




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': 10_00,
    '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': 10_00,
    'block_out_channels': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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': 2_56,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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': 2_01,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_lowerCamelCase      			=						{
    'num_train_timesteps': 1_51,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							Union[str, Any]:
    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Dict=False				)  ->							List[Any]:
    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						int=None				)  ->							Union[str, Any]:
    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						List[str]				)  ->							str:
    UpperCAmelCase_										=					torch.load(__UpperCamelCase	,					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(__UpperCamelCase				):
        UpperCAmelCase_										=					channels_list[i]
        UpperCAmelCase_										=					current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(__UpperCamelCase				):
                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(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					has_skip=__UpperCamelCase				)
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(__UpperCamelCase				):
                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(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					has_skip=__UpperCamelCase				)
                UpperCAmelCase_										=					f'down_blocks.{i}.attentions.{j}'
                UpperCAmelCase_										=					f'input_blocks.{current_layer}.1'
                UpperCAmelCase_										=					convert_attention(
                    __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
                current_layer += 1

        if i != len(__UpperCamelCase				) - 1:
            UpperCAmelCase_										=					f'down_blocks.{i}.downsamplers.0'
            UpperCAmelCase_										=					f'input_blocks.{current_layer}.0'
            UpperCAmelCase_										=					convert_resnet(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
            current_layer += 1

        UpperCAmelCase_										=					current_channels

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

    UpperCAmelCase_										=					0
    UpperCAmelCase_										=					unet_config['''up_block_types''']

    for i, layer_type in enumerate(__UpperCamelCase				):
        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(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					has_skip=__UpperCamelCase				)
                current_layer += 1

            if i != len(__UpperCamelCase				) - 1:
                UpperCAmelCase_										=					f'up_blocks.{i}.upsamplers.0'
                UpperCAmelCase_										=					f'output_blocks.{current_layer-1}.1'
                UpperCAmelCase_										=					convert_resnet(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
        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(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					has_skip=__UpperCamelCase				)
                UpperCAmelCase_										=					f'up_blocks.{i}.attentions.{j}'
                UpperCAmelCase_										=					f'output_blocks.{current_layer}.1'
                UpperCAmelCase_										=					convert_attention(
                    __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
                current_layer += 1

            if i != len(__UpperCamelCase				) - 1:
                UpperCAmelCase_										=					f'up_blocks.{i}.upsamplers.0'
                UpperCAmelCase_										=					f'output_blocks.{current_layer-1}.2'
                UpperCAmelCase_										=					convert_resnet(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

    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__":
 _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)











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[str]=5				)  ->							Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''				) == 1
    UpperCAmelCase_										=					torch.tensor(tokenizer.encode(__UpperCamelCase	,					add_special_tokens=__UpperCamelCase				)				).unsqueeze(0				)  # Batch size 1
    UpperCAmelCase_										=					model(__UpperCamelCase				)[0]  # The last hidden-state is the first element of the output tuple
    UpperCAmelCase_										=					(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    UpperCAmelCase_										=					logits[0, masked_index, :]
    UpperCAmelCase_										=					logits.softmax(dim=0				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					prob.topk(k=__UpperCamelCase	,					dim=0				)
    UpperCAmelCase_										=					''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCamelCase				)				)]				)
    UpperCAmelCase_										=					tokenizer.mask_token
    UpperCAmelCase_										=					[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''				)				):
        UpperCAmelCase_										=					predicted_token_bpe.replace('''\u2581'''	,					''' '''				)
        if " {0}".format(__UpperCamelCase				) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(__UpperCamelCase				)	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(__UpperCamelCase	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
    return topk_filled_outputs


_lowerCamelCase      			=						CamembertTokenizer.from_pretrained('camembert-base')
_lowerCamelCase      			=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_lowerCamelCase      			=						'Le camembert est <mask> :)'


print(fill_mask(masked_input, model, tokenizer, topk=3))





