
from ...processing_utils import ProcessorMixin


class  __magic_name__						(     _a):
	_UpperCAmelCase	:			str		   =     'SpeechT5FeatureExtractor'
	_UpperCAmelCase	:			Any		   =     'SpeechT5Tokenizer'





	def __init__(		self	: Optional[Any]    ,__SCREAMING_SNAKE_CASE	: Any    ,__SCREAMING_SNAKE_CASE	: List[Any]       ):
						super().__init__(A_    ,A_       )





	def __call__(		self	: Any    ,*__SCREAMING_SNAKE_CASE	: Optional[Any]    ,**__SCREAMING_SNAKE_CASE	: List[str]       ):
						UpperCAmelCase				   =  kwargs.pop("audio"    ,A_       )
						UpperCAmelCase				   =  kwargs.pop("text"    ,A_       )
						UpperCAmelCase				   =  kwargs.pop("text_target"    ,A_       )
						UpperCAmelCase				   =  kwargs.pop("audio_target"    ,A_       )
						UpperCAmelCase				   =  kwargs.pop("sampling_rate"    ,A_       )

						if audio is not None and text is not None:
											raise ValueError(
											    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"       )
						if audio_target is not None and text_target is not None:
											raise ValueError(
											    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"       )
						if audio is None and audio_target is None and text is None and text_target is None:
											raise ValueError(
											    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."       )

						if audio is not None:
											UpperCAmelCase				   =  self.feature_extractor(A_    ,*A_    ,sampling_rate=A_    ,**A_       )
						elif text is not None:
											UpperCAmelCase				   =  self.tokenizer(A_    ,**A_       )
						else:
											UpperCAmelCase				   =  None

						if audio_target is not None:
											UpperCAmelCase				   =  self.feature_extractor(audio_target=A_    ,*A_    ,sampling_rate=A_    ,**A_       )
											UpperCAmelCase				   =  targets["input_values"]
						elif text_target is not None:
											UpperCAmelCase				   =  self.tokenizer(A_    ,**A_       )
											UpperCAmelCase				   =  targets["input_ids"]
						else:
											UpperCAmelCase				   =  None

						if inputs is None:
											return targets

						if targets is not None:
											UpperCAmelCase				   =  labels

											UpperCAmelCase				   =  targets.get("attention_mask"       )
											if decoder_attention_mask is not None:
																UpperCAmelCase				   =  decoder_attention_mask

						return inputs





	def 	_UpperCAmelCase						(		self	: List[Any]    ,*__SCREAMING_SNAKE_CASE	: str    ,**__SCREAMING_SNAKE_CASE	: List[Any]       ):
						UpperCAmelCase				   =  kwargs.pop("input_values"    ,A_       )
						UpperCAmelCase				   =  kwargs.pop("input_ids"    ,A_       )
						UpperCAmelCase				   =  kwargs.pop("labels"    ,A_       )

						if input_values is not None and input_ids is not None:
											raise ValueError("Cannot process both `input_values` and `input_ids` inputs."       )
						if input_values is None and input_ids is None and labels is None:
											raise ValueError(
											    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."       )

						if input_values is not None:
											UpperCAmelCase				   =  self.feature_extractor.pad(A_    ,*A_    ,**A_       )
						elif input_ids is not None:
											UpperCAmelCase				   =  self.tokenizer.pad(A_    ,**A_       )
						else:
											UpperCAmelCase				   =  None

						if labels is not None:
											if "input_ids" in labels or (isinstance(A_    ,A_       ) and "input_ids" in labels[0]):
																UpperCAmelCase				   =  self.tokenizer.pad(A_    ,**A_       )
																UpperCAmelCase				   =  targets["input_ids"]
											else:
																UpperCAmelCase				   =  self.feature_extractor.feature_size
																UpperCAmelCase				   =  self.feature_extractor.num_mel_bins
																UpperCAmelCase				   =  self.feature_extractor.pad(A_    ,*A_    ,**A_       )
																UpperCAmelCase				   =  feature_size_hack
																UpperCAmelCase				   =  targets["input_values"]
						else:
											UpperCAmelCase				   =  None

						if inputs is None:
											return targets

						if targets is not None:
											UpperCAmelCase				   =  labels

											UpperCAmelCase				   =  targets.get("attention_mask"       )
											if decoder_attention_mask is not None:
																UpperCAmelCase				   =  decoder_attention_mask

						return inputs





	def 	_UpperCAmelCase						(		self	: List[str]    ,*__SCREAMING_SNAKE_CASE	: Any    ,**__SCREAMING_SNAKE_CASE	: List[str]       ):
						return self.tokenizer.batch_decode(*A_    ,**A_       )





	def 	_UpperCAmelCase						(		self	: str    ,*__SCREAMING_SNAKE_CASE	: Optional[int]    ,**__SCREAMING_SNAKE_CASE	: int       ):
						return self.tokenizer.decode(*A_    ,**A_       )









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			(    A=""     ):




   '''simple docstring'''

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

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






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=AgentAudio(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

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

    del agent_type

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

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






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=get_new_path(suffix=".wav"		)
    sf.write(A_,		A_,		1_6000		)

    UpperCAmelCase__						=AgentAudio(A_		)

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

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






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=torch.randint(0,		256,		(64, 64, 3)		)
    UpperCAmelCase__						=AgentImage(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

    # Ensure that the tensor and the agent_type's tensor are the same
    self.assertTrue(torch.allclose(A_,		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(A_		)		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    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(A_		)		)






 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    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(A_		)		)

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						="Hey!"
    UpperCAmelCase__						=AgentText(A_		)

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



