



'''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  ,   _lowerCAmelCase					):



							"""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  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""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  ,   _lowerCAmelCase					):



							"""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  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase = WEIGHTS_NAME					):



							"""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	 =       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	 =       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

							__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

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class _UpperCamelCase		(     A      ,       A      ,       A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    AltDiffusionPipeline
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_IMAGE_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_IMAGE_PARAMS

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											torch.manual_seed(0)
											__lowercase       	=UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)  ,       layers_per_block=2  ,       sample_size=3_2  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,       cross_attention_dim=3_2  ,       )
											__lowercase       	=DDIMScheduler(
											    beta_start=0.0_0085  ,       beta_end=0.012  ,       beta_schedule='scaled_linear'  ,       clip_sample=_lowerCAmelCase  ,       set_alpha_to_one=_lowerCAmelCase  ,       )
											torch.manual_seed(0)
											__lowercase       	=AutoencoderKL(
											    block_out_channels=[3_2, 6_4]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,       latent_channels=4  ,       )

											# TODO: address the non-deterministic text encoder (fails for save-load tests)
											# torch.manual_seed(0)
											# text_encoder_config = RobertaSeriesConfig(
											#     hidden_size=32,
											#     project_dim=32,
											#     intermediate_size=37,
											#     layer_norm_eps=1e-05,
											#     num_attention_heads=4,
											#     num_hidden_layers=5,
											#     vocab_size=5002,
											# )
											# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

											torch.manual_seed(0)
											__lowercase       	=CLIPTextConfig(
											    bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=3_2  ,       projection_dim=3_2  ,       intermediate_size=3_7  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=5_0_0_2  ,       )
											__lowercase       	=CLIPTextModel(_lowerCAmelCase)

											__lowercase       	=XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
											__lowercase       	=7_7

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

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  Dict=0):







											'''simple docstring'''

											if str(_lowerCAmelCase).startswith('mps'):
																		__lowercase       	=torch.manual_seed(_lowerCAmelCase)
											else:
																		__lowercase       	=torch.Generator(device=_lowerCAmelCase).manual_seed(_lowerCAmelCase)
											__lowercase       	={
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											super().test_inference_batch_single_identical(expected_max_diff=3e-3)

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	='cpu'  # ensure determinism for the device-dependent torch.Generator

											__lowercase       	=self.get_dummy_components()
											torch.manual_seed(0)
											__lowercase       	=RobertaSeriesConfig(
											    hidden_size=3_2  ,       project_dim=3_2  ,       intermediate_size=3_7  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       vocab_size=5_0_0_2  ,       )
											# TODO: remove after fixing the non-deterministic text encoder
											__lowercase       	=RobertaSeriesModelWithTransformation(_lowerCAmelCase)
											__lowercase       	=text_encoder

											__lowercase       	=AltDiffusionPipeline(**_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=self.get_dummy_inputs(_lowerCAmelCase)
											__lowercase       	='A photo of an astronaut'
											__lowercase       	=alt_pipe(**_lowerCAmelCase)
											__lowercase       	=output.images
											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__lowercase       	=np.array(
											    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

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

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	='cpu'  # ensure determinism for the device-dependent torch.Generator

											__lowercase       	=self.get_dummy_components()
											__lowercase       	=PNDMScheduler(skip_prk_steps=_lowerCAmelCase)
											torch.manual_seed(0)
											__lowercase       	=RobertaSeriesConfig(
											    hidden_size=3_2  ,       project_dim=3_2  ,       intermediate_size=3_7  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       vocab_size=5_0_0_2  ,       )
											# TODO: remove after fixing the non-deterministic text encoder
											__lowercase       	=RobertaSeriesModelWithTransformation(_lowerCAmelCase)
											__lowercase       	=text_encoder
											__lowercase       	=AltDiffusionPipeline(**_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=self.get_dummy_inputs(_lowerCAmelCase)
											__lowercase       	=alt_pipe(**_lowerCAmelCase)
											__lowercase       	=output.images
											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__lowercase       	=np.array(
											    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

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





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





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,       safety_checker=_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	='A painting of a squirrel eating a burger'
											__lowercase       	=torch.manual_seed(0)
											__lowercase       	=alt_pipe([prompt]  ,       generator=_lowerCAmelCase  ,       guidance_scale=6.0  ,       num_inference_steps=2_0  ,       output_type='np')

											__lowercase       	=output.images

											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowercase       	=np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

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

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=DDIMScheduler.from_pretrained('BAAI/AltDiffusion'  ,       subfolder='scheduler')

											__lowercase       	=AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,       scheduler=_lowerCAmelCase  ,       safety_checker=_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	='A painting of a squirrel eating a burger'
											__lowercase       	=torch.manual_seed(0)

											__lowercase       	=alt_pipe([prompt]  ,       generator=_lowerCAmelCase  ,       num_inference_steps=2  ,       output_type='numpy')
											__lowercase       	=output.images

											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowercase       	=np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

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



