


'''simple docstring'''



import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
	import torch

if is_vision_available():
	import PIL


__lowercase	:						Optional[Any]       =	logging.get_logger(__name__)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   np.ndarray						,      _SCREAMING_SNAKE_CASE      :   Union[int, Iterable[int]]						,      _SCREAMING_SNAKE_CASE      :   bool						,      _SCREAMING_SNAKE_CASE      :   int   ):
					def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   str=0						,      _SCREAMING_SNAKE_CASE      :   Optional[int]=None   ):
										__a			:       Tuple					    =  round(val / multiple   ) * multiple

										if max_val is not None and x > max_val:
															__a			:       Optional[int]					    =  math.floor(val / multiple   ) * multiple

										if x < min_val:
															__a			:       int					    =  math.ceil(val / multiple   ) * multiple

										return x

					__a			:       str					    =  (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) else output_size

					__a    ,				__a			:       List[Any]					    =  get_image_size(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       List[Any]					    =  output_size

					# determine new height and width
					__a			:       Optional[Any]					    =  output_height / input_height
					__a			:       str					    =  output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width   ) < abs(1 - scale_height   ):
															# fit width
															__a			:       Optional[Any]					    =  scale_width
										else:
															# fit height
															__a			:       List[Any]					    =  scale_height

					__a			:       Any					    =  constraint_to_multiple_of(scale_height * input_height						,      multiple=_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  constraint_to_multiple_of(scale_width * input_width						,      multiple=_SCREAMING_SNAKE_CASE   )

					return (new_height, new_width)





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    ["pixel_values"]




	def __init__(   self						,				__a = True						,				__a = None						,				__a = PILImageResampling.BILINEAR						,				__a = False						,				__a = 1						,				__a = True						,				__a = 1 / 255						,				__a = True						,				__a = None						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						super().__init__(**__a							)
						__a			:       Optional[Any]					    =  size if size is not None else {'height': 384, 'width': 384}
						__a			:       Optional[int]					    =  get_size_dict(__a							)
						__a			:       str					    =  do_resize
						__a			:       Optional[Any]					    =  size
						__a			:       Optional[int]					    =  keep_aspect_ratio
						__a			:       int					    =  ensure_multiple_of
						__a			:       List[str]					    =  resample
						__a			:       str					    =  do_rescale
						__a			:       int					    =  rescale_factor
						__a			:       Optional[int]					    =  do_normalize
						__a			:       Dict					    =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__a			:       Dict					    =  image_std if image_std is not None else IMAGENET_STANDARD_STD




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = False						,				__a = 1						,				__a = PILImageResampling.BICUBIC						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						__a			:       int					    =  get_size_dict(__a							)
						if "height" not in size or "width" not in size:
											raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""							)
						__a			:       str					    =  get_resize_output_image_size(
						    __a						,				output_size=(size['height'], size['width'])						,				keep_aspect_ratio=__a						,				multiple=__a						,				)
						return resize(__a						,				size=__a						,				resample=__a						,				data_format=__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						return rescale(__a						,				scale=__a						,				data_format=__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						return normalize(__a						,				mean=__a						,				std=__a						,				data_format=__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = ChannelDimension.FIRST						,				**__a						,				):

						'''simple docstring'''





						__a			:       int					    =  do_resize if do_resize is not None else self.do_resize
						__a			:       Union[str, Any]					    =  size if size is not None else self.size
						__a			:       List[str]					    =  get_size_dict(__a							)
						__a			:       Tuple					    =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__a			:       Tuple					    =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__a			:       int					    =  resample if resample is not None else self.resample
						__a			:       Dict					    =  do_rescale if do_rescale is not None else self.do_rescale
						__a			:       Tuple					    =  rescale_factor if rescale_factor is not None else self.rescale_factor
						__a			:       Dict					    =  do_normalize if do_normalize is not None else self.do_normalize
						__a			:       Union[str, Any]					    =  image_mean if image_mean is not None else self.image_mean
						__a			:       Optional[int]					    =  image_std if image_std is not None else self.image_std

						__a			:       Any					    =  make_list_of_images(__a							)

						if not valid_images(__a							):
											raise ValueError(
											    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
											    'torch.Tensor, tf.Tensor or jax.ndarray.'							)

						if do_resize and size is None or resample is None:
											raise ValueError('Size and resample must be specified if do_resize is True.'							)

						if do_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.'							)

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('Image mean and std must be specified if do_normalize is True.'							)

						# All transformations expect numpy arrays.
						__a			:       Optional[int]					    =  [to_numpy_array(__a							) for image in images]

						if do_resize:
											__a			:       Optional[int]					    =  [self.resize(image=__a						,				size=__a						,				resample=__a							) for image in images]

						if do_rescale:
											__a			:       List[str]					    =  [self.rescale(image=__a						,				scale=__a							) for image in images]

						if do_normalize:
											__a			:       Optional[Any]					    =  [self.normalize(image=__a						,				mean=__a						,				std=__a							) for image in images]

						__a			:       List[Any]					    =  [to_channel_dimension_format(__a						,				__a							) for image in images]

						__a			:       Dict					    =  {'pixel_values': images}
						return BatchFeature(data=__a						,				tensor_type=__a							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
											if len(__a							) != len(__a							):
																raise ValueError(
																    'Make sure that you pass in as many target sizes as the batch dimension of the logits'							)

											if is_torch_tensor(__a							):
																__a			:       Tuple					    =  target_sizes.numpy()

											__a			:       Optional[int]					    =  []

											for idx in range(len(__a							)							):
																__a			:       List[Any]					    =  torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0							)						,				size=target_sizes[idx]						,				mode='bilinear'						,				align_corners=__a							)
																__a			:       List[str]					    =  resized_logits[0].argmax(dim=0							)
																semantic_segmentation.append(__a							)
						else:
											__a			:       int					    =  logits.argmax(dim=1							)
											__a			:       Union[str, Any]					    =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

						return semantic_segmentation





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 			UpperCamelCase					(    __lowerCamelCase     : Dataset				,					__lowerCamelCase     : Dict[str, str]    ):
       snake_case       :   int							=      args.log_outputs
       snake_case       :   Dict							=      "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

       # load metric
       snake_case       :   List[str]							=      load_metric("wer"    )
       snake_case       :   Tuple							=      load_metric("cer"    )

       # compute metrics
       snake_case       :   List[Any]							=      wer.compute(references=result["target"]				,					predictions=result["prediction"]    )
       snake_case       :   int							=      cer.compute(references=result["target"]				,					predictions=result["prediction"]    )

       # print & log results
       snake_case       :   int							=      f"""WER: {wer_result}\nCER: {cer_result}"""
       print(__lowerCamelCase    )

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

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

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

                     result.map(__lowerCamelCase				,					with_indices=__lowerCamelCase    )


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

       snake_case       :   List[Any]							=      re.sub(__lowerCamelCase				,					""				,					text.lower()    )

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

       for t in token_sequences_to_ignore:
              snake_case       :   Dict							=      " ".join(text.split(__lowerCamelCase    )    )

       return text


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       # load dataset
       snake_case       :   str							=      load_dataset(args.dataset				,					args.config				,					split=args.split				,					use_auth_token=__lowerCamelCase    )

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

       # load processor
       snake_case       :   List[Any]							=      AutoFeatureExtractor.from_pretrained(args.model_id    )
       snake_case       :   Union[str, Any]							=      feature_extractor.sampling_rate

       # resample audio
       snake_case       :   Union[str, Any]							=      dataset.cast_column("audio"				,					Audio(sampling_rate=__lowerCamelCase    )    )

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

       # map function to decode audio
       def map_to_pred(__lowerCamelCase     : int    ):
              snake_case       :   Dict							=      asr(
                  batch["audio"]["array"]				,					chunk_length_s=args.chunk_length_s				,					stride_length_s=args.stride_length_s    )

              snake_case       :   str							=      prediction["text"]
              snake_case       :   Tuple							=      normalize_text(batch["sentence"]    )
              return batch

       # run inference on all examples
       snake_case       :   Dict							=      dataset.map(__lowerCamelCase				,					remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
   __lowerCamelCase							=       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.""",
   )
   __lowerCamelCase							=       parser.parse_args()

   main(args)





