
"""simple docstring"""


from ...processing_utils import ProcessorMixin




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Tuple          =    'SpeechT5FeatureExtractor'
				a__:      List[str]          =    'SpeechT5Tokenizer'




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							super().__init__(UpperCAmelCase							,						UpperCAmelCase   )




				def __call__(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  kwargs.pop('''audio'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''text'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''text_target'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''audio_target'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''sampling_rate'''							,						UpperCAmelCase   )

							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_					=  self.feature_extractor(UpperCAmelCase							,						*UpperCAmelCase							,						sampling_rate=UpperCAmelCase							,						**UpperCAmelCase   )
							elif text is not None:
										lowerCamelCase_					=  self.tokenizer(UpperCAmelCase							,						**UpperCAmelCase   )
							else:
										lowerCamelCase_					=  None

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

							if inputs is None:
										return targets

							if targets is not None:
										lowerCamelCase_					=  labels

										lowerCamelCase_					=  targets.get('''attention_mask'''   )
										if decoder_attention_mask is not None:
													lowerCamelCase_					=  decoder_attention_mask

							return inputs




				def        UpperCAmelCase__						(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  kwargs.pop('''input_values'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''input_ids'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''labels'''							,						UpperCAmelCase   )

							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_					=  self.feature_extractor.pad(UpperCAmelCase							,						*UpperCAmelCase							,						**UpperCAmelCase   )
							elif input_ids is not None:
										lowerCamelCase_					=  self.tokenizer.pad(UpperCAmelCase							,						**UpperCAmelCase   )
							else:
										lowerCamelCase_					=  None

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

							if inputs is None:
										return targets

							if targets is not None:
										lowerCamelCase_					=  labels

										lowerCamelCase_					=  targets.get('''attention_mask'''   )
										if decoder_attention_mask is not None:
													lowerCamelCase_					=  decoder_attention_mask

							return inputs




				def        UpperCAmelCase__						(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							return self.tokenizer.batch_decode(*UpperCAmelCase							,						**UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							return self.tokenizer.decode(*UpperCAmelCase							,						**UpperCAmelCase   )





"""simple docstring"""


import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test




class  __lowerCamelCase  ( unittest.TestCase			):




				@parameterized.expand([(None,), ('''foo.json''',)]   )
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						bad_words_ids=[[1, 2, 3], [4, 5]]							,						)
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(UpperCAmelCase							,						config_name=UpperCAmelCase   )
										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase							,						config_name=UpperCAmelCase   )

							# Checks parameters that were specified
							self.assertEqual(loaded_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(loaded_config.temperature							,						0.7   )
							self.assertEqual(loaded_config.length_penalty							,						1.0   )
							self.assertEqual(loaded_config.bad_words_ids							,						[[1, 2, 3], [4, 5]]   )

							# Checks parameters that were not specified (defaults)
							self.assertEqual(loaded_config.top_k							,						50   )
							self.assertEqual(loaded_config.max_length							,						20   )
							self.assertEqual(loaded_config.max_time							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoConfig.from_pretrained('''gpt2'''   )
							lowerCamelCase_					=  GenerationConfig.from_model_config(UpperCAmelCase   )
							lowerCamelCase_					=  GenerationConfig()

							# The generation config has loaded a few non-default parameters from the model config
							self.assertNotEqual(UpperCAmelCase							,						UpperCAmelCase   )

							# One of those parameters is eos_token_id -- check if it matches
							self.assertNotEqual(generation_config_from_model.eos_token_id							,						default_generation_config.eos_token_id   )
							self.assertEqual(generation_config_from_model.eos_token_id							,						model_config.eos_token_id   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							lowerCamelCase_					=  {
							    '''max_new_tokens''': 1024,
							    '''foo''': '''bar''',
							}
							lowerCamelCase_					=  copy.deepcopy(UpperCAmelCase   )
							lowerCamelCase_					=  generation_config.update(**UpperCAmelCase   )

							# update_kwargs was not modified (no side effects)
							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )

							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(generation_config.max_new_tokens							,						1024   )

							# `.update()` returns a dictionary of unused kwargs
							self.assertEqual(UpperCAmelCase							,						{'''foo''': '''bar'''}   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							lowerCamelCase_					=  '''bar'''

							with tempfile.TemporaryDirectory('''test-generation-config'''   ) as tmp_dir:
										generation_config.save_pretrained(UpperCAmelCase   )

										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase   )
							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(new_config.foo							,						'''bar'''   )

							lowerCamelCase_					=  GenerationConfig.from_model_config(UpperCAmelCase   )
							assert not hasattr(UpperCAmelCase							,						'''foo'''   )  # no new kwargs should be initialized if from config





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							self.assertEqual(default_config.temperature							,						1.0   )
							self.assertEqual(default_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(default_config.num_beams							,						1   )

							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						bad_words_ids=[[1, 2, 3], [4, 5]]							,						)
							self.assertEqual(config.temperature							,						0.7   )
							self.assertEqual(config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(config.num_beams							,						1   )

							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(UpperCAmelCase   )
										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase							,						temperature=1.0   )

							self.assertEqual(loaded_config.temperature							,						1.0   )
							self.assertEqual(loaded_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(loaded_config.num_beams							,						1   )  # default value




@is_staging_test
class  __lowerCamelCase  ( unittest.TestCase			):




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							lowerCamelCase_					=  TOKEN
							HfFolder.save_token(UpperCAmelCase   )




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							try:
										delete_repo(token=cls._token							,						repo_id='''test-generation-config'''   )
							except HTTPError:
										pass

							try:
										delete_repo(token=cls._token							,						repo_id='''valid_org/test-generation-config-org'''   )
							except HTTPError:
										pass




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						)
							config.push_to_hub('''test-generation-config'''							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained(f"{USER}/test-generation-config"   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )

        # Reset repo
							delete_repo(token=self._token							,						repo_id='''test-generation-config'''   )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(
										    UpperCAmelCase							,						repo_id='''test-generation-config'''							,						push_to_hub=UpperCAmelCase							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained(f"{USER}/test-generation-config"   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						)
							config.push_to_hub('''valid_org/test-generation-config-org'''							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )

        # Reset repo
							delete_repo(token=self._token							,						repo_id='''valid_org/test-generation-config-org'''   )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(
										    UpperCAmelCase							,						repo_id='''valid_org/test-generation-config-org'''							,						push_to_hub=UpperCAmelCase							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )



