'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor, CLIPProcessor






@require_vision
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tempfile.mkdtemp()

         # fmt: off
         SCREAMING_SNAKE_CASE					:			int    	=       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         SCREAMING_SNAKE_CASE					:			Dict    	=       dict(zip(_lowerCAmelCase    ,							range(len(_lowerCAmelCase ) ) ) )
         SCREAMING_SNAKE_CASE					:			str    	=       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
         SCREAMING_SNAKE_CASE					:			Any    	=       {'unk_token': '<unk>'}

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['vocab_file'] )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['merges_file'] )
         with open(self.vocab_file    ,							'w'    ,							encoding='utf-8' ) as fp:
              fp.write(json.dumps(_lowerCAmelCase ) + '\n' )
         with open(self.merges_file    ,							'w'    ,							encoding='utf-8' ) as fp:
              fp.write('\n'.join(_lowerCAmelCase ) )

         SCREAMING_SNAKE_CASE					:			int    	=       {
             'do_resize': True,
             'size': 20,
             'do_center_crop': True,
             'crop_size': 18,
             'do_normalize': True,
             'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
             'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
         }
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       os.path.join(self.tmpdirname    ,							_lowerCAmelCase )
         with open(self.image_processor_file    ,							'w'    ,							encoding='utf-8' ) as fp:
              json.dump(_lowerCAmelCase    ,							_lowerCAmelCase )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> List[str]:
         return CLIPTokenizer.from_pretrained(self.tmpdirname    ,							**_lowerCAmelCase )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Any:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,							**_lowerCAmelCase )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Optional[Any]:
         return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,							**_lowerCAmelCase )






    def       _UpperCamelCase    (       self )							-> Any:
         shutil.rmtree(self.tmpdirname )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       [np.random.randint(255    ,							size=(3, 30, 400)    ,							dtype=np.uinta )]

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [Image.fromarray(np.moveaxis(_lowerCAmelCase    ,							0    ,							-1 ) ) for x in image_inputs]

         return image_inputs






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_tokenizer()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_rust_tokenizer()
         SCREAMING_SNAKE_CASE					:			int    	=       self.get_image_processor()

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       CLIPProcessor(tokenizer=_lowerCAmelCase    ,							image_processor=_lowerCAmelCase )
         processor_slow.save_pretrained(self.tmpdirname )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       CLIPProcessor.from_pretrained(self.tmpdirname    ,							use_fast=_lowerCAmelCase )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       CLIPProcessor(tokenizer=_lowerCAmelCase    ,							image_processor=_lowerCAmelCase )
         processor_fast.save_pretrained(self.tmpdirname )
         SCREAMING_SNAKE_CASE					:			Any    	=       CLIPProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor_slow.tokenizer.get_vocab()    ,							tokenizer_slow.get_vocab() )
         self.assertEqual(processor_fast.tokenizer.get_vocab()    ,							tokenizer_fast.get_vocab() )
         self.assertEqual(tokenizer_slow.get_vocab()    ,							tokenizer_fast.get_vocab() )
         self.assertIsInstance(processor_slow.tokenizer    ,							_lowerCAmelCase )
         self.assertIsInstance(processor_fast.tokenizer    ,							_lowerCAmelCase )

         self.assertEqual(processor_slow.image_processor.to_json_string()    ,							image_processor.to_json_string() )
         self.assertEqual(processor_fast.image_processor.to_json_string()    ,							image_processor.to_json_string() )
         self.assertIsInstance(processor_slow.image_processor    ,							_lowerCAmelCase )
         self.assertIsInstance(processor_fast.image_processor    ,							_lowerCAmelCase )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       CLIPProcessor(tokenizer=self.get_tokenizer()    ,							image_processor=self.get_image_processor() )
         processor.save_pretrained(self.tmpdirname )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_tokenizer(bos_token='(BOS)'    ,							eos_token='(EOS)' )
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_image_processor(do_normalize=_lowerCAmelCase    ,							padding_value=1.0 )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       CLIPProcessor.from_pretrained(
             self.tmpdirname    ,							bos_token='(BOS)'    ,							eos_token='(EOS)'    ,							do_normalize=_lowerCAmelCase    ,							padding_value=1.0 )

         self.assertEqual(processor.tokenizer.get_vocab()    ,							tokenizer_add_kwargs.get_vocab() )
         self.assertIsInstance(processor.tokenizer    ,							_lowerCAmelCase )

         self.assertEqual(processor.image_processor.to_json_string()    ,							image_processor_add_kwargs.to_json_string() )
         self.assertIsInstance(processor.image_processor    ,							_lowerCAmelCase )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Any    	=       CLIPProcessor(tokenizer=_lowerCAmelCase    ,							image_processor=_lowerCAmelCase )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image_processor(_lowerCAmelCase    ,							return_tensors='np' )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       processor(images=_lowerCAmelCase    ,							return_tensors='np' )

         for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()    ,							input_processor[key].sum()    ,							delta=1E-2 )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Any    	=       CLIPProcessor(tokenizer=_lowerCAmelCase    ,							image_processor=_lowerCAmelCase )

         SCREAMING_SNAKE_CASE					:			Dict    	=       'lower newer'

         SCREAMING_SNAKE_CASE					:			str    	=       processor(text=_lowerCAmelCase )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tokenizer(_lowerCAmelCase )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,							encoded_processor[key] )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Any    	=       CLIPProcessor(tokenizer=_lowerCAmelCase    ,							image_processor=_lowerCAmelCase )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'lower newer'
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE					:			str    	=       processor(text=_lowerCAmelCase    ,							images=_lowerCAmelCase )

         self.assertListEqual(list(inputs.keys() )    ,							['input_ids', 'attention_mask', 'pixel_values'] )

         # test if it raises when no input is passed
         with pytest.raises(_lowerCAmelCase ):
              processor()






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       CLIPProcessor(tokenizer=_lowerCAmelCase    ,							image_processor=_lowerCAmelCase )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         SCREAMING_SNAKE_CASE					:			Tuple    	=       processor.batch_decode(_lowerCAmelCase )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer.batch_decode(_lowerCAmelCase )

         self.assertListEqual(_lowerCAmelCase    ,							_lowerCAmelCase )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       CLIPProcessor(tokenizer=_lowerCAmelCase    ,							image_processor=_lowerCAmelCase )

         SCREAMING_SNAKE_CASE					:			Dict    	=       'lower newer'
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE					:			int    	=       processor(text=_lowerCAmelCase    ,							images=_lowerCAmelCase )

         self.assertListEqual(list(inputs.keys() )    ,							processor.model_input_names )




'''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     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       args.log_outputs
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       '_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     SCREAMING_SNAKE_CASE					:			int    	=       load_metric('wer'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_metric('cer'		)

     # compute metrics
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       wer.compute(references=result['target']       ,      predictions=result['prediction']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cer.compute(references=result['target']       ,      predictions=result['prediction']		)

     # print & log results
     SCREAMING_SNAKE_CASE					:			int    	=       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:
          SCREAMING_SNAKE_CASE					:			Any    	=       f"""log_{dataset_id}_predictions.txt"""
          SCREAMING_SNAKE_CASE					:			int    	=       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     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





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

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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!
     SCREAMING_SNAKE_CASE					:			str    	=       ['\n\n', '\n', '   ', '  ']

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       ' '.join(text.split(a_		)		)

     return text


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       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
     SCREAMING_SNAKE_CASE					:			str    	=       AutoFeatureExtractor.from_pretrained(args.model_id		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       dataset.cast_column('audio'       ,      Audio(sampling_rate=a_		)		)

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

     # map function to decode audio
     def map_to_pred(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       asr(
              batch['audio']['array']       ,      chunk_length_s=args.chunk_length_s       ,      stride_length_s=args.stride_length_s		)

          SCREAMING_SNAKE_CASE					:			str    	=       prediction['text']
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset.map(a_       ,      remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[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.""",
 )
 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 main(args)



