
'''simple docstring'''






from ...processing_utils import ProcessorMixin




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''SpeechT5FeatureExtractor'''
					_lowerCAmelCase			     =	'''SpeechT5Tokenizer'''
					def __init__(     self,    A,    A      ):

									"""simple docstring"""


									super().__init__(A,    A      )
					def __call__(     self,    *A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= kwargs.pop('audio',    A      )
									lowerCamelCase					:			int									= kwargs.pop('text',    A      )
									lowerCamelCase					:			List[Any]									= kwargs.pop('text_target',    A      )
									lowerCamelCase					:			Dict									= kwargs.pop('audio_target',    A      )
									lowerCamelCase					:			int									= 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:
													lowerCamelCase					:			Any									= self.feature_extractor(A,    *A,    sampling_rate=A,    **A      )
									elif text is not None:
													lowerCamelCase					:			int									= self.tokenizer(A,    **A      )
									else:
													lowerCamelCase					:			Optional[int]									= None

									if audio_target is not None:
													lowerCamelCase					:			List[str]									= self.feature_extractor(audio_target=A,    *A,    sampling_rate=A,    **A      )
													lowerCamelCase					:			Any									= targets['input_values']
									elif text_target is not None:
													lowerCamelCase					:			Optional[int]									= self.tokenizer(A,    **A      )
													lowerCamelCase					:			Tuple									= targets['input_ids']
									else:
													lowerCamelCase					:			List[Any]									= None

									if inputs is None:
													return targets

									if targets is not None:
													lowerCamelCase					:			List[str]									= labels

													lowerCamelCase					:			Dict									= targets.get('attention_mask'      )
													if decoder_attention_mask is not None:
																	lowerCamelCase					:			Any									= decoder_attention_mask

									return inputs
					def     UpperCAmelCase_							(     self,    *A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= kwargs.pop('input_values',    A      )
									lowerCamelCase					:			Tuple									= kwargs.pop('input_ids',    A      )
									lowerCamelCase					:			Tuple									= 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:
													lowerCamelCase					:			Tuple									= self.feature_extractor.pad(A,    *A,    **A      )
									elif input_ids is not None:
													lowerCamelCase					:			Union[str, Any]									= self.tokenizer.pad(A,    **A      )
									else:
													lowerCamelCase					:			List[Any]									= None

									if labels is not None:
													if "input_ids" in labels or (isinstance(A,    A      ) and "input_ids" in labels[0]):
																	lowerCamelCase					:			str									= self.tokenizer.pad(A,    **A      )
																	lowerCamelCase					:			Tuple									= targets['input_ids']
													else:
																	lowerCamelCase					:			Optional[int]									= self.feature_extractor.feature_size
																	lowerCamelCase					:			List[Any]									= self.feature_extractor.num_mel_bins
																	lowerCamelCase					:			Tuple									= self.feature_extractor.pad(A,    *A,    **A      )
																	lowerCamelCase					:			Union[str, Any]									= feature_size_hack
																	lowerCamelCase					:			int									= targets['input_values']
									else:
													lowerCamelCase					:			str									= None

									if inputs is None:
													return targets

									if targets is not None:
													lowerCamelCase					:			Dict									= labels

													lowerCamelCase					:			Tuple									= targets.get('attention_mask'      )
													if decoder_attention_mask is not None:
																	lowerCamelCase					:			List[str]									= decoder_attention_mask

									return inputs
					def     UpperCAmelCase_							(     self,    *A,    **A      ):

									"""simple docstring"""


									return self.tokenizer.batch_decode(*A,    **A      )




					def     UpperCAmelCase_							(     self,    *A,    **A      ):

									"""simple docstring"""


									return self.tokenizer.decode(*A,    **A      )


'''simple docstring'''






import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
					import torch

					from transformers import AutoModelForCausalLM




@require_torch
class     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Optional[int]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= -1

									lowerCamelCase					:			Optional[Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			List[Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= tokenizer.decode(greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Optional[int]									= TextStreamer(A      )
													model.generate(A,    max_new_tokens=10,    do_sample=A,    streamer=A      )
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									lowerCamelCase					:			int									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			List[Any]									= -1

									lowerCamelCase					:			Optional[int]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			Any									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			Optional[int]									= tokenizer.decode(greedy_ids[0]      )

									lowerCamelCase					:			Union[str, Any]									= TextIteratorStreamer(A      )
									lowerCamelCase					:			Tuple									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			List[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()
									lowerCamelCase					:			List[Any]									= ''
									for new_text in streamer:
													streamer_text += new_text

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			int									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Optional[Any]									= -1

									lowerCamelCase					:			Union[str, Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= greedy_ids[:, input_ids.shape[1] :]
									lowerCamelCase					:			Dict									= tokenizer.decode(new_greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Tuple									= TextStreamer(A,    skip_prompt=A      )
													model.generate(A,    max_new_tokens=10,    do_sample=A,    streamer=A      )
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									lowerCamelCase					:			Optional[int]									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= AutoTokenizer.from_pretrained('distilgpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('distilgpt2'      ).to(A      )
									lowerCamelCase					:			Dict									= -1

									lowerCamelCase					:			Any									= torch.ones((1, 5),    device=A      ).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
													lowerCamelCase					:			int									= TextStreamer(A,    skip_special_tokens=A      )
													model.generate(A,    max_new_tokens=1,    do_sample=A,    streamer=A      )

									# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
									# re-tokenized, must only contain one token
									lowerCamelCase					:			List[str]									= cs.out[:-1]  # Remove the final "\n"
									lowerCamelCase					:			Optional[Any]									= tokenizer(A,    return_tensors='pt'      )
									self.assertEqual(streamer_text_tokenized.input_ids.shape,    (1, 1)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			List[str]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Any									= -1

									lowerCamelCase					:			int									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			int									= TextIteratorStreamer(A,    timeout=0.001      )
									lowerCamelCase					:			Any									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			Optional[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()

									# The streamer will timeout after 0.001 seconds, so an exception will be raised
									with self.assertRaises(A      ):
													lowerCamelCase					:			Dict									= ''
													for new_text in streamer:
																	streamer_text += new_text
