

"""simple docstring"""


# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class    snake_case     (    __snake_case,							__snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  StableDiffusionControlNetImgaImgPipeline
				SCREAMING_SNAKE_CASE_       :				int       				=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_       :				str       				=  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}  )
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  IMAGE_TO_IMAGE_IMAGE_PARAMS

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Union[str, Any]   				=      UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)					,  layers_per_block=2					,  sample_size=3_2					,  in_channels=4					,  out_channels=4					,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,  cross_attention_dim=3_2					,  )
						torch.manual_seed(0)
						__lowerCAmelCase:	List[Any]   				=      ControlNetModel(
						    block_out_channels=(3_2, 6_4)					,  layers_per_block=2					,  in_channels=4					,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,  cross_attention_dim=3_2					,  conditioning_embedding_out_channels=(1_6, 3_2)					,  )
						torch.manual_seed(0)
						__lowerCAmelCase:	str   				=      DDIMScheduler(
						    beta_start=0.00085					,  beta_end=0.012					,  beta_schedule="scaled_linear"					,  clip_sample=UpperCamelCase__					,  set_alpha_to_one=UpperCamelCase__					,  )
						torch.manual_seed(0)
						__lowerCAmelCase:	Tuple   				=      AutoencoderKL(
						    block_out_channels=[3_2, 6_4]					,  in_channels=3					,  out_channels=3					,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,  latent_channels=4					,  )
						torch.manual_seed(0)
						__lowerCAmelCase:	Optional[Any]   				=      CLIPTextConfig(
						    bos_token_id=0					,  eos_token_id=2					,  hidden_size=3_2					,  intermediate_size=3_7					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1_0_0_0					,  )
						__lowerCAmelCase:	Tuple   				=      CLIPTextModel(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__lowerCAmelCase:	List[str]   				=      {
						    "unet": unet,
						    "controlnet": controlnet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Optional[int]=0)->       List[str]:



						'''simple docstring'''


						if str(UpperCamelCase__).startswith("mps"):
								__lowerCAmelCase:	List[Any]   				=      torch.manual_seed(UpperCamelCase__)
						else:
								__lowerCAmelCase:	Tuple   				=      torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)

						__lowerCAmelCase:	int   				=      2
						__lowerCAmelCase:	Any   				=      randn_tensor(
						    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,  generator=UpperCamelCase__					,  device=torch.device(UpperCamelCase__)					,  )
						__lowerCAmelCase:	str   				=      floats_tensor(control_image.shape					,  rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
						__lowerCAmelCase:	str   				=      image.cpu().permute(0					,  2					,  3					,  1)[0]
						__lowerCAmelCase:	List[str]   				=      Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((6_4, 6_4))
						__lowerCAmelCase:	Tuple   				=      {
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						    "image": image,
						    "control_image": control_image,
						}

						return inputs

				def 							lowercase_					(     self							:    Optional[int])->       List[str]:



						'''simple docstring'''


						return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()					,  reason="XFormers attention is only available with CUDA and `xformers` installed"					,  )
				def 							lowercase_					(     self							:    Tuple)->       Optional[int]:



						'''simple docstring'''


						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)




				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						self._test_inference_batch_single_identical(expected_max_diff=2e-3)





class    snake_case     (    __snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  StableDiffusionControlNetImgaImgPipeline
				SCREAMING_SNAKE_CASE_       :				int       				=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				SCREAMING_SNAKE_CASE_       :				str       				=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_       :				str       				=  frozenset([]  )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess

				def 							lowercase_					(     self							:    Optional[int])->       List[Any]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	str   				=      UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)					,  layers_per_block=2					,  sample_size=3_2					,  in_channels=4					,  out_channels=4					,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,  cross_attention_dim=3_2					,  )
						torch.manual_seed(0)

						def init_weights(UpperCamelCase__							:    str):
								if isinstance(UpperCamelCase__					,  torch.nn.Convad):
										torch.nn.init.normal(m.weight)
										m.bias.data.fill_(1.0)

						__lowerCAmelCase:	Optional[int]   				=      ControlNetModel(
						    block_out_channels=(3_2, 6_4)					,  layers_per_block=2					,  in_channels=4					,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,  cross_attention_dim=3_2					,  conditioning_embedding_out_channels=(1_6, 3_2)					,  )
						controlneta.controlnet_down_blocks.apply(UpperCamelCase__)

						torch.manual_seed(0)
						__lowerCAmelCase:	Dict   				=      ControlNetModel(
						    block_out_channels=(3_2, 6_4)					,  layers_per_block=2					,  in_channels=4					,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,  cross_attention_dim=3_2					,  conditioning_embedding_out_channels=(1_6, 3_2)					,  )
						controlneta.controlnet_down_blocks.apply(UpperCamelCase__)

						torch.manual_seed(0)
						__lowerCAmelCase:	Tuple   				=      DDIMScheduler(
						    beta_start=0.00085					,  beta_end=0.012					,  beta_schedule="scaled_linear"					,  clip_sample=UpperCamelCase__					,  set_alpha_to_one=UpperCamelCase__					,  )
						torch.manual_seed(0)
						__lowerCAmelCase:	Optional[Any]   				=      AutoencoderKL(
						    block_out_channels=[3_2, 6_4]					,  in_channels=3					,  out_channels=3					,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,  latent_channels=4					,  )
						torch.manual_seed(0)
						__lowerCAmelCase:	Any   				=      CLIPTextConfig(
						    bos_token_id=0					,  eos_token_id=2					,  hidden_size=3_2					,  intermediate_size=3_7					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1_0_0_0					,  )
						__lowerCAmelCase:	str   				=      CLIPTextModel(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__lowerCAmelCase:	Optional[Any]   				=      MultiControlNetModel([controlneta, controlneta])

						__lowerCAmelCase:	Dict   				=      {
						    "unet": unet,
						    "controlnet": controlnet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    int=0)->       Optional[int]:



						'''simple docstring'''


						if str(UpperCamelCase__).startswith("mps"):
								__lowerCAmelCase:	Tuple   				=      torch.manual_seed(UpperCamelCase__)
						else:
								__lowerCAmelCase:	Dict   				=      torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      2

						__lowerCAmelCase:	Optional[Any]   				=      [
						    randn_tensor(
						        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,  generator=UpperCamelCase__					,  device=torch.device(UpperCamelCase__)					,  ),
						    randn_tensor(
						        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,  generator=UpperCamelCase__					,  device=torch.device(UpperCamelCase__)					,  ),
						]

						__lowerCAmelCase:	Dict   				=      floats_tensor(control_image[0].shape					,  rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      image.cpu().permute(0					,  2					,  3					,  1)[0]
						__lowerCAmelCase:	str   				=      Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((6_4, 6_4))
						__lowerCAmelCase:	Optional[Any]   				=      {
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						    "image": image,
						    "control_image": control_image,
						}

						return inputs

				def 							lowercase_					(     self							:    int)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.get_dummy_components()
						__lowerCAmelCase:	List[str]   				=      self.pipeline_class(**UpperCamelCase__)
						pipe.to(UpperCamelCase__)

						__lowerCAmelCase:	Union[str, Any]   				=      10.0
						__lowerCAmelCase:	Dict   				=      4

						__lowerCAmelCase:	Optional[int]   				=      self.get_dummy_inputs(UpperCamelCase__)
						__lowerCAmelCase:	str   				=      steps
						__lowerCAmelCase:	Any   				=      scale
						__lowerCAmelCase:	Union[str, Any]   				=      pipe(**UpperCamelCase__)[0]

						__lowerCAmelCase:	List[str]   				=      self.get_dummy_inputs(UpperCamelCase__)
						__lowerCAmelCase:	str   				=      steps
						__lowerCAmelCase:	Tuple   				=      scale
						__lowerCAmelCase:	int   				=      pipe(**UpperCamelCase__					,  control_guidance_start=0.1					,  control_guidance_end=0.2)[0]

						__lowerCAmelCase:	Any   				=      self.get_dummy_inputs(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      steps
						__lowerCAmelCase:	int   				=      scale
						__lowerCAmelCase:	int   				=      pipe(**UpperCamelCase__					,  control_guidance_start=[0.1, 0.3]					,  control_guidance_end=[0.2, 0.7])[0]

						__lowerCAmelCase:	Optional[int]   				=      self.get_dummy_inputs(UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      steps
						__lowerCAmelCase:	List[str]   				=      scale
						__lowerCAmelCase:	Optional[Any]   				=      pipe(**UpperCamelCase__					,  control_guidance_start=0.4					,  control_guidance_end=[0.5, 0.8])[0]

						# make sure that all outputs are different
						assert np.sum(np.abs(output_a - output_a)) > 1e-3
						assert np.sum(np.abs(output_a - output_a)) > 1e-3
						assert np.sum(np.abs(output_a - output_a)) > 1e-3

				def 							lowercase_					(     self							:    str)->       Any:



						'''simple docstring'''


						return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()					,  reason="XFormers attention is only available with CUDA and `xformers` installed"					,  )
				def 							lowercase_					(     self							:    List[str])->       List[str]:



						'''simple docstring'''


						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)

				def 							lowercase_					(     self							:    Optional[int])->       Optional[Any]:



						'''simple docstring'''


						self._test_inference_batch_single_identical(expected_max_diff=2e-3)




				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.get_dummy_components()
						__lowerCAmelCase:	List[str]   				=      self.pipeline_class(**UpperCamelCase__)
						pipe.to(UpperCamelCase__)
						pipe.set_progress_bar_config(disable=UpperCamelCase__)
						with tempfile.TemporaryDirectory() as tmpdir:
								try:
										# save_pretrained is not implemented for Multi-ControlNet
										pipe.save_pretrained(UpperCamelCase__)
								except NotImplementedError:
										pass





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

				def 							lowercase_					(     self							:    int)->       Union[str, Any]:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




				def 							lowercase_					(     self							:    List[Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")

						__lowerCAmelCase:	Dict   				=      StableDiffusionControlNetImgaImgPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"					,  safety_checker=UpperCamelCase__					,  controlnet=UpperCamelCase__)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      torch.Generator(device="cpu").manual_seed(0)
						__lowerCAmelCase:	Union[str, Any]   				=      "evil space-punk bird"
						__lowerCAmelCase:	Tuple   				=      load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png").resize((5_1_2, 5_1_2))
						__lowerCAmelCase:	List[Any]   				=      load_image(
						    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png").resize((5_1_2, 5_1_2))

						__lowerCAmelCase:	int   				=      pipe(
						    UpperCamelCase__					,  UpperCamelCase__					,  control_image=UpperCamelCase__					,  generator=UpperCamelCase__					,  output_type="np"					,  num_inference_steps=5_0					,  strength=0.6					,  )

						__lowerCAmelCase:	int   				=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)

						__lowerCAmelCase:	List[Any]   				=      load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy")

						assert np.abs(expected_image - image).max() < 9e-2





"""simple docstring"""


import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	int   				=      args.log_outputs
		__lowerCAmelCase:	Tuple   				=      "_".join(args.dataset.split("/"			) + [args.config, args.split]			)

		# load metric
		__lowerCAmelCase:	str   				=      load_metric("wer"			)
		__lowerCAmelCase:	int   				=      load_metric("cer"			)

		# compute metrics
		__lowerCAmelCase:	Optional[Any]   				=      wer.compute(references=result["target"] ,			predictions=result["prediction"]			)
		__lowerCAmelCase:	Dict   				=      cer.compute(references=result["target"] ,			predictions=result["prediction"]			)

		# print & log results
		__lowerCAmelCase:	Optional[int]   				=      F"WER: {wer_result}\nCER: {cer_result}"
		print(__SCREAMING_SNAKE_CASE			)

		with open(F"{dataset_id}_eval_results.txt" ,			"w"			) as f:
				f.write(__SCREAMING_SNAKE_CASE			)

		# log all results in text file. Possibly interesting for analysis
		if log_outputs is not None:
				__lowerCAmelCase:	int   				=      F"log_{dataset_id}_predictions.txt"
				__lowerCAmelCase:	Tuple   				=      F"log_{dataset_id}_targets.txt"

				with open(__SCREAMING_SNAKE_CASE ,			"w"			) as p, open(__SCREAMING_SNAKE_CASE ,			"w"			) as t:
						# mapping function to write output
						def write_to_file(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			):
								p.write(F"{i}" + "\n"			)
								p.write(batch["prediction"] + "\n"			)
								t.write(F"{i}" + "\n"			)
								t.write(batch["target"] + "\n"			)

						result.map(__SCREAMING_SNAKE_CASE ,			with_indices=__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Any   				=      "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

		__lowerCAmelCase:	List[Any]   				=      re.sub(__SCREAMING_SNAKE_CASE ,			"" ,			text.lower()			)

		# In addition, we can normalize the target text, e.g. removing new lines characters etc...
		# note that order is important here!
		__lowerCAmelCase:	Optional[int]   				=      ["\n\n", "\n", "   ", "  "]

		for t in token_sequences_to_ignore:
				__lowerCAmelCase:	str   				=      " ".join(text.split(__SCREAMING_SNAKE_CASE			)			)

		return text






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Dict:
		# load dataset
		__lowerCAmelCase:	List[str]   				=      load_dataset(args.dataset ,			args.config ,			split=args.split ,			use_auth_token=__SCREAMING_SNAKE_CASE			)

		# for testing: only process the first two examples as a test
		# dataset = dataset.select(range(10))

		# load processor
		__lowerCAmelCase:	Dict   				=      AutoFeatureExtractor.from_pretrained(args.model_id			)
		__lowerCAmelCase:	str   				=      feature_extractor.sampling_rate

		# resample audio
		__lowerCAmelCase:	Tuple   				=      dataset.cast_column("audio" ,			Audio(sampling_rate=__SCREAMING_SNAKE_CASE			)			)

		# load eval pipeline
		if args.device is None:
				__lowerCAmelCase:	List[Any]   				=      0 if torch.cuda.is_available() else -1
		__lowerCAmelCase:	Optional[int]   				=      pipeline("automatic-speech-recognition" ,			model=args.model_id ,			device=args.device			)

		# map function to decode audio
		def map_to_pred(__SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase:	Union[str, Any]   				=      asr(
				    batch["audio"]["array"] ,			chunk_length_s=args.chunk_length_s ,			stride_length_s=args.stride_length_s			)

				__lowerCAmelCase:	Any   				=      prediction["text"]
				__lowerCAmelCase:	List[str]   				=      normalize_text(batch["sentence"]			)
				return batch

		# run inference on all examples
		__lowerCAmelCase:	str   				=      dataset.map(__SCREAMING_SNAKE_CASE ,			remove_columns=dataset.column_names			)

		# compute and log_results
		# do not change function below
		log_results(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()

			parser.add_argument(
			    "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
			)
			parser.add_argument(
			    "--dataset",
			    type=str,
			    required=True,
			    help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
			)
			parser.add_argument(
			    "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
			)
			parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
			parser.add_argument(
			    "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
			)
			parser.add_argument(
			    "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
			)
			parser.add_argument(
			    "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
			)
			parser.add_argument(
			    "--device",
			    type=int,
			    default=None,
			    help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
			)
			__A						       =      parser.parse_args()

			main(args)


