






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Tuple   =					'''ClapFeatureExtractor'''
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					('''RobertaTokenizer''', '''RobertaTokenizerFast''')






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												super().__init__(_UpperCamelCase     ,						_UpperCamelCase   )






						def __call__(	self     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.pop('sampling_rate'     ,						_UpperCamelCase   )

												if text is None and audios is None:
																		raise ValueError('You have to specify either text or audios. Both cannot be none.'   )

												if text is not None:
																		lowerCAmelCase__    						=						self.tokenizer(_UpperCamelCase     ,						return_tensors=_UpperCamelCase     ,						**_UpperCamelCase   )

												if audios is not None:
																		lowerCAmelCase__    						=						self.feature_extractor(
																		    _UpperCamelCase     ,						sampling_rate=_UpperCamelCase     ,						return_tensors=_UpperCamelCase     ,						**_UpperCamelCase   )

												if text is not None and audios is not None:
																		lowerCAmelCase__    						=						audio_features.input_features
																		return encoding
												elif text is not None:
																		return encoding
												else:
																		return BatchEncoding(data=dict(**_UpperCamelCase   )     ,						tensor_type=_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												return self.tokenizer.batch_decode(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												return self.tokenizer.decode(*_UpperCamelCase     ,						**_UpperCamelCase   )







						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.tokenizer.model_input_names
												lowerCAmelCase__    						=						self.feature_extractor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names   )   )













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





