




'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a  :				Dict									=  {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a  :				List[str]									=  {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a  :				Optional[Any]									=  {
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a  :				Optional[Any]									=  {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

a  :				List[Any]									=  {
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

a  :				int									=  {
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}


def 		__magic_name__    (	__UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
										return v
					if v.lower() in ("yes", "true", "t", "y", "1"):
										return True
					elif v.lower() in ("no", "false", "f", "n", "0"):
										return False
					else:
										raise argparse.ArgumentTypeError('''boolean value expected'''	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=False	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				checkpoint[F"{old_prefix}.in_layers.0.weight"]
					snake_case_									=				checkpoint[F"{old_prefix}.in_layers.0.bias"]
					snake_case_									=				checkpoint[F"{old_prefix}.in_layers.2.weight"]
					snake_case_									=				checkpoint[F"{old_prefix}.in_layers.2.bias"]
					snake_case_									=				checkpoint[F"{old_prefix}.emb_layers.1.weight"]
					snake_case_									=				checkpoint[F"{old_prefix}.emb_layers.1.bias"]
					snake_case_									=				checkpoint[F"{old_prefix}.out_layers.0.weight"]
					snake_case_									=				checkpoint[F"{old_prefix}.out_layers.0.bias"]
					snake_case_									=				checkpoint[F"{old_prefix}.out_layers.3.weight"]
					snake_case_									=				checkpoint[F"{old_prefix}.out_layers.3.bias"]

					if has_skip:
										snake_case_									=				checkpoint[F"{old_prefix}.skip_connection.weight"]
										snake_case_									=				checkpoint[F"{old_prefix}.skip_connection.bias"]

					return new_checkpoint


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=None	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_			,snake_case_			,snake_case_									=				checkpoint[F"{old_prefix}.qkv.weight"].chunk(3,      dim=0	)
					snake_case_			,snake_case_			,snake_case_									=				checkpoint[F"{old_prefix}.qkv.bias"].chunk(3,      dim=0	)

					snake_case_									=				checkpoint[F"{old_prefix}.norm.weight"]
					snake_case_									=				checkpoint[F"{old_prefix}.norm.bias"]

					snake_case_									=				weight_q.squeeze(-1	).squeeze(-1	)
					snake_case_									=				bias_q.squeeze(-1	).squeeze(-1	)
					snake_case_									=				weight_k.squeeze(-1	).squeeze(-1	)
					snake_case_									=				bias_k.squeeze(-1	).squeeze(-1	)
					snake_case_									=				weight_v.squeeze(-1	).squeeze(-1	)
					snake_case_									=				bias_v.squeeze(-1	).squeeze(-1	)

					snake_case_									=				(
					    checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1	).squeeze(-1	)
					)
					snake_case_									=				checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1	).squeeze(-1	)

					return new_checkpoint


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				{}

					snake_case_									=				checkpoint['''time_embed.0.weight''']
					snake_case_									=				checkpoint['''time_embed.0.bias''']
					snake_case_									=				checkpoint['''time_embed.2.weight''']
					snake_case_									=				checkpoint['''time_embed.2.bias''']

					if unet_config["num_class_embeds"] is not None:
										snake_case_									=				checkpoint['''label_emb.weight''']

					snake_case_									=				checkpoint['''input_blocks.0.0.weight''']
					snake_case_									=				checkpoint['''input_blocks.0.0.bias''']

					snake_case_									=				unet_config['''down_block_types''']
					snake_case_									=				unet_config['''layers_per_block''']
					snake_case_									=				unet_config['''attention_head_dim''']
					snake_case_									=				unet_config['''block_out_channels''']
					snake_case_									=				1
					snake_case_									=				channels_list[0]

					for i, layer_type in enumerate(__UpperCAmelCase	):
										snake_case_									=				channels_list[i]
										snake_case_									=				current_channels != prev_channels
										if layer_type == "ResnetDownsampleBlock2D":
															for j in range(__UpperCAmelCase	):
																				snake_case_									=				F"down_blocks.{i}.resnets.{j}"
																				snake_case_									=				F"input_blocks.{current_layer}.0"
																				snake_case_									=				True if j == 0 and downsample_block_has_skip else False
																				snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      has_skip=__UpperCAmelCase	)
																				current_layer += 1

										elif layer_type == "AttnDownBlock2D":
															for j in range(__UpperCAmelCase	):
																				snake_case_									=				F"down_blocks.{i}.resnets.{j}"
																				snake_case_									=				F"input_blocks.{current_layer}.0"
																				snake_case_									=				True if j == 0 and downsample_block_has_skip else False
																				snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      has_skip=__UpperCAmelCase	)
																				snake_case_									=				F"down_blocks.{i}.attentions.{j}"
																				snake_case_									=				F"input_blocks.{current_layer}.1"
																				snake_case_									=				convert_attention(
																				    __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
																				current_layer += 1

										if i != len(__UpperCAmelCase	) - 1:
															snake_case_									=				F"down_blocks.{i}.downsamplers.0"
															snake_case_									=				F"input_blocks.{current_layer}.0"
															snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
															current_layer += 1

										snake_case_									=				current_channels

					# hardcoded the mid-block for now
					snake_case_									=				'''mid_block.resnets.0'''
					snake_case_									=				'''middle_block.0'''
					snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
					snake_case_									=				'''mid_block.attentions.0'''
					snake_case_									=				'''middle_block.1'''
					snake_case_									=				convert_attention(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
					snake_case_									=				'''mid_block.resnets.1'''
					snake_case_									=				'''middle_block.2'''
					snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)

					snake_case_									=				0
					snake_case_									=				unet_config['''up_block_types''']

					for i, layer_type in enumerate(__UpperCAmelCase	):
										if layer_type == "ResnetUpsampleBlock2D":
															for j in range(layers_per_block + 1	):
																				snake_case_									=				F"up_blocks.{i}.resnets.{j}"
																				snake_case_									=				F"output_blocks.{current_layer}.0"
																				snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      has_skip=__UpperCAmelCase	)
																				current_layer += 1

															if i != len(__UpperCAmelCase	) - 1:
																				snake_case_									=				F"up_blocks.{i}.upsamplers.0"
																				snake_case_									=				F"output_blocks.{current_layer-1}.1"
																				snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
										elif layer_type == "AttnUpBlock2D":
															for j in range(layers_per_block + 1	):
																				snake_case_									=				F"up_blocks.{i}.resnets.{j}"
																				snake_case_									=				F"output_blocks.{current_layer}.0"
																				snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      has_skip=__UpperCAmelCase	)
																				snake_case_									=				F"up_blocks.{i}.attentions.{j}"
																				snake_case_									=				F"output_blocks.{current_layer}.1"
																				snake_case_									=				convert_attention(
																				    __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
																				current_layer += 1

															if i != len(__UpperCAmelCase	) - 1:
																				snake_case_									=				F"up_blocks.{i}.upsamplers.0"
																				snake_case_									=				F"output_blocks.{current_layer-1}.2"
																				snake_case_									=				convert_resnet(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)

					snake_case_									=				checkpoint['''out.0.weight''']
					snake_case_									=				checkpoint['''out.0.bias''']
					snake_case_									=				checkpoint['''out.2.weight''']
					snake_case_									=				checkpoint['''out.2.bias''']

					return new_checkpoint


if __name__ == "__main__":
							a  :				List[str]									=  argparse.ArgumentParser()

							parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
							parser.add_argument(
							    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
							)
							parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

							a  :				Any									=  parser.parse_args()
							a  :				List[Any]									=  strabool(args.class_cond)

							a  :				Any									=  os.path.basename(args.unet_path)
							print(f'''Checkpoint: {ckpt_name}''')

							# Get U-Net config
							if "imagenet64" in ckpt_name:
														a  :				str									=  IMAGENET_64_UNET_CONFIG
							elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														a  :				List[str]									=  LSUN_256_UNET_CONFIG
							elif "test" in ckpt_name:
														a  :				Optional[int]									=  TEST_UNET_CONFIG
							else:
														raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

							if not args.class_cond:
														a  :				List[Any]									=  None

							a  :				Optional[Any]									=  con_pt_to_diffuser(args.unet_path, unet_config)

							a  :				Tuple									=  UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

							# Get scheduler config
							if "cd" in ckpt_name or "test" in ckpt_name:
														a  :				List[Any]									=  CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														a  :				Union[str, Any]									=  CT_IMAGENET_64_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														a  :				List[str]									=  CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

							a  :				Dict									=  CMStochasticIterativeScheduler(**scheduler_config)

							a  :				Any									=  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :				str									=  logging.get_logger(__name__)

a  :				str									=  {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"big_bird"


				def __init__(		self    :					Union[str, Any]       ,				lowercase_    :					List[Any]=5_0358       ,				lowercase_    :					Tuple=768       ,				lowercase_    :					Dict=12       ,				lowercase_    :					str=12       ,				lowercase_    :					Tuple=3072       ,				lowercase_    :					Any="gelu_new"       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					List[Any]=4096       ,				lowercase_    :					List[Any]=2       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					Optional[int]=1e-12       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Tuple=0       ,				lowercase_    :					str=1       ,				lowercase_    :					Union[str, Any]=2       ,				lowercase_    :					Optional[Any]=66       ,				lowercase_    :					Optional[int]="block_sparse"       ,				lowercase_    :					Any=True       ,				lowercase_    :					List[str]=False       ,				lowercase_    :					Any=64       ,				lowercase_    :					Tuple=3       ,				lowercase_    :					Tuple=None       ,				**lowercase_    :					Tuple       ,				):
									super().__init__(
									    pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				sep_token_id=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_size
									snake_case_									=				max_position_embeddings
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				initializer_range
									snake_case_									=				type_vocab_size
									snake_case_									=				layer_norm_eps
									snake_case_									=				use_cache

									snake_case_									=				rescale_embeddings
									snake_case_									=				attention_type
									snake_case_									=				use_bias
									snake_case_									=				block_size
									snake_case_									=				num_random_blocks
									snake_case_									=				classifier_dropout







class 						a				(   _lowerCamelCase     ):


				@property
				def   A_ (		self    :					str					):
									if self.task == "multiple-choice":
														snake_case_									=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														snake_case_									=				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]					)

