
import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase      =random.Random()

if is_torch_available():
    import torch


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=1.0 ,		UpperCamelCase__=None ,		UpperCamelCase__=None		):
      if rng is None:
            UpperCamelCase__    :   Union[str, Any]					=				global_rng

      UpperCamelCase__    :   str					=				[]
      for batch_idx in range(shape[0]		):
            values.append([]		)
            for _ in range(shape[1]		):
                  values[-1].append(rng.random() * scale		)

      return values




class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=4_0_0						,						__SCREAMING_SNAKE_CASE=2_0_0_0						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=0.0						,						__SCREAMING_SNAKE_CASE=1_6_0_0_0						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						)  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				parent
           UpperCamelCase__    :   Tuple					=				batch_size
           UpperCamelCase__    :   int					=				min_seq_length
           UpperCamelCase__    :   Tuple					=				max_seq_length
           UpperCamelCase__    :   Optional[Any]					=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           UpperCamelCase__    :   Optional[Any]					=				feature_size
           UpperCamelCase__    :   Tuple					=				padding_value
           UpperCamelCase__    :   Optional[Any]					=				sampling_rate
           UpperCamelCase__    :   Optional[Any]					=				return_attention_mask
           UpperCamelCase__    :   List[Any]					=				do_normalize






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
               "do_normalize": self.do_normalize,
           }






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=False  )  ->   List[Any]:




           """simple docstring"""







           def _flatten(__SCREAMING_SNAKE_CASE  ):
                 return list(itertools.chain(*__SCREAMING_SNAKE_CASE  )  )

           if equal_length:
                 UpperCamelCase__    :   Union[str, Any]					=				floats_list((self.batch_size, self.max_seq_length)  )
           else:
                 # make sure that inputs increase in size
                 UpperCamelCase__    :   List[Any]					=				[
                     _flatten(floats_list((x, self.feature_size)  )  )
                     for x in range(self.min_seq_length						,						self.max_seq_length						,						self.seq_length_diff  )
                 ]

           if numpify:
                 UpperCamelCase__    :   List[Any]					=				[np.asarray(__SCREAMING_SNAKE_CASE  ) for x in speech_inputs]

           return speech_inputs




@require_torch
@require_torchaudio
class 				_lowerCamelCase (      UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ASTFeatureExtractor






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   str					=				ASTFeatureExtractionTester(self  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
           # create three inputs of length 800, 1000, and 1200
           UpperCamelCase__    :   List[str]					=				[floats_list((1, x)  )[0] for x in range(8_0_0						,						1_4_0_0						,						2_0_0  )]
           UpperCamelCase__    :   Any					=				[np.asarray(__SCREAMING_SNAKE_CASE  ) for speech_input in speech_inputs]

           # Test not batched input
           UpperCamelCase__    :   int					=				feat_extract(speech_inputs[0]						,						return_tensors='''np'''  ).input_values
           UpperCamelCase__    :   List[Any]					=				feat_extract(np_speech_inputs[0]						,						return_tensors='''np'''  ).input_values
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						atol=1e-3  )  )

           # Test batched
           UpperCamelCase__    :   Union[str, Any]					=				feat_extract(__SCREAMING_SNAKE_CASE						,						padding=__SCREAMING_SNAKE_CASE						,						return_tensors='''np'''  ).input_values
           UpperCamelCase__    :   Optional[Any]					=				feat_extract(__SCREAMING_SNAKE_CASE						,						padding=__SCREAMING_SNAKE_CASE						,						return_tensors='''np'''  ).input_values
           for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						atol=1e-3  )  )

           # Test 2-D numpy arrays are batched.
           UpperCamelCase__    :   Optional[Any]					=				[floats_list((1, x)  )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
           UpperCamelCase__    :   Dict					=				np.asarray(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				feat_extract(__SCREAMING_SNAKE_CASE						,						return_tensors='''np'''  ).input_values
           UpperCamelCase__    :   List[Any]					=				feat_extract(__SCREAMING_SNAKE_CASE						,						return_tensors='''np'''  ).input_values
           for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						atol=1e-3  )  )






     @require_torch
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           import torch

           UpperCamelCase__    :   List[str]					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
           UpperCamelCase__    :   int					=				np.random.rand(1_0_0  ).astype(np.floataa  )
           UpperCamelCase__    :   str					=				np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                 UpperCamelCase__    :   Optional[int]					=				feature_extractor.pad([{'''input_values''': inputs}]						,						return_tensors='''np'''  )
                 self.assertTrue(np_processed.input_values.dtype == np.floataa  )
                 UpperCamelCase__    :   Dict					=				feature_extractor.pad([{'''input_values''': inputs}]						,						return_tensors='''pt'''  )
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           from datasets import load_dataset

           UpperCamelCase__    :   int					=				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''						,						'''clean'''						,						split='''validation'''  )
           # automatic decoding with librispeech
           UpperCamelCase__    :   str					=				ds.sort('''id'''  ).select(range(__SCREAMING_SNAKE_CASE  )  )[:num_samples]['''audio''']

           return [x["array"] for x in speech_samples]






     @require_torch
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				torch.tensor(
               [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
                -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
                -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
                -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]  )
           # fmt: on

           UpperCamelCase__    :   Dict					=				self._load_datasamples(1  )
           UpperCamelCase__    :   Union[str, Any]					=				ASTFeatureExtractor()
           UpperCamelCase__    :   Any					=				feature_extractor(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).input_values
           self.assertEquals(input_values.shape						,						(1, 1_0_2_4, 1_2_8)  )
           self.assertTrue(torch.allclose(input_values[0, 0, :3_0]						,						__SCREAMING_SNAKE_CASE						,						atol=1e-4  )  )




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCamelCase      =logging.get_logger(__name__)  # pylint: disable=invalid-name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(UpperCamelCase__		):
                  return ext

      raise Exception(
          f'''Unable to determine file format from file extension {path}. '''
          f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				PipelineDataFormat.from_str(
          format=UpperCamelCase__ ,		output_path=args.output ,		input_path=args.input ,		column=args.column if args.column else nlp.default_input_names ,		overwrite=args.overwrite ,		)
      return RunCommand(UpperCamelCase__ ,		UpperCamelCase__		)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				parser.add_parser('''run'''						,						help='''Run a pipeline through the CLI'''  )
           run_parser.add_argument('''--task'''						,						choices=get_supported_tasks()						,						help='''Task to run'''  )
           run_parser.add_argument('''--input'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file to use for inference'''  )
           run_parser.add_argument('''--output'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file that will be used post to write results.'''  )
           run_parser.add_argument('''--model'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model to instantiate.'''  )
           run_parser.add_argument('''--config'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model\'s config to instantiate.'''  )
           run_parser.add_argument(
               '''--tokenizer'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the tokenizer to use. (default: same as the model name)'''  )
           run_parser.add_argument(
               '''--column'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''						,						)
           run_parser.add_argument(
               '''--format'''						,						type=__SCREAMING_SNAKE_CASE						,						default='''infer'''						,						choices=PipelineDataFormat.SUPPORTED_FORMATS						,						help='''Input format to read from'''						,						)
           run_parser.add_argument(
               '''--device'''						,						type=__SCREAMING_SNAKE_CASE						,						default=-1						,						help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''						,						)
           run_parser.add_argument('''--overwrite'''						,						action='''store_true'''						,						help='''Allow overwriting the output file.'''  )
           run_parser.set_defaults(func=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				nlp(**__SCREAMING_SNAKE_CASE  ) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE  )
                 if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       outputs.append(__SCREAMING_SNAKE_CASE  )
                 else:
                       outputs += output

        # Saving data
           if self._nlp.binary_output:
                 UpperCamelCase__    :   Union[str, Any]					=				self._reader.save_binary(__SCREAMING_SNAKE_CASE  )
                 logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}'''  )
           else:
                 self._reader.save(__SCREAMING_SNAKE_CASE  )


