
"""simple docstring"""





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_a														=		{
    """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														=		{
    """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														=		{
    """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														=		{
    """num_train_timesteps""": 40,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

_a														=		{
    """num_train_timesteps""": 201,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

_a														=		{
    """num_train_timesteps""": 151,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}



def 				lowerCamelCase__		(      __snake_case     )							->		Tuple:


					"""simple docstring"""





					if isinstance(__snake_case,					__snake_case     ):
										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 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case=False     )							->		Union[str, Any]:


					"""simple docstring"""





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

					if has_skip:
										_UpperCamelCase										=  checkpoint[F'''{old_prefix}.skip_connection.weight''']
										_UpperCamelCase										=  checkpoint[F'''{old_prefix}.skip_connection.bias''']

					return new_checkpoint



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case=None     )							->		Optional[Any]:


					"""simple docstring"""





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

					_UpperCamelCase										=  checkpoint[F'''{old_prefix}.norm.weight''']
					_UpperCamelCase										=  checkpoint[F'''{old_prefix}.norm.bias''']

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

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

					return new_checkpoint



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  torch.load(__snake_case,					map_location='''cpu'''     )
					_UpperCamelCase										=  {}

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

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

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

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

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

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

										if i != len(__snake_case     ) - 1:
															_UpperCamelCase										=  F'''down_blocks.{i}.downsamplers.0'''
															_UpperCamelCase										=  F'''input_blocks.{current_layer}.0'''
															_UpperCamelCase										=  convert_resnet(__snake_case,					__snake_case,					__snake_case,					__snake_case     )
															current_layer += 1

										_UpperCamelCase										=  current_channels

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

					_UpperCamelCase										=  0
					_UpperCamelCase										=  unet_config['''up_block_types''']

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

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

															if i != len(__snake_case     ) - 1:
																				_UpperCamelCase										=  F'''up_blocks.{i}.upsamplers.0'''
																				_UpperCamelCase										=  F'''output_blocks.{current_layer-1}.2'''
																				_UpperCamelCase										=  convert_resnet(__snake_case,					__snake_case,					__snake_case,					__snake_case     )

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

					return new_checkpoint


if __name__ == "__main__":
				_a														=		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														=		parser.parse_args()
				_a														=		strabool(args.class_cond)

				_a														=		os.path.basename(args.unet_path)
				print(F"""Checkpoint: {ckpt_name}""")

				# Get U-Net config
				if "imagenet64" in ckpt_name:
								_a														=		IMAGENET_64_UNET_CONFIG
				elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
								_a														=		LSUN_256_UNET_CONFIG
				elif "test" in ckpt_name:
								_a														=		TEST_UNET_CONFIG
				else:
								raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

				if not args.class_cond:
								_a														=		None

				_a														=		con_pt_to_diffuser(args.unet_path, unet_config)

				_a														=		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														=		CD_SCHEDULER_CONFIG
				elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
								_a														=		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														=		CT_LSUN_256_SCHEDULER_CONFIG
				else:
								raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

				_a														=		CMStochasticIterativeScheduler(**scheduler_config)

				_a														=		ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
				consistency_model.save_pretrained(args.dump_path)








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






