"""simple docstring"""


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__UpperCamelCase									=			{
    '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',
}

__UpperCamelCase									=			{
    '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',
}

__UpperCamelCase									=			{
    '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',
}

__UpperCamelCase									=			{
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__UpperCamelCase									=			{
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

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







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      List[str]:
     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 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=False					)				->      str:
     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 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=None					)				->      Union[str, Any]:
     snake_case_					       =					checkpoint[f'{old_prefix}.qkv.weight'].chunk(3						,					dim=0					)
     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 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      Any:
     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__":
    __UpperCamelCase									=			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.''')

    __UpperCamelCase									=			parser.parse_args()
    __UpperCamelCase									=			strabool(args.class_cond)

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

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

    if not args.class_cond:
        __UpperCamelCase									=			None

    __UpperCamelCase									=			con_pt_to_diffuser(args.unet_path, unet_config)

    __UpperCamelCase									=			UNetaDModel(**unet_config)
    image_unet.load_state_dict(converted_unet_ckpt)

    # Get scheduler config
    if "cd" in ckpt_name or "test" in ckpt_name:
        __UpperCamelCase									=			CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        __UpperCamelCase									=			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)):
        __UpperCamelCase									=			CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

    __UpperCamelCase									=			CMStochasticIterativeScheduler(**scheduler_config)

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






"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


