

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCamelCase           =					{
    '''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''',
}

lowerCamelCase           =					{
    '''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''',
}

lowerCamelCase           =					{
    '''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''',
}

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

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

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




def 			UpperCAmelCase__				(			_A      : str							):





  '''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 			UpperCAmelCase__				(			_A      : int	,      _A      : Optional[Any]	,      _A      : Optional[int]	,      _A      : List[str]	,      _A      : List[Any]=False							):





  '''simple docstring'''







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

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

  return new_checkpoint




def 			UpperCAmelCase__				(			_A      : Optional[Any]	,      _A      : Union[str, Any]	,      _A      : List[str]	,      _A      : Optional[Any]	,      _A      : int=None							):





  '''simple docstring'''







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

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

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

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

  return new_checkpoint




def 			UpperCAmelCase__				(			_A      : str	,      _A      : Optional[int]							):





  '''simple docstring'''







  a__  =torch.load(_lowerCamelCase	,      map_location='''cpu'''							)
  a__  ={}

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

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

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

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

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

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

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

    a__  =current_channels

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

  a__  =0
  a__  =unet_config['''up_block_types''']

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

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

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

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

  return new_checkpoint


if __name__ == "__main__":
       lowerCamelCase           =					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.''')

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

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

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

       if not args.class_cond:
              lowerCamelCase           =					None

       lowerCamelCase           =					con_pt_to_diffuser(args.unet_path, unet_config)

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

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

       lowerCamelCase           =					CMStochasticIterativeScheduler(**scheduler_config)

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








"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_SCREAMING_SNAKE_CASE					:				Optional[int]					    =						'''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''






class      a						(					unittest.TestCase  ,      __snake_case		):






    def    UpperCamelCase       (       self     :							int					)     ->		Optional[int]:
      lowerCamelCase_         =						load_tool('text-question-answering'					)
      self.tool.setup()
      lowerCamelCase_         =						load_tool('text-question-answering'       ,						remote=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Dict					)     ->		Tuple:
      lowerCamelCase_         =						self.tool(__SCREAMING_SNAKE_CASE       ,						'What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[int]:
      lowerCamelCase_         =						self.remote_tool(__SCREAMING_SNAKE_CASE       ,						'What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Optional[int]:
      lowerCamelCase_         =						self.tool(text=__SCREAMING_SNAKE_CASE       ,						question='What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		int:
      lowerCamelCase_         =						self.remote_tool(text=__SCREAMING_SNAKE_CASE       ,						question='What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)




