import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Tuple		="""MCTCTFeatureExtractor"""
     _SCREAMING_SNAKE_CASE     :     int		="""AutoTokenizer"""



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            super().__init__(lowerCAmelCase__				,      lowerCAmelCase__       )
            _A=	self.feature_extractor
            _A=	False



     def __call__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor(*lowerCAmelCase__				,      **lowerCAmelCase__       )

            if "raw_speech" in kwargs:
                   warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
                   _A=	kwargs.pop('raw_speech'       )
            else:
                   _A=	kwargs.pop('audio'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('sampling_rate'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('text'				,      lowerCAmelCase__       )
            if len(lowerCAmelCase__       ) > 0:
                   _A=	args[0]
                   _A=	args[1:]

            if audio is None and text is None:
                   raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

            if audio is not None:
                   _A=	self.feature_extractor(lowerCAmelCase__				,      *lowerCAmelCase__				,      sampling_rate=lowerCAmelCase__				,      **lowerCAmelCase__       )
            if text is not None:
                   _A=	self.tokenizer(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if text is None:
                   return inputs
            elif audio is None:
                   return encodings
            else:
                   _A=	encodings['input_ids']
                   return inputs



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.tokenizer.batch_decode(*lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor.pad(*lowerCAmelCase__				,      **lowerCAmelCase__       )

            _A=	kwargs.pop('input_features'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('labels'				,      lowerCAmelCase__       )
            if len(lowerCAmelCase__       ) > 0:
                   _A=	args[0]
                   _A=	args[1:]

            if input_features is not None:
                   _A=	self.feature_extractor.pad(lowerCAmelCase__				,      *lowerCAmelCase__				,      **lowerCAmelCase__       )
            if labels is not None:
                   _A=	self.tokenizer.pad(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if labels is None:
                   return input_features
            elif input_features is None:
                   return labels
            else:
                   _A=	labels['input_ids']
                   return input_features



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.tokenizer.decode(*lowerCAmelCase__				,      **lowerCAmelCase__       )





     @contextmanager
     def 			a__  (					self       ):
            warnings.warn(
                '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
                'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
                'your audio inputs, or in a separate call.'       )
            _A=	True
            _A=	self.tokenizer
            yield
            _A=	self.feature_extractor
            _A=	False
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

if is_vision_available():
      from PIL import Image

def 							UpperCamelCase				(    lowerCAmelCase_=""   )		->						str:




       '''simple docstring'''






       _A=	tempfile.mkdtemp()
       return os.path.join(lowerCAmelCase_       ,					str(uuid.uuida()   ) + suffix   )





@require_soundfile
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	AgentAudio(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )

            del agent_type

            # Ensure the path remains even after the object deletion
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )

            # Ensure that the file contains the same value as the original tensor
            _A,			_A=	sf.read(lowerCAmelCase__       )
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      torch.tensor(lowerCAmelCase__       )				,      atol=1E-4       )       )





     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	get_new_path(suffix='.wav'       )
            sf.write(lowerCAmelCase__				,      lowerCAmelCase__				,      16000       )

            _A=	AgentAudio(lowerCAmelCase__       )

            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )
            self.assertEqual(agent_type.to_string()				,      lowerCAmelCase__       )





@require_vision
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.randint(0				,      256				,      (64, 64, 3)       )
            _A=	AgentImage(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type._tensor				,      atol=1E-4       )       )

            self.assertIsInstance(agent_type.to_raw()				,      Image.Image       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertTrue(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertFalse(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	'Hey!'
            _A=	AgentText(lowerCAmelCase__       )

            self.assertEqual(lowerCAmelCase__				,      agent_type.to_string()       )
            self.assertEqual(lowerCAmelCase__				,      agent_type.to_raw()       )
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )