'''simple docstring'''




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_a   :	Tuple 							=   {
    "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   :	Optional[Any] 							=   {
    "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[str] 							=   {
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

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

def _lowercase	(      lowerCamelCase__       )					->       Tuple:





       """simple docstring"""


       if isinstance(lowerCamelCase__							,    lowerCamelCase__       ):
              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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=False       )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
       __UpperCAmelCase  :				Any       				=		checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
       __UpperCAmelCase  :				Optional[Any]       				=		checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
       __UpperCAmelCase  :				List[Any]       				=		checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
       __UpperCAmelCase  :				Dict       				=		checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
       __UpperCAmelCase  :				List[str]       				=		checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
       __UpperCAmelCase  :				List[str]       				=		checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
       __UpperCAmelCase  :				Optional[int]       				=		checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
       __UpperCAmelCase  :				Optional[int]       				=		checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
       __UpperCAmelCase  :				Union[str, Any]       				=		checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

       if has_skip:
              __UpperCAmelCase  :				List[str]       				=		checkpoint[f"""{old_prefix}.skip_connection.weight"""]
              __UpperCAmelCase  :				int       				=		checkpoint[f"""{old_prefix}.skip_connection.bias"""]

       return new_checkpoint

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





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3							,    dim=0       )
       __UpperCAmelCase  :				List[str]       				=		checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3							,    dim=0       )

       __UpperCAmelCase  :				Any       				=		checkpoint[f"""{old_prefix}.norm.weight"""]
       __UpperCAmelCase  :				int       				=		checkpoint[f"""{old_prefix}.norm.bias"""]

       __UpperCAmelCase  :				Union[str, Any]       				=		weight_q.squeeze(-1       ).squeeze(-1       )
       __UpperCAmelCase  :				int       				=		bias_q.squeeze(-1       ).squeeze(-1       )
       __UpperCAmelCase  :				List[Any]       				=		weight_k.squeeze(-1       ).squeeze(-1       )
       __UpperCAmelCase  :				Any       				=		bias_k.squeeze(-1       ).squeeze(-1       )
       __UpperCAmelCase  :				List[str]       				=		weight_v.squeeze(-1       ).squeeze(-1       )
       __UpperCAmelCase  :				Any       				=		bias_v.squeeze(-1       ).squeeze(-1       )

       __UpperCAmelCase  :				Dict       				=		(
           checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1       ).squeeze(-1       )
       )
       __UpperCAmelCase  :				List[str]       				=		checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1       ).squeeze(-1       )

       return new_checkpoint

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__       )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		torch.load(lowerCamelCase__							,    map_location="cpu"       )
       __UpperCAmelCase  :				Union[str, Any]       				=		{}

       __UpperCAmelCase  :				Tuple       				=		checkpoint["time_embed.0.weight"]
       __UpperCAmelCase  :				Any       				=		checkpoint["time_embed.0.bias"]
       __UpperCAmelCase  :				Union[str, Any]       				=		checkpoint["time_embed.2.weight"]
       __UpperCAmelCase  :				List[Any]       				=		checkpoint["time_embed.2.bias"]

       if unet_config["num_class_embeds"] is not None:
              __UpperCAmelCase  :				str       				=		checkpoint["label_emb.weight"]

       __UpperCAmelCase  :				Union[str, Any]       				=		checkpoint["input_blocks.0.0.weight"]
       __UpperCAmelCase  :				Optional[int]       				=		checkpoint["input_blocks.0.0.bias"]

       __UpperCAmelCase  :				Tuple       				=		unet_config["down_block_types"]
       __UpperCAmelCase  :				Optional[Any]       				=		unet_config["layers_per_block"]
       __UpperCAmelCase  :				Optional[Any]       				=		unet_config["attention_head_dim"]
       __UpperCAmelCase  :				int       				=		unet_config["block_out_channels"]
       __UpperCAmelCase  :				int       				=		1
       __UpperCAmelCase  :				List[Any]       				=		channels_list[0]

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

              elif layer_type == "AttnDownBlock2D":
                     for j in range(lowerCamelCase__       ):
                            __UpperCAmelCase  :				str       				=		f"""down_blocks.{i}.resnets.{j}"""
                            __UpperCAmelCase  :				Any       				=		f"""input_blocks.{current_layer}.0"""
                            __UpperCAmelCase  :				str       				=		True if j == 0 and downsample_block_has_skip else False
                            __UpperCAmelCase  :				Union[str, Any]       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    has_skip=lowerCamelCase__       )
                            __UpperCAmelCase  :				List[str]       				=		f"""down_blocks.{i}.attentions.{j}"""
                            __UpperCAmelCase  :				Dict       				=		f"""input_blocks.{current_layer}.1"""
                            __UpperCAmelCase  :				Union[str, Any]       				=		convert_attention(
                                lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )
                            current_layer += 1

              if i != len(lowerCamelCase__       ) - 1:
                     __UpperCAmelCase  :				Dict       				=		f"""down_blocks.{i}.downsamplers.0"""
                     __UpperCAmelCase  :				int       				=		f"""input_blocks.{current_layer}.0"""
                     __UpperCAmelCase  :				Union[str, Any]       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )
                     current_layer += 1

              __UpperCAmelCase  :				Union[str, Any]       				=		current_channels

       # hardcoded the mid-block for now
       __UpperCAmelCase  :				str       				=		"mid_block.resnets.0"
       __UpperCAmelCase  :				Optional[Any]       				=		"middle_block.0"
       __UpperCAmelCase  :				Optional[int]       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )
       __UpperCAmelCase  :				str       				=		"mid_block.attentions.0"
       __UpperCAmelCase  :				int       				=		"middle_block.1"
       __UpperCAmelCase  :				Union[str, Any]       				=		convert_attention(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )
       __UpperCAmelCase  :				List[str]       				=		"mid_block.resnets.1"
       __UpperCAmelCase  :				int       				=		"middle_block.2"
       __UpperCAmelCase  :				Any       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )

       __UpperCAmelCase  :				Dict       				=		0
       __UpperCAmelCase  :				List[str]       				=		unet_config["up_block_types"]

       for i, layer_type in enumerate(lowerCamelCase__       ):
              if layer_type == "ResnetUpsampleBlock2D":
                     for j in range(layers_per_block + 1       ):
                            __UpperCAmelCase  :				Union[str, Any]       				=		f"""up_blocks.{i}.resnets.{j}"""
                            __UpperCAmelCase  :				Optional[int]       				=		f"""output_blocks.{current_layer}.0"""
                            __UpperCAmelCase  :				Union[str, Any]       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    has_skip=lowerCamelCase__       )
                            current_layer += 1

                     if i != len(lowerCamelCase__       ) - 1:
                            __UpperCAmelCase  :				List[Any]       				=		f"""up_blocks.{i}.upsamplers.0"""
                            __UpperCAmelCase  :				List[Any]       				=		f"""output_blocks.{current_layer-1}.1"""
                            __UpperCAmelCase  :				List[Any]       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )
              elif layer_type == "AttnUpBlock2D":
                     for j in range(layers_per_block + 1       ):
                            __UpperCAmelCase  :				int       				=		f"""up_blocks.{i}.resnets.{j}"""
                            __UpperCAmelCase  :				Union[str, Any]       				=		f"""output_blocks.{current_layer}.0"""
                            __UpperCAmelCase  :				Dict       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    has_skip=lowerCamelCase__       )
                            __UpperCAmelCase  :				Union[str, Any]       				=		f"""up_blocks.{i}.attentions.{j}"""
                            __UpperCAmelCase  :				str       				=		f"""output_blocks.{current_layer}.1"""
                            __UpperCAmelCase  :				Tuple       				=		convert_attention(
                                lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )
                            current_layer += 1

                     if i != len(lowerCamelCase__       ) - 1:
                            __UpperCAmelCase  :				Any       				=		f"""up_blocks.{i}.upsamplers.0"""
                            __UpperCAmelCase  :				List[Any]       				=		f"""output_blocks.{current_layer-1}.2"""
                            __UpperCAmelCase  :				Optional[int]       				=		convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__       )

       __UpperCAmelCase  :				Union[str, Any]       				=		checkpoint["out.0.weight"]
       __UpperCAmelCase  :				Optional[int]       				=		checkpoint["out.0.bias"]
       __UpperCAmelCase  :				Optional[int]       				=		checkpoint["out.2.weight"]
       __UpperCAmelCase  :				List[Any]       				=		checkpoint["out.2.bias"]

       return new_checkpoint


if __name__ == "__main__":
   _a   :	Optional[int] 							=   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   :	Optional[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   :	Optional[int] 							=   IMAGENET_64_UNET_CONFIG
   elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
      _a   :	str 							=   LSUN_256_UNET_CONFIG
   elif "test" in ckpt_name:
      _a   :	List[Any] 							=   TEST_UNET_CONFIG
   else:
      raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

   if not args.class_cond:
      _a   :	List[str] 							=   None

   _a   :	str 							=   con_pt_to_diffuser(args.unet_path, unet_config)

   _a   :	str 							=   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   :	Union[str, Any] 							=   CD_SCHEDULER_CONFIG
   elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
      _a   :	List[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   :	Optional[int] 							=   CMStochasticIterativeScheduler(**scheduler_config)

   _a   :	Optional[Any] 							=   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
   consistency_model.save_pretrained(args.dump_path)







'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )






@require_torch
class 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		3
              __UpperCAmelCase  :				Tuple       				=		2_50

              __UpperCAmelCase  :				str       				=		ids_tensor((batch_size, length)				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		torch.ones((batch_size, length)				,     device=UpperCamelCase_				,     dtype=torch.float      ) / length
              return input_ids, scores






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				Tuple       				=		StoppingCriteriaList(
                  [
                      MaxLengthCriteria(max_length=10      ),
                      MaxTimeCriteria(max_time=0.1      ),
                  ]      )

              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		MaxLengthCriteria(max_length=10      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		MaxNewTokensCriteria(start_length=5				,     max_new_tokens=5      )

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StoppingCriteriaList([criteria]      )
              self.assertEqual(criteria_list.max_length				,     10      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1				,     initial_timestamp=time.time() - 0.2      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )







       def 			_snake_case						(      self      ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     10      )

              with self.assertWarns(UpperCamelCase_      ):
                     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     11      )

              __UpperCAmelCase  :				Optional[int]       				=		validate_stopping_criteria(StoppingCriteriaList()				,     11      )

              self.assertEqual(len(UpperCamelCase_      )				,     1      )






