import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

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


SCREAMING_SNAKE_CASE :	Optional[int]							     =		False
@skip_mps
class    A_ (       a_			,       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			StableDiffusionAttendAndExcitePipeline
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			TEXT_TO_IMAGE_PARAMS
  _SCREAMING_SNAKE_CASE    				=			TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""} )
  _SCREAMING_SNAKE_CASE    				=			TEXT_TO_IMAGE_IMAGE_PARAMS
  _SCREAMING_SNAKE_CASE    				=			TEXT_TO_IMAGE_IMAGE_PARAMS

  @classmethod
  def 		_UpperCAmelCase   (		cls :      Dict       ):
   super().setUpClass()
   torch.use_deterministic_algorithms(__SCREAMING_SNAKE_CASE       )

  @classmethod
  def 		_UpperCAmelCase   (		cls :      Dict       ):
   super().tearDownClass()
   torch.use_deterministic_algorithms(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   torch.manual_seed(0       )
   __a       =  UNetaDConditionModel(
       block_out_channels=(32, 64)     ,							layers_per_block=1     ,							sample_size=32     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,							cross_attention_dim=32     ,							attention_head_dim=(2, 4)     ,							use_linear_projection=__SCREAMING_SNAKE_CASE     ,							)
   __a       =  DDIMScheduler(
       beta_start=0.0_00_85     ,							beta_end=0.0_12     ,							beta_schedule="scaled_linear"     ,							clip_sample=__SCREAMING_SNAKE_CASE     ,							set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,							)
   torch.manual_seed(0       )
   __a       =  AutoencoderKL(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=4     ,							sample_size=1_28     ,							)
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=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     ,							hidden_act="gelu"     ,							projection_dim=5_12     ,							)
   __a       =  CLIPTextModel(__SCREAMING_SNAKE_CASE       )
   __a       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

   __a       =  {
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "safety_checker": None,
       "feature_extractor": None,
   }

   return components

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0       ):
   if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
    __a       =  torch.manual_seed(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
   __a       =  __a       =  {
       "prompt": "a cat and a frog",
       "token_indices": [2, 5],
       "generator": generator,
       "num_inference_steps": 1,
       "guidance_scale": 6.0,
       "output_type": "numpy",
       "max_iter_to_alter": 2,
       "thresholds": {0: 0.7},
   }
   return inputs

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  "cpu"

   __a       =  self.get_dummy_components()
   __a       =  self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
   pipe.to(__SCREAMING_SNAKE_CASE       )
   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
   __a       =  pipe(**__SCREAMING_SNAKE_CASE       ).images
   __a       =  image[0, -3:, -3:, -1]

   self.assertEqual(image.shape     ,							(1, 64, 64, 3)       )
   __a       =  np.array(
       [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]       )
   __a       =  np.abs(image_slice.flatten() - expected_slice       ).max()
   self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,							1E-3       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   super().test_cpu_offload_forward_pass(expected_max_diff=5E-4       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
   self._test_inference_batch_consistent(batch_sizes=[1, 2]       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   self._test_inference_batch_single_identical(batch_size=2     ,							expected_max_diff=7E-4       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   super().test_save_load_local(expected_max_difference=5E-4       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   super().test_save_load_optional_components(expected_max_difference=4E-4       )
@require_torch_gpu
@slow
class    A_ (       unittest.TestCase ):

  @classmethod
  def 		_UpperCAmelCase   (		cls :      Union[str, Any]       ):
   super().setUpClass()
   torch.use_deterministic_algorithms(__SCREAMING_SNAKE_CASE       )

  @classmethod
  def 		_UpperCAmelCase   (		cls :      Tuple       ):
   super().tearDownClass()
   torch.use_deterministic_algorithms(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  torch.manual_seed(51       )

   __a       =  StableDiffusionAttendAndExcitePipeline.from_pretrained(
       "CompVis/stable-diffusion-v1-4"     ,							safety_checker=__SCREAMING_SNAKE_CASE     ,							torch_dtype=torch.floataa       )
   pipe.to("cuda"       )

   __a       =  "a painting of an elephant with glasses"
   __a       =  [5, 7]

   __a       =  pipe(
       prompt=__SCREAMING_SNAKE_CASE     ,							token_indices=__SCREAMING_SNAKE_CASE     ,							guidance_scale=7.5     ,							generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							max_iter_to_alter=5     ,							output_type="numpy"     ,							).images[0]

   __a       =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"       )
   assert np.abs((expected_image - image).max()       ) < 5E-1



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		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

 # compute metrics
 __a       =  wer.compute(references=result["target"]							,				predictions=result["prediction"]					)
 __a       =  cer.compute(references=result["target"]							,				predictions=result["prediction"]					)

 # print & log results
 __a       =  f"""WER: {wer_result}\nCER: {cer_result}"""
 print(_A					)

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

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

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

   result.map(_A							,				with_indices=_A					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 __a       =  re.sub(_A							,				""							,				text.lower()					)

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

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  load_dataset(args.dataset							,				args.config							,				split=args.split							,				use_auth_token=_A					)

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

 # load processor
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

 # load eval pipeline
 if args.device is None:
  __a       =  0 if torch.cuda.is_available() else -1
 __a       =  pipeline("automatic-speech-recognition"							,				model=args.model_id							,				device=args.device					)

 # map function to decode audio
 def map_to_pred(_A					):
  __a       =  asr(
      batch["audio"]["array"]							,				chunk_length_s=args.chunk_length_s							,				stride_length_s=args.stride_length_s					)

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Any							     =		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.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


