




"""simple docstring"""





from jiwer import compute_measures

import datasets


A   =				"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

A   =				"""\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""

A   =				"""
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> wer = datasets.load_metric(\"wer\")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,    _KWARGS_DESCRIPTION   )
class      a__				(	datasets.Metric   ):


			def       a_     (      self      :    Any):


						"""simple docstring"""




						return datasets.MetricInfo(
						    description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"      ,  id="sequence"),
						            "references": datasets.Value("string"      ,  id="sequence"),
						        })      ,  codebase_urls=["https://github.com/jitsi/jiwer/"]      ,  reference_urls=[
						        "https://en.wikipedia.org/wiki/Word_error_rate",
						    ]      ,  )


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    List[Any]=None      ,  UpperCamelCase_      :    Optional[Any]=None      ,  UpperCamelCase_      :    str=False):


						"""simple docstring"""




						if concatenate_texts:
									return compute_measures(UpperCamelCase_      ,  UpperCamelCase_)["wer"]
						else:
									__UpperCAmelCase	: List[Any]		=					0
									__UpperCAmelCase	: List[str]		=					0
									for prediction, reference in zip(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: str		=					compute_measures(UpperCamelCase_      ,  UpperCamelCase_)
												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
												total += measures["substitutions"] + measures["deletions"] + measures["hits"]
									return incorrect / total








"""simple docstring"""





import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	LDMTextToImagePipeline
			lowercase_    =	TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "negative_prompt_embeds",
			    "cross_attention_kwargs",
			    "prompt_embeds",
			}
			lowercase_    =	PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			lowercase_    =	TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase_    =	False


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						torch.manual_seed(0)
						__UpperCAmelCase	: str		=					UNetaDConditionModel(
						    block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=4      ,  out_channels=4      ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,  cross_attention_dim=32      ,  )
						__UpperCAmelCase	: Dict		=					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)
						__UpperCAmelCase	: Optional[int]		=					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      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: List[str]		=					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=1000      ,  )
						__UpperCAmelCase	: List[str]		=					CLIPTextModel(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__UpperCAmelCase	: Optional[int]		=					{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vqvae": vae,
						    "bert": text_encoder,
						    "tokenizer": tokenizer,
						}
						return components


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Tuple=0):


						"""simple docstring"""




						if str(UpperCamelCase_).startswith("mps"):
									__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						else:
									__UpperCAmelCase	: Tuple		=					torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					"cpu"  # ensure determinism for the device-dependent torch.Generator

						__UpperCAmelCase	: Union[str, Any]		=					self.get_dummy_components()
						__UpperCAmelCase	: Union[str, Any]		=					LDMTextToImagePipeline(**UpperCamelCase_)
						pipe.to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: int		=					self.get_dummy_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: List[Any]		=					image[0, -3:, -3:, -1]

						assert image.shape == (1, 16, 16, 3)
						__UpperCAmelCase	: List[str]		=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3






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


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Dict=0):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Optional[Any]		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: str		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: Dict		=					image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 256, 256, 3)
						__UpperCAmelCase	: List[Any]		=					np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
						__UpperCAmelCase	: List[Any]		=					np.abs(expected_slice - image_slice).max()
						assert max_diff < 1e-3






@nightly
@require_torch_gpu
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




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


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Optional[int]=0):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: Optional[Any]		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 50,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Union[str, Any]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					pipe(**UpperCamelCase_).images[0]

						__UpperCAmelCase	: int		=					load_numpy(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
						__UpperCAmelCase	: int		=					np.abs(expected_image - image).max()
						assert max_diff < 1e-3


