




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
UpperCAmelCase_									=	pytest.mark.integration


UpperCAmelCase_									=	{'comet'}
UpperCAmelCase_									=	importlib.util.find_spec('fairseq') is not None

UpperCAmelCase_									=	{'code_eval'}
UpperCAmelCase_									=	os.name == 'nt'

UpperCAmelCase_									=	{'bertscore', 'frugalscore', 'perplexity'}
UpperCAmelCase_									=	importlib.util.find_spec('transformers') is not None




def 					lowerCamelCase__       (						A__       :					Any	):





       '''simple docstring'''



       @wraps(A__	)
       def wrapper(self       :					List[str]  ,			A__       :					str	):
              if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
                     self.skipTest("""\"test requires Fairseq\""""	)
              else:
                     test_case(self  ,			A__	)

       return wrapper




def 					lowerCamelCase__       (						A__       :					Optional[Any]	):





       '''simple docstring'''



       @wraps(A__	)
       def wrapper(self       :					List[str]  ,			A__       :					Any	):
              if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                     self.skipTest("""\"test requires transformers\""""	)
              else:
                     test_case(self  ,			A__	)

       return wrapper




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       @wraps(A__	)
       def wrapper(self       :					Union[str, Any]  ,			A__       :					int	):
              if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
                     self.skipTest("""\"test not supported on Windows\""""	)
              else:
                     test_case(self  ,			A__	)

       return wrapper




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						[metric_dir.split(os.sep	)[-2] for metric_dir in glob.glob("""./metrics/*/"""	)]
       return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names())
@for_all_test_methods(
    __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase)
@local
class     lowerCamelCase__(					parameterized.TestCase):
      UpperCAmelCase__      :	Union[str, Any]    		=   {}
      UpperCAmelCase__      :	str    		=   None
      @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""   )
      @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						"""[...]"""
             __lowerCamelCase   =						importlib.import_module(
                 datasets.load.metric_module_factory(os.path.join("""metrics"""    ,       UpperCamelCase_   )   ).module_path   )
             __lowerCamelCase   =						datasets.load.import_main_class(metric_module.__name__    ,       dataset=UpperCamelCase_   )
             # check parameters
             __lowerCamelCase   =						inspect.signature(metric._compute   ).parameters
             self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()   )   )  # no **kwargs
             # run doctest
             with self.patch_intensive_calls(UpperCamelCase_    ,       metric_module.__name__   ):
                    with self.use_local_metrics():
                           try:
                                  __lowerCamelCase   =						doctest.testmod(UpperCamelCase_    ,       verbose=UpperCamelCase_    ,       raise_on_error=UpperCamelCase_   )
                           except doctest.UnexpectedException as e:
                                  raise e.exc_info[1]  # raise the exception that doctest caught
             self.assertEqual(results.failed    ,       0   )
             self.assertGreater(results.attempted    ,       1   )
      @slow
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Tuple   ):
             __lowerCamelCase   =						"""[...]"""
             __lowerCamelCase   =						importlib.import_module(
                 datasets.load.metric_module_factory(os.path.join("""metrics"""    ,       UpperCamelCase_   )   ).module_path   )
             # run doctest
             with self.use_local_metrics():
                    __lowerCamelCase   =						doctest.testmod(UpperCamelCase_    ,       verbose=UpperCamelCase_    ,       raise_on_error=UpperCamelCase_   )
             self.assertEqual(results.failed    ,       0   )
             self.assertGreater(results.attempted    ,       1   )
      @contextmanager
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: int   ):
             if metric_name in self.INTENSIVE_CALLS_PATCHER:
                    with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCamelCase_   ):
                           yield
             else:
                    yield
      @contextmanager
      def 			lowerCAmelCase__						(				self: str   ):
             def load_local_metric(UpperCamelCase_: int    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Any   ):
                    return load_metric(os.path.join("""metrics"""    ,       UpperCamelCase_   )    ,       *UpperCamelCase_    ,       **UpperCamelCase_   )

             with patch("""datasets.load_metric"""   ) as mock_load_metric:
                    __lowerCamelCase   =						load_local_metric
                    yield




      @classmethod
      def 			lowerCAmelCase__						(				cls: Union[str, Any]    ,       UpperCamelCase_: Any   ):
             def wrapper(UpperCamelCase_: Any   ):
                    __lowerCamelCase   =						contextmanager(UpperCamelCase_   )
                    __lowerCamelCase   =						patcher
                    return patcher

             return wrapper




@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""	)
def 					lowerCamelCase__       (						A__       :					Union[str, Any]	):





       '''simple docstring'''



       import tensorflow.compat.va as tf
       from bleurt.score import Predictor

       tf.flags.DEFINE_string("""sv"""  ,			""""""  ,			""""""	)  # handle pytest cli flags





       class     lowerCamelCase__(					__lowerCamelCase):
             def 			lowerCAmelCase__						(				self: Any    ,       UpperCamelCase_: Any   ):
                    assert len(input_dict["""input_ids"""]   ) == 2
                    return np.array([1.03, 1.04]   )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
       with patch("""bleurt.score._create_predictor"""	) as mock_create_predictor:
              __lowerCamelCase   =						MockedPredictor()
              yield




@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""	)
def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       import torch

       def bert_cos_score_idf(A__       :					Optional[Any]  ,			A__       :					List[str]  ,			*A__       :					List[str]  ,			**A__       :					str	):
              return torch.tensor([[1.0, 1.0, 1.0]] * len(A__	)	)

       # mock get_model which is supposed to do download a bert model
       # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
       with patch("""bert_score.scorer.get_model"""	), patch(
           """bert_score.scorer.bert_cos_score_idf"""	) as mock_bert_cos_score_idf:
              __lowerCamelCase   =						bert_cos_score_idf
              yield




@LocalMetricTest.register_intensive_calls_patcher("""comet"""	)
def 					lowerCamelCase__       (						A__       :					Any	):





       '''simple docstring'''



       def load_from_checkpoint(A__       :					str	):





              class     lowerCamelCase__:
                    def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: Optional[Any]    ,       *UpperCamelCase_: Optional[Any]    ,       **UpperCamelCase_: List[str]   ):
                           assert len(UpperCamelCase_   ) == 2
                           __lowerCamelCase   =						[0.19, 0.92]
                           return scores, sum(UpperCamelCase_   ) / len(UpperCamelCase_   )



              return Model()

       # mock load_from_checkpoint which is supposed to do download a bert model
       # mock load_from_checkpoint which is supposed to do download a bert model
       with patch("""comet.download_model"""	) as mock_download_model:
              __lowerCamelCase   =						None
              with patch("""comet.load_from_checkpoint"""	) as mock_load_from_checkpoint:
                     __lowerCamelCase   =						load_from_checkpoint
                     yield




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						load_metric(os.path.join("""metrics"""  ,			"""seqeval"""	)	)
       __lowerCamelCase   =						"""ERROR"""
       __lowerCamelCase   =						f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
       with pytest.raises(A__  ,			match=re.escape(A__	)	):
              metric.compute(predictions=[]  ,			references=[]  ,			scheme=A__	)







from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase_									=	logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_									=	'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Dict  ,			A__       :					Optional[int]=8	):





       '''simple docstring'''



       __lowerCamelCase   =						height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       __lowerCamelCase   =						width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor





class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: List[Any]    ,       UpperCamelCase_: UNetaDConditionModel    ,       UpperCamelCase_: DDPMScheduler    ,       UpperCamelCase_: VQModel    ,       ):
             super().__init__()

             self.register_modules(
                 unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       movq=UpperCamelCase_    ,       )
             __lowerCamelCase   =						2 ** (len(self.movq.config.block_out_channels   ) - 1)
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: int   ):
             if latents is None:
                    __lowerCamelCase   =						randn_tensor(UpperCamelCase_    ,       generator=UpperCamelCase_    ,       device=UpperCamelCase_    ,       dtype=UpperCamelCase_   )
             else:
                    if latents.shape != shape:
                           raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'   )
                    __lowerCamelCase   =						latents.to(UpperCamelCase_   )

             __lowerCamelCase   =						latents * scheduler.init_noise_sigma
             return latents
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: str=0   ):
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

             __lowerCamelCase   =						torch.device(F'cuda:{gpu_id}'   )

             __lowerCamelCase   =						[
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[int]=0   ):
             if is_accelerate_available() and is_accelerate_version(""">="""    ,       """0.17.0.dev0"""   ):
                    from accelerate import cpu_offload_with_hook
             else:
                    raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""   )

             __lowerCamelCase   =						torch.device(F'cuda:{gpu_id}'   )

             if self.device.type != "cpu":
                    self.to("""cpu"""    ,       silence_dtype_warnings=UpperCamelCase_   )
                    torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

             __lowerCamelCase   =						None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    __lowerCamelCase,   __lowerCamelCase   =						cpu_offload_with_hook(UpperCamelCase_    ,       UpperCamelCase_    ,       prev_module_hook=UpperCamelCase_   )

             # We'll offload the last model manually.
             __lowerCamelCase   =						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 			lowerCAmelCase__						(				self: int   ):
             if not hasattr(self.unet    ,       """_hf_hook"""   ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(UpperCamelCase_    ,       """_hf_hook"""   )
                        and hasattr(module._hf_hook    ,       """execution_device"""   )
                        and module._hf_hook.execution_device is not None
                    ):
                           return torch.device(module._hf_hook.execution_device   )
             return self.device




      @torch.no_grad()
      @replace_example_docstring(UpperCamelCase_   )
      def __call__(				self: Tuple    ,       UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       UpperCamelCase_: torch.FloatTensor    ,       UpperCamelCase_: int = 5_12    ,       UpperCamelCase_: int = 5_12    ,       UpperCamelCase_: int = 1_00    ,       UpperCamelCase_: float = 4.0    ,       UpperCamelCase_: int = 1    ,       UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       UpperCamelCase_: Optional[torch.FloatTensor] = None    ,       UpperCamelCase_: Optional[str] = "pil"    ,       UpperCamelCase_: bool = True    ,       ):
             __lowerCamelCase   =						self._execution_device

             __lowerCamelCase   =						guidance_scale > 1.0

             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						torch.cat(UpperCamelCase_    ,       dim=0   )
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						torch.cat(UpperCamelCase_    ,       dim=0   )
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						torch.cat(UpperCamelCase_    ,       dim=0   )

             __lowerCamelCase   =						image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    __lowerCamelCase   =						image_embeds.repeat_interleave(UpperCamelCase_    ,       dim=0   )
                    __lowerCamelCase   =						negative_image_embeds.repeat_interleave(UpperCamelCase_    ,       dim=0   )
                    __lowerCamelCase   =						hint.repeat_interleave(UpperCamelCase_    ,       dim=0   )

             __lowerCamelCase   =						torch.cat([negative_image_embeds, image_embeds]    ,       dim=0   ).to(dtype=self.unet.dtype    ,       device=UpperCamelCase_   )
             __lowerCamelCase   =						torch.cat([hint, hint]    ,       dim=0   ).to(dtype=self.unet.dtype    ,       device=UpperCamelCase_   )
             self.scheduler.set_timesteps(UpperCamelCase_    ,       device=UpperCamelCase_   )
             __lowerCamelCase   =						self.scheduler.timesteps

             __lowerCamelCase   =						self.movq.config.latent_channels

             __lowerCamelCase,   __lowerCamelCase   =						downscale_height_and_width(UpperCamelCase_    ,       UpperCamelCase_    ,       self.movq_scale_factor   )

             # create initial latent
             __lowerCamelCase   =						self.prepare_latents(
                 (batch_size, num_channels_latents, height, width)    ,       image_embeds.dtype    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       self.scheduler    ,       )

             for i, t in enumerate(self.progress_bar(UpperCamelCase_   )   ):
                    # expand the latents if we are doing classifier free guidance
                    __lowerCamelCase   =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

                    __lowerCamelCase   =						{"""image_embeds""": image_embeds, """hint""": hint}
                    __lowerCamelCase   =						self.unet(
                        sample=UpperCamelCase_    ,       timestep=UpperCamelCase_    ,       encoder_hidden_states=UpperCamelCase_    ,       added_cond_kwargs=UpperCamelCase_    ,       return_dict=UpperCamelCase_    ,       )[0]

                    if do_classifier_free_guidance:
                           __lowerCamelCase,   __lowerCamelCase   =						noise_pred.split(latents.shape[1]    ,       dim=1   )
                           __lowerCamelCase,   __lowerCamelCase   =						noise_pred.chunk(2   )
                           __lowerCamelCase,   __lowerCamelCase   =						variance_pred.chunk(2   )
                           __lowerCamelCase   =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                           __lowerCamelCase   =						torch.cat([noise_pred, variance_pred_text]    ,       dim=1   )

                    if not (
                        hasattr(self.scheduler.config    ,       """variance_type"""   )
                        and self.scheduler.config.variance_type in ["learned", "learned_range"]
                    ):
                           __lowerCamelCase,   __lowerCamelCase   =						noise_pred.split(latents.shape[1]    ,       dim=1   )

                    # compute the previous noisy sample x_t -> x_t-1
                    __lowerCamelCase   =						self.scheduler.step(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       generator=UpperCamelCase_    ,       )[0]
             # post-processing
             __lowerCamelCase   =						self.movq.decode(UpperCamelCase_    ,       force_not_quantize=UpperCamelCase_   )["""sample"""]

             if output_type not in ["pt", "np", "pil"]:
                    raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'   )

             if output_type in ["np", "pil"]:
                    __lowerCamelCase   =						image * 0.5 + 0.5
                    __lowerCamelCase   =						image.clamp(0    ,       1   )
                    __lowerCamelCase   =						image.cpu().permute(0    ,       2    ,       3    ,       1   ).float().numpy()

             if output_type == "pil":
                    __lowerCamelCase   =						self.numpy_to_pil(UpperCamelCase_   )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase_   )

