

from ...processing_utils import ProcessorMixin

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Tuple	    =    "SpeechT5FeatureExtractor"
							UpperCAmelCase__       :						Optional[int]	    =    "SpeechT5Tokenizer"


							def __init__(			self       ,							_a       ,							_a       )    ->    List[Any]:
												super().__init__(_a       ,							_a       )


							def __call__(			self       ,							*_a       ,							**_a       )    ->    int:
												_a	:    Dict         =		kwargs.pop('''audio'''       ,							_a       )
												_a	:    Dict         =		kwargs.pop('''text'''       ,							_a       )
												_a	:    Union[str, Any]         =		kwargs.pop('''text_target'''       ,							_a       )
												_a	:    Tuple         =		kwargs.pop('''audio_target'''       ,							_a       )
												_a	:    Tuple         =		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:
																	_a	:    Optional[int]         =		self.feature_extractor(_a       ,							*_a       ,							sampling_rate=_a       ,							**_a       )
												elif text is not None:
																	_a	:    Any         =		self.tokenizer(_a       ,							**_a       )
												else:
																	_a	:    int         =		None

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

												if inputs is None:
																	return targets

												if targets is not None:
																	_a	:    int         =		labels

																	_a	:    List[Any]         =		targets.get('''attention_mask'''       )
																	if decoder_attention_mask is not None:
																						_a	:    Optional[Any]         =		decoder_attention_mask

												return inputs


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Tuple:
												_a	:    Optional[Any]         =		kwargs.pop('''input_values'''       ,							_a       )
												_a	:    Dict         =		kwargs.pop('''input_ids'''       ,							_a       )
												_a	:    int         =		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:
																	_a	:    Union[str, Any]         =		self.feature_extractor.pad(_a       ,							*_a       ,							**_a       )
												elif input_ids is not None:
																	_a	:    int         =		self.tokenizer.pad(_a       ,							**_a       )
												else:
																	_a	:    Dict         =		None

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

												if inputs is None:
																	return targets

												if targets is not None:
																	_a	:    Union[str, Any]         =		labels

																	_a	:    int         =		targets.get('''attention_mask'''       )
																	if decoder_attention_mask is not None:
																						_a	:    Optional[int]         =		decoder_attention_mask

												return inputs


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Tuple:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )




							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.decode(*_a       ,							**_a       )






import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			Dict	)			->     Tuple:

					"""simple docstring"""

					_a	:    Any         =		R'''\w+[.]\d+'''
					_a	:    Union[str, Any]         =		re.findall(__a		,__a	)
					for pat in pats:
										_a	:    int         =		key.replace(__a		,'''_'''.join(pat.split('''.'''	)	)	)
					return key



def    __UpperCAmelCase (	__a      :			List[str]		,__a      :			Union[str, Any]		,__a      :			Optional[int]	)			->     Tuple:

					"""simple docstring"""

					_a	:    Dict         =		pt_tuple_key[:-1] + ('''scale''',)
					if (
					    any('''norm''' in str_ for str_ in pt_tuple_key	)
					    and (pt_tuple_key[-1] == "bias")
					    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
					    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
					):
										_a	:    Dict         =		pt_tuple_key[:-1] + ('''scale''',)
										return renamed_pt_tuple_key, pt_tensor
					elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
										_a	:    Optional[int]         =		pt_tuple_key[:-1] + ('''scale''',)
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
										_a	:    Union[str, Any]         =		pt_tuple_key[:-1] + ('''embedding''',)
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					_a	:    Tuple         =		pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
										_a	:    List[str]         =		pt_tensor.transpose(2		,3		,1		,0	)
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					_a	:    Dict         =		pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight":
										_a	:    Union[str, Any]         =		pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					_a	:    Dict         =		pt_tuple_key[:-1] + ('''weight''',)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					_a	:    Union[str, Any]         =		pt_tuple_key[:-1] + ('''bias''',)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor







def    __UpperCAmelCase (	__a      :			Dict		,__a      :			str		,__a      :			str=42	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    Tuple         =		{k: v.numpy() for k, v in pt_state_dict.items()}

					# Step 2: Since the model is stateless, get random Flax params
					_a	:    List[Any]         =		flax_model.init_weights(PRNGKey(__a	)	)

					_a	:    Optional[int]         =		flatten_dict(__a	)
					_a	:    Tuple         =		{}

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										_a	:    List[str]         =		rename_key(__a	)
										_a	:    Optional[Any]         =		tuple(renamed_pt_key.split('''.'''	)	)

										# Correctly rename weight parameters
										_a      ,					_a	:    List[str]         =		rename_key_and_reshape_tensor(__a		,__a		,__a	)

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																				    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)

        # also add unexpected weight so that warning is thrown
										_a	:    Dict         =		jnp.asarray(__a	)

					return unflatten_dict(__a	)



