice / 2)
     lowerCamelCase :					Optional[Any]    =				max(0						,				__A )
     lowerCamelCase :					Tuple    =				squeeze_tile(__A						,				__A						,				__A						,				__A )
     lowerCamelCase :					Dict    =				to_input.size
     lowerCamelCase :					Optional[int]    =				to_input.resize((tile_size, tile_size)						,				Image.BICUBIC )
     lowerCamelCase :					Dict    =				super(__A						,				self ).__call__(image=__A						,				**__A ).images[0]
     lowerCamelCase :					Tuple    =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					Optional[Any]    =				unsqueeze_tile(__A						,				__A )
     lowerCamelCase :					Optional[Any]    =				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					int    =				[]
     if x == 0:
       remove_borders.append("l" )
     elif crop_rect[2] == image.size[0]:
       remove_borders.append("r" )
     if y == 0:
       remove_borders.append("t" )
     elif crop_rect[3] == image.size[1]:
       remove_borders.append("b" )
     lowerCamelCase :					int    =				Image.fromarray(
         make_transparency_mask(
             (upscaled_tile.size[0], upscaled_tile.size[1])						,				tile_border * 4						,				remove_borders=__A )						,				mode="L"						,				)
     final_image.paste(
         __A						,				(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)						,				__A )






   @torch.no_grad()
   def __call__(		self						,				__A						,				__A						,				__A = 75						,				__A = 9.0						,				__A = 50						,				__A = None						,				__A = 1						,				__A = 0.0						,				__A = None						,				__A = None						,				__A = None						,				__A = 1						,				__A = 128						,				__A = 32						,				__A = 32						,				):






     """simple docstring"""





     lowerCamelCase :					Dict    =				Image.new("RGB"						,				(image.size[0] * 4, image.size[1] * 4) )
     lowerCamelCase :					Union[str, Any]    =				math.ceil(image.size[0] / tile_size )
     lowerCamelCase :					Dict    =				math.ceil(image.size[1] / tile_size )
     lowerCamelCase :					str    =				tcx * tcy
     lowerCamelCase :					int    =				0
     for y in range(__A ):
       for x in range(__A ):
         self._process_tile(
             __A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				prompt=__A						,				num_inference_steps=__A						,				guidance_scale=__A						,				noise_level=__A						,				negative_prompt=__A						,				num_images_per_prompt=__A						,				eta=__A						,				generator=__A						,				latents=__A						,				)
         current_count += 1
         if callback is not None:
           callback({"progress": current_count / total_tile_count, "image": final_image} )
     return final_image






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"stabilityai/stable-diffusion-x4-upscaler"
  lowerCamelCase :					Union[str, Any]    =				StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE_      ,						revision="fp16"      ,						torch_dtype=torch.floataa	)
  lowerCamelCase :					Optional[Any]    =				pipe.to("cuda"	)
  lowerCamelCase :					List[str]    =				Image.open("../../docs/source/imgs/diffusers_library.jpg"	)

  def callback(SCREAMING_SNAKE_CASE_	):
    print(f"""progress: {obj['progress']:.4f}"""	)
    obj["image"].save("diffusers_library_progress.jpg"	)

  lowerCamelCase :					int    =				pipe(image=SCREAMING_SNAKE_CASE_      ,						prompt="Black font, white background, vector"      ,						noise_level=40      ,						callback=SCREAMING_SNAKE_CASE_	)
  final_image.save("diffusers_library.jpg"	)


if __name__ == "__main__":
   main()



