
"""simple docstring"""





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = StableDiffusionInpaintPipeline
		lowercase__         = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		lowercase__         = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowercase__         = frozenset(
		    []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		lowercase__         = frozenset([]      )



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							torch.manual_seed(0)
							_UpperCamelCase									=    UNetaDConditionModel(
							    block_out_channels=(32, 64)							,						layers_per_block=2							,						sample_size=32							,						in_channels=9							,						out_channels=4							,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,						cross_attention_dim=32							,						attention_head_dim=(2, 4)							,						use_linear_projection=__a							,						)
							_UpperCamelCase									=    PNDMScheduler(skip_prk_steps=__a)
							torch.manual_seed(0)
							_UpperCamelCase									=    AutoencoderKL(
							    block_out_channels=[32, 64]							,						in_channels=3							,						out_channels=3							,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,						latent_channels=4							,						sample_size=1_28							,						)
							torch.manual_seed(0)
							_UpperCamelCase									=    CLIPTextConfig(
							    bos_token_id=0							,						eos_token_id=2							,						hidden_size=32							,						intermediate_size=37							,						layer_norm_eps=1e-05							,						num_attention_heads=4							,						num_hidden_layers=5							,						pad_token_id=1							,						vocab_size=10_00							,						hidden_act='''gelu'''							,						projection_dim=5_12							,						)
							_UpperCamelCase									=    CLIPTextModel(__a)
							_UpperCamelCase									=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							_UpperCamelCase									=    {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components



		def 		UpperCAmelCase				(   self							,						__a							,						__a=0)			->			Optional[int]:






							'''simple docstring'''
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							_UpperCamelCase									=    floats_tensor((1, 3, 32, 32)							,						rng=random.Random(__a)).to(__a)
							_UpperCamelCase									=    image.cpu().permute(0							,						2							,						3							,						1)[0]
							_UpperCamelCase									=    Image.fromarray(np.uinta(__a)).convert('''RGB''').resize((64, 64))
							_UpperCamelCase									=    Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((64, 64))
							if str(__a).startswith('''mps'''):
												_UpperCamelCase									=    torch.manual_seed(__a)
							else:
												_UpperCamelCase									=    torch.Generator(device=__a).manual_seed(__a)
							_UpperCamelCase									=    {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': init_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase									=    self.get_dummy_components()
							_UpperCamelCase									=    StableDiffusionInpaintPipeline(**__a)
							_UpperCamelCase									=    sd_pipe.to(__a)
							sd_pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase									=    self.get_dummy_inputs(__a)
							_UpperCamelCase									=    sd_pipe(**__a).images
							_UpperCamelCase									=    image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							_UpperCamelCase									=    np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							super().test_inference_batch_single_identical(expected_max_diff=3e-3)






@slow
@require_torch_gpu
class        _UpperCAmelCase(       unittest.TestCase      ):



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png''')
							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
							_UpperCamelCase									=    load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
							    '''/yellow_cat_sitting_on_a_park_bench.npy''')

							_UpperCamelCase									=    '''stabilityai/stable-diffusion-2-inpainting'''
							_UpperCamelCase									=    StableDiffusionInpaintPipeline.from_pretrained(__a							,						safety_checker=__a)
							pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing()

							_UpperCamelCase									=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

							_UpperCamelCase									=    torch.manual_seed(0)
							_UpperCamelCase									=    pipe(
							    prompt=__a							,						image=__a							,						mask_image=__a							,						generator=__a							,						output_type='''np'''							,						)
							_UpperCamelCase									=    output.images[0]

							assert image.shape == (5_12, 5_12, 3)
							assert np.abs(expected_image - image).max() < 9e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png''')
							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
							_UpperCamelCase									=    load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
							    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''')

							_UpperCamelCase									=    '''stabilityai/stable-diffusion-2-inpainting'''
							_UpperCamelCase									=    StableDiffusionInpaintPipeline.from_pretrained(
							    __a							,						torch_dtype=torch.floataa							,						safety_checker=__a							,						)
							pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing()

							_UpperCamelCase									=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

							_UpperCamelCase									=    torch.manual_seed(0)
							_UpperCamelCase									=    pipe(
							    prompt=__a							,						image=__a							,						mask_image=__a							,						generator=__a							,						output_type='''np'''							,						)
							_UpperCamelCase									=    output.images[0]

							assert image.shape == (5_12, 5_12, 3)
							assert np.abs(expected_image - image).max() < 5e-1




		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png''')
							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

							_UpperCamelCase									=    '''stabilityai/stable-diffusion-2-inpainting'''
							_UpperCamelCase									=    PNDMScheduler.from_pretrained(__a							,						subfolder='''scheduler''')
							_UpperCamelCase									=    StableDiffusionInpaintPipeline.from_pretrained(
							    __a							,						safety_checker=__a							,						scheduler=__a							,						torch_dtype=torch.floataa							,						)
							pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing(1)
							pipe.enable_sequential_cpu_offload()

							_UpperCamelCase									=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

							_UpperCamelCase									=    torch.manual_seed(0)
							_UpperCamelCase									=    pipe(
							    prompt=__a							,						image=__a							,						mask_image=__a							,						generator=__a							,						num_inference_steps=2							,						output_type='''np'''							,						)

							_UpperCamelCase									=    torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9











"""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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






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

						return flax_key_tuple, flax_tensor

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						if "metadata" in layer:
												snake_case   :			Dict						    = layer.split("metadata" )
												snake_case   :			Optional[Any]						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Any						    = [tuple(("metadata" + split_layer[1]).split("/" ) )]
						elif "kvstore" in layer:
												snake_case   :			List[str]						    = layer.split("kvstore" )
												snake_case   :			Tuple						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Union[str, Any]						    = [tuple(("kvstore" + split_layer[1]).split("/" ) )]

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

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

						return curr_real_layer_name, split_layer, content

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = rename_keys(lowerCamelCase_ )
						snake_case   :			str						    = {}
						for k, v in current_block.items():
												snake_case   :			List[str]						    = v
						snake_case   :			List[str]						    = new_current_block
						torch.save(lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							str = WEIGHTS_NAME ):
						"""simple docstring"""






						snake_case   :			List[str]						    = convert_file_size_to_int(lowerCamelCase_ )
						snake_case   :			List[Any]						    = []
						snake_case   :			Dict						    = {}
						snake_case   :			str						    = 0
						snake_case   :			List[str]						    = 0

						os.makedirs(lowerCamelCase_     ,		exist_ok=lowerCamelCase_ )
						with gfile.GFile(switch_checkpoint_path + "/checkpoint"     ,		"rb" ) as fp:
												snake_case   :			List[Any]						    = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
												snake_case   :			Union[str, Any]						    = flatten_dict(lowerCamelCase_     ,		sep="/" )

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

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

												# use the renaming pattern from the small conversion scripts
												snake_case    ,			snake_case   :			Dict						    = rename_base_flax_keys(tuple(key.split("/" ) )     ,		lowerCamelCase_ )
												snake_case   :			Union[str, Any]						    = "/".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:
																		snake_case   :			str						    = 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
																		snake_case   :			Any						    = {}
																		snake_case   :			Union[str, Any]						    = 0

												snake_case   :			Any						    = raw_weights.to(getattr(lowerCamelCase_     ,		lowerCamelCase_ ) )
												current_block_size += weight_size
												total_size += weight_size

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

    # Add the metadata
						snake_case   :			Optional[int]						    = {"total_size": total_size}
						snake_case   :			List[str]						    = {"metadata": metadata, "weight_map": weight_map}

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

						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  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

						snake_case   :			Dict						    = tokenizer(lowerCamelCase_     ,		return_tensors="pt" ).input_ids
						snake_case   :			Optional[int]						    = model.generate(lowerCamelCase_     ,		decoder_start_token_id=0 )
						print(tokenizer.decode(out[0] ) )



