




"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a_        =       {
    'sample_size': 3_2,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [3_2, 6_4],
    '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': 6_4,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    'attention_head_dim': 6_4,
    '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': 2_5_6,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    'attention_head_dim': 6_4,
    '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': 4_0,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

a_        =       {
    'num_train_timesteps': 2_0_1,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

a_        =       {
    'num_train_timesteps': 1_5_1,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}







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

			if has_skip:
						__lowercase			:  Optional[Any]								=       checkpoint[f"""{old_prefix}.skip_connection.weight"""]
						__lowercase			:  int								=       checkpoint[f"""{old_prefix}.skip_connection.bias"""]

			return new_checkpoint







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=None	):
			__lowercase			,__lowercase			,__lowercase			:  Tuple								=       checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3	,				dim=0	)
			__lowercase			,__lowercase			,__lowercase			:  List[Any]								=       checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3	,				dim=0	)

			__lowercase			:  List[str]								=       checkpoint[f"""{old_prefix}.norm.weight"""]
			__lowercase			:  Tuple								=       checkpoint[f"""{old_prefix}.norm.bias"""]

			__lowercase			:  Tuple								=       weight_q.squeeze(-1	).squeeze(-1	)
			__lowercase			:  int								=       bias_q.squeeze(-1	).squeeze(-1	)
			__lowercase			:  int								=       weight_k.squeeze(-1	).squeeze(-1	)
			__lowercase			:  Optional[int]								=       bias_k.squeeze(-1	).squeeze(-1	)
			__lowercase			:  int								=       weight_v.squeeze(-1	).squeeze(-1	)
			__lowercase			:  str								=       bias_v.squeeze(-1	).squeeze(-1	)

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

			return new_checkpoint







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  int								=       torch.load(__UpperCamelCase	,				map_location='''cpu'''	)
			__lowercase			:  Union[str, Any]								=       {}

			__lowercase			:  Union[str, Any]								=       checkpoint['''time_embed.0.weight''']
			__lowercase			:  List[Any]								=       checkpoint['''time_embed.0.bias''']
			__lowercase			:  Dict								=       checkpoint['''time_embed.2.weight''']
			__lowercase			:  Optional[int]								=       checkpoint['''time_embed.2.bias''']

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

			__lowercase			:  Any								=       checkpoint['''input_blocks.0.0.weight''']
			__lowercase			:  str								=       checkpoint['''input_blocks.0.0.bias''']

			__lowercase			:  Dict								=       unet_config['''down_block_types''']
			__lowercase			:  str								=       unet_config['''layers_per_block''']
			__lowercase			:  Tuple								=       unet_config['''attention_head_dim''']
			__lowercase			:  List[str]								=       unet_config['''block_out_channels''']
			__lowercase			:  Tuple								=       1
			__lowercase			:  Optional[Any]								=       channels_list[0]

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

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

						if i != len(__UpperCamelCase	) - 1:
									__lowercase			:  int								=       f"""down_blocks.{i}.downsamplers.0"""
									__lowercase			:  Optional[int]								=       f"""input_blocks.{current_layer}.0"""
									__lowercase			:  int								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
									current_layer += 1

						__lowercase			:  Any								=       current_channels

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

			__lowercase			:  Optional[int]								=       0
			__lowercase			:  Optional[int]								=       unet_config['''up_block_types''']

			for i, layer_type in enumerate(__UpperCamelCase	):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1	):
												__lowercase			:  int								=       f"""up_blocks.{i}.resnets.{j}"""
												__lowercase			:  List[str]								=       f"""output_blocks.{current_layer}.0"""
												__lowercase			:  Tuple								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				has_skip=__UpperCamelCase	)
												current_layer += 1

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

									if i != len(__UpperCamelCase	) - 1:
												__lowercase			:  List[str]								=       f"""up_blocks.{i}.upsamplers.0"""
												__lowercase			:  Dict								=       f"""output_blocks.{current_layer-1}.2"""
												__lowercase			:  Dict								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)

			__lowercase			:  Tuple								=       checkpoint['''out.0.weight''']
			__lowercase			:  List[Any]								=       checkpoint['''out.0.bias''']
			__lowercase			:  str								=       checkpoint['''out.2.weight''']
			__lowercase			:  List[Any]								=       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)










import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_lowerCamelCase      							=   '\\n\n'

_lowerCamelCase      							=   '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

_lowerCamelCase      							=   '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION	)
class       __A							(   datasets.Metric	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





             return datasets.MetricInfo(
                 description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
                     {
                         '''input_texts''': datasets.Value('''string'''),
                     })   ,				reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']   ,				)



      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__ = 16   ,				a__ = True   ,				a__=None):



             """simple docstring"""





             if device is not None:
                    assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
                    if device == "gpu":
                           _lowerCamelCase				:  List[Any]						=		'''cuda'''
             else:
                    _lowerCamelCase				:  Dict						=		'''cuda''' if torch.cuda.is_available() else '''cpu'''

             _lowerCamelCase				:  List[str]						=		AutoModelForCausalLM.from_pretrained(a__)
             _lowerCamelCase				:  Union[str, Any]						=		model.to(a__)

             _lowerCamelCase				:  Tuple						=		AutoTokenizer.from_pretrained(a__)

             # if batch_size > 1 (which generally leads to padding being required), and
             # if there is not an already assigned pad_token, assign an existing
             # special token to also be the padding token
             if tokenizer.pad_token is None and batch_size > 1:
                    _lowerCamelCase				:  Any						=		list(tokenizer.special_tokens_map_extended.values())
                    # check that the model already has at least one special token defined
                    assert (
                        len(a__) > 0
                    ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
                    # assign one of the special tokens to also be the pad token
                    tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]})

             if add_start_token:
                    # leave room for <BOS> token to be added:
                    assert (
                        tokenizer.bos_token is not None
                    ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length - 1
             else:
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length

             _lowerCamelCase				:  Union[str, Any]						=		tokenizer(
                 a__   ,				add_special_tokens=a__   ,				padding=a__   ,				truncation=a__   ,				max_length=a__   ,				return_tensors='''pt'''   ,				return_attention_mask=a__   ,				).to(a__)

             _lowerCamelCase				:  Any						=		encodings['''input_ids''']
             _lowerCamelCase				:  Dict						=		encodings['''attention_mask''']

             # check that each input is long enough:
             if add_start_token:
                    assert torch.all(torch.ge(attn_masks.sum(1)   ,				1)), "Each input text must be at least one token long."
             else:
                    assert torch.all(
                        torch.ge(attn_masks.sum(1)   ,				2)), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

             _lowerCamelCase				:  Dict						=		[]
             _lowerCamelCase				:  Union[str, Any]						=		CrossEntropyLoss(reduction='''none''')

             for start_index in logging.tqdm(range(0   ,				len(a__)   ,				a__)):
                    _lowerCamelCase				:  Any						=		min(start_index + batch_size   ,				len(a__))
                    _lowerCamelCase				:  List[Any]						=		encoded_texts[start_index:end_index]
                    _lowerCamelCase				:  Optional[int]						=		attn_masks[start_index:end_index]

                    if add_start_token:
                           _lowerCamelCase				:  Tuple						=		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(a__)
                           _lowerCamelCase				:  str						=		torch.cat([bos_tokens_tensor, encoded_batch]   ,				dim=1)
                           _lowerCamelCase				:  str						=		torch.cat(
                               [torch.ones(bos_tokens_tensor.size()   ,				dtype=torch.intaa).to(a__), attn_mask]   ,				dim=1)

                    _lowerCamelCase				:  Dict						=		encoded_batch

                    with torch.no_grad():
                           _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__).logits

                    _lowerCamelCase				:  List[str]						=		out_logits[..., :-1, :].contiguous()
                    _lowerCamelCase				:  Dict						=		labels[..., 1:].contiguous()
                    _lowerCamelCase				:  List[Any]						=		attn_mask[..., 1:].contiguous()

                    _lowerCamelCase				:  str						=		torch.expa(
                        (loss_fct(shift_logits.transpose(1   ,				2)   ,				a__) * shift_attention_mask_batch).sum(1)
                        / shift_attention_mask_batch.sum(1))

                    ppls += perplexity_batch.tolist()

             return {"perplexities": ppls, "mean_perplexity": np.mean(a__)}

