


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 		__magic_name__						(snake_case_   ,snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       StableUnCLIPPipeline
     __lowercase				:							Any	   =       TEXT_TO_IMAGE_PARAMS
     __lowercase				:							List[str]	   =       TEXT_TO_IMAGE_BATCH_PARAMS
     __lowercase				:							Optional[int]	   =       TEXT_TO_IMAGE_IMAGE_PARAMS
     __lowercase				:							Any	   =       TEXT_TO_IMAGE_IMAGE_PARAMS

     # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
     __lowercase				:							List[Any]	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 32
       snake_case__						= embedder_hidden_size

       # prior components

       torch.manual_seed(0   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       torch.manual_seed(0   )
       snake_case__						= CLIPTextModelWithProjection(
           CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=_a     ,		projection_dim=_a     ,		intermediate_size=37     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=10_00     ,		)   )

       torch.manual_seed(0   )
       snake_case__						= PriorTransformer(
           num_attention_heads=2     ,		attention_head_dim=12     ,		embedding_dim=_a     ,		num_layers=1     ,		)

       torch.manual_seed(0   )
       snake_case__						= DDPMScheduler(
           variance_type='''fixed_small_log'''     ,		prediction_type='''sample'''     ,		num_train_timesteps=10_00     ,		clip_sample=_a     ,		clip_sample_range=5.0     ,		beta_schedule='''squaredcos_cap_v2'''     ,		)

       # regular denoising components

       torch.manual_seed(0   )
       snake_case__						= StableUnCLIPImageNormalizer(embedding_dim=_a   )
       snake_case__						= DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''   )

       torch.manual_seed(0   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       torch.manual_seed(0   )
       snake_case__						= CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=_a     ,		projection_dim=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=10_00     ,		)   )

       torch.manual_seed(0   )
       snake_case__						= UNetaDConditionModel(
           sample_size=32     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')     ,		up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')     ,		block_out_channels=(32, 64)     ,		attention_head_dim=(2, 4)     ,		class_embed_type='''projection'''     ,		projection_class_embeddings_input_dim=embedder_projection_dim * 2     ,		cross_attention_dim=_a     ,		layers_per_block=1     ,		upcast_attention=_a     ,		use_linear_projection=_a     ,		)

       torch.manual_seed(0   )
       snake_case__						= DDIMScheduler(
           beta_schedule='''scaled_linear'''     ,		beta_start=0.00085     ,		beta_end=0.012     ,		prediction_type='''v_prediction'''     ,		set_alpha_to_one=_a     ,		steps_offset=1     ,		)

       torch.manual_seed(0   )
       snake_case__						= AutoencoderKL()

       snake_case__						= {
           # prior components
           '''prior_tokenizer''': prior_tokenizer,
           '''prior_text_encoder''': prior_text_encoder,
           '''prior''': prior,
           '''prior_scheduler''': prior_scheduler,
           # image noising components
           '''image_normalizer''': image_normalizer,
           '''image_noising_scheduler''': image_noising_scheduler,
           # regular denoising components
           '''tokenizer''': tokenizer,
           '''text_encoder''': text_encoder,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
       }

       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:Tuple     ,		_a:Any=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''prior_num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= torch_device == '''cpu'''

       self._test_attention_slicing_forward_pass(test_max_difference=_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= torch_device in ['''cpu''', '''mps''']

       self._test_inference_batch_single_identical(test_max_difference=_a   )




@slow
@require_torch_gpu
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''   )

       snake_case__						= StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''     ,		torch_dtype=torch.floataa   )
       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       snake_case__						= torch.Generator(device='''cpu'''   ).manual_seed(0   )
       snake_case__						= pipe('''anime turle'''     ,		generator=_a     ,		output_type='''np'''   )

       snake_case__						= output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(_a     ,		_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case__						= StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''     ,		torch_dtype=torch.floataa   )
       snake_case__						= pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       snake_case__						= pipe(
           '''anime turtle'''     ,		prior_num_inference_steps=2     ,		num_inference_steps=2     ,		output_type='''np'''     ,		)

       snake_case__						= torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9





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

import datasets
from datasets import logging


lowerCamelCase__							:						Any							     =   """\

"""

lowerCamelCase__							:						List[str]							     =   """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

lowerCamelCase__							:						Any							     =   """
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to 'cuda' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!='']
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION				)
class 		__magic_name__						(datasets.Metric				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int     ,		_a:List[Any]     ,		_a:int = 16     ,		_a:bool = True     ,		_a:Any=None   ):
       if device is not None:
         assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
         if device == "gpu":
           snake_case__						= '''cuda'''
       else:
         snake_case__						= '''cuda''' if torch.cuda.is_available() else '''cpu'''

       snake_case__						= AutoModelForCausalLM.from_pretrained(_a   )
       snake_case__						= model.to(_a   )

       snake_case__						= 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:
         snake_case__						= 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"
         snake_case__						= model.config.max_length - 1
       else:
         snake_case__						= model.config.max_length

       snake_case__						= tokenizer(
           _a     ,		add_special_tokens=_a     ,		padding=_a     ,		truncation=_a     ,		max_length=_a     ,		return_tensors='''pt'''     ,		return_attention_mask=_a     ,		).to(_a   )

       snake_case__						= encodings['''input_ids''']
       snake_case__						= 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."

       snake_case__						= []
       snake_case__						= CrossEntropyLoss(reduction='''none'''   )

       for start_index in logging.tqdm(range(0     ,		len(_a   )     ,		_a   )   ):
         snake_case__						= min(start_index + batch_size     ,		len(_a   )   )
         snake_case__						= encoded_texts[start_index:end_index]
         snake_case__						= attn_masks[start_index:end_index]

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

         snake_case__						= encoded_batch

         with torch.no_grad():
           snake_case__						= model(_a     ,		attention_mask=_a   ).logits

         snake_case__						= out_logits[..., :-1, :].contiguous()
         snake_case__						= labels[..., 1:].contiguous()
         snake_case__						= attn_mask[..., 1:].contiguous()

         snake_case__						= 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   )}

