






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

import datasets
from datasets import logging


__lowerCamelCase	:		List[Any]										=   """\

"""

__lowerCamelCase	:		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	:		Optional[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  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self				,			a				,			a				,			a = 1_6				,			a = True				,			a=None   ):
       if device is not None:
        assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
        if device == "gpu":
         snake_case__  :    Optional[Any]							       ="cuda"
       else:
        snake_case__  :    Tuple							       ="cuda" if torch.cuda.is_available() else "cpu"

       snake_case__  :    Tuple							       =AutoModelForCausalLM.from_pretrained(a_   )
       snake_case__  :    List[str]							       =model.to(a_   )

       snake_case__  :    Any							       =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__  :    str							       =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__  :    List[str]							       =model.config.max_length - 1
       else:
        snake_case__  :    int							       =model.config.max_length

       snake_case__  :    Any							       =tokenizer(
           a_				,			add_special_tokens=a_				,			padding=a_				,			truncation=a_				,			max_length=a_				,			return_tensors="""pt"""				,			return_attention_mask=a_				,			).to(a_   )

       snake_case__  :    Dict							       =encodings["input_ids"]
       snake_case__  :    Union[str, Any]							       =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__  :    str							       =[]
       snake_case__  :    Union[str, Any]							       =CrossEntropyLoss(reduction="""none"""   )

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

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

        snake_case__  :    Tuple							       =encoded_batch

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

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

        snake_case__  :    Optional[int]							       =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_   )}













import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						Any										=							KandinskyImgaImgPipeline
      _a							:						List[Any]										=							['prompt', 'image_embeds', 'negative_image_embeds', 'image']
      _a							:						int										=							[
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
      ]
      _a							:						int										=							[
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _a							:						List[str]										=							False
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim * 4
      @property
      def 						lowercase__					(     self   ):
       return 1_0_0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""   )
       return tokenizer
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    Optional[Any]							       =MCLIPConfig(
           numDims=self.cross_attention_dim				,			transformerDimensions=self.text_embedder_hidden_size				,			hidden_size=self.text_embedder_hidden_size				,			intermediate_size=3_7				,			num_attention_heads=4				,			num_hidden_layers=5				,			vocab_size=1_0_0_5				,			)

       snake_case__  :    Optional[int]							       =MultilingualCLIP(a   )
       snake_case__  :    Union[str, Any]							       =text_encoder.eval()

       return text_encoder
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    Optional[int]							       ={
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       snake_case__  :    List[str]							       =UNetaDConditionModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       return {
           "block_out_channels": [3_2, 6_4],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 1_2,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    List[Any]							       =VQModel(**self.dummy_movq_kwargs   )
       return model
      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =self.dummy_text_encoder
       snake_case__  :    Tuple							       =self.dummy_tokenizer
       snake_case__  :    List[str]							       =self.dummy_unet
       snake_case__  :    Dict							       =self.dummy_movq

       snake_case__  :    Union[str, Any]							       ={
           """num_train_timesteps""": 1_0_0_0,
           """beta_schedule""": """linear""",
           """beta_start""": 0.00085,
           """beta_end""": 0.012,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       snake_case__  :    Union[str, Any]							       =DDIMScheduler(**a   )

       snake_case__  :    Any							       ={
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components
      def 						lowercase__					(     self				,			a				,			a=0   ):
       snake_case__  :    str							       =floats_tensor((1, self.cross_attention_dim)				,			rng=random.Random(a   )   ).to(a   )
       snake_case__  :    Tuple							       =floats_tensor((1, self.cross_attention_dim)				,			rng=random.Random(seed + 1   )   ).to(a   )
       # create init_image
       snake_case__  :    str							       =floats_tensor((1, 3, 6_4, 6_4)				,			rng=random.Random(a   )   ).to(a   )
       snake_case__  :    Tuple							       =image.cpu().permute(0				,			2				,			3				,			1   )[0]
       snake_case__  :    Dict							       =Image.fromarray(np.uinta(a   )   ).convert("""RGB"""   ).resize((2_5_6, 2_5_6)   )

       if str(a   ).startswith("""mps"""   ):
        snake_case__  :    Dict							       =torch.manual_seed(a   )
       else:
        snake_case__  :    Optional[Any]							       =torch.Generator(device=a   ).manual_seed(a   )
       snake_case__  :    str							       ={
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 6_4,
           """width""": 6_4,
           """num_inference_steps""": 1_0,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs



      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="""cpu"""

       snake_case__  :    Tuple							       =self.get_dummy_components()

       snake_case__  :    Optional[int]							       =self.pipeline_class(**a   )
       snake_case__  :    Union[str, Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[Any]							       =pipe(**self.get_dummy_inputs(a   )   )
       snake_case__  :    List[str]							       =output.images

       snake_case__  :    int							       =pipe(
           **self.get_dummy_inputs(a   )				,			return_dict=a				,			)[0]

       snake_case__  :    str							       =image[0, -3:, -3:, -1]
       snake_case__  :    Tuple							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       snake_case__  :    List[str]							       =np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]   )
       assert (
           np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_img2img_frog.npy"""   )

       snake_case__  :    Dict							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""   )
       snake_case__  :    Any							       ="""A red cartoon frog, 4k"""

       snake_case__  :    Dict							       =KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""				,			torch_dtype=torch.floataa   )
       pipe_prior.to(a   )

       snake_case__  :    Tuple							       =KandinskyImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1"""				,			torch_dtype=torch.floataa   )
       snake_case__  :    str							       =pipeline.to(a   )

       pipeline.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[Any]							       =torch.Generator(device="""cpu"""   ).manual_seed(0   )
       snake_case__       ,   snake_case__  :    Any							       =pipe_prior(
           a				,			generator=a				,			num_inference_steps=5				,			negative_prompt=""""""				,			).to_tuple()

       snake_case__  :    str							       =pipeline(
           a				,			image=a				,			image_embeds=a				,			negative_image_embeds=a				,			generator=a				,			num_inference_steps=1_0_0				,			height=7_6_8				,			width=7_6_8				,			strength=0.2				,			output_type="""np"""				,			)

       snake_case__  :    Optional[int]							       =output.images[0]

       assert image.shape == (7_6_8, 7_6_8, 3)

       assert_mean_pixel_difference(a				,			a   )





