




import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
		import librosa  # noqa: E402

		lowercase_		=							True
		lowercase_		=							''''''
except Exception as e:
		lowercase_		=							False
		lowercase_		=							(
		    f"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."
		)


from PIL import Image  # noqa: E402





class 		A__    (       UpperCamelCase_		,					UpperCamelCase_			):
	lowerCamelCase__		:			Any       ='mel_config.json'






	@register_to_config
	def __init__(     self    ,					lowerCamelCase = 256    ,					lowerCamelCase = 256    ,					lowerCamelCase = 22050    ,					lowerCamelCase = 2048    ,					lowerCamelCase = 512    ,					lowerCamelCase = 80    ,					lowerCamelCase = 32    ,					)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							hop_length
						__magic_name__ :							Optional[int]		       =							sample_rate
						__magic_name__ :							Tuple		       =							n_fft
						__magic_name__ :							int		       =							top_db
						__magic_name__ :							List[Any]		       =							n_iter
						self.set_resolution(lowercase_    ,					lowercase_						)
						__magic_name__ :							Dict		       =							None

						if not _librosa_can_be_imported:
											raise ValueError(_import_error						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							x_res
						__magic_name__ :							int		       =							y_res
						__magic_name__ :							Dict		       =							self.y_res
						__magic_name__ :							Tuple		       =							self.x_res * self.hop_length - 1






	def 			lowercase				(     self    ,					lowerCamelCase = None    ,					lowerCamelCase = None						)				->       Union[str, Any]:




						"""simple docstring"""






						if audio_file is not None:
											__magic_name__ :							Union[str, Any]		       =							librosa.load(lowercase_    ,					mono=lowercase_    ,					sr=self.sr						)
						else:
											__magic_name__ :							Optional[int]		       =							raw_audio

						# Pad with silence if necessary.
						if len(self.audio						) < self.x_res * self.hop_length:
											__magic_name__ :							Any		       =							np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio						),)						)]						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						return len(self.audio						) // self.slice_size






	def 			lowercase				(     self    ,					lowerCamelCase = 0						)				->       np.ndarray:




						"""simple docstring"""






						return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						return self.sr






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Image.Image:




						"""simple docstring"""






						__magic_name__ :							Any		       =							librosa.feature.melspectrogram(
						    y=self.get_audio_slice(lowercase_						)    ,					sr=self.sr    ,					n_fft=self.n_fft    ,					hop_length=self.hop_length    ,					n_mels=self.n_mels						)
						__magic_name__ :							int		       =							librosa.power_to_db(lowercase_    ,					ref=np.max    ,					top_db=self.top_db						)
						__magic_name__ :							Dict		       =							(((log_S + self.top_db) * 255 / self.top_db).clip(0    ,					255						) + 0.5).astype(np.uinta						)
						__magic_name__ :							List[str]		       =							Image.fromarray(lowercase_						)
						return image






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       np.ndarray:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							np.frombuffer(image.tobytes()    ,					dtype='''uint8'''						).reshape((image.height, image.width)						)
						__magic_name__ :							int		       =							bytedata.astype('''float'''						) * self.top_db / 255 - self.top_db
						__magic_name__ :							str		       =							librosa.db_to_power(lowercase_						)
						__magic_name__ :							List[str]		       =							librosa.feature.inverse.mel_to_audio(
						    lowercase_    ,					sr=self.sr    ,					n_fft=self.n_fft    ,					hop_length=self.hop_length    ,					n_iter=self.n_iter						)
						return audio









from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class 		A__    (       __SCREAMING_SNAKE_CASE			):
	lowerCamelCase__		:			Optional[int]       ="m2m_100"
	lowerCamelCase__		:			List[str]       =["past_key_values"]
	lowerCamelCase__		:			Union[str, Any]       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






	def __init__(     self    ,					lowerCamelCase=128112    ,					lowerCamelCase=1024    ,					lowerCamelCase=12    ,					lowerCamelCase=4096    ,					lowerCamelCase=16    ,					lowerCamelCase=12    ,					lowerCamelCase=4096    ,					lowerCamelCase=16    ,					lowerCamelCase=0.0_5    ,					lowerCamelCase=0.0_5    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase="relu"    ,					lowerCamelCase=1024    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.0    ,					lowerCamelCase=0.0_2    ,					lowerCamelCase=2    ,					lowerCamelCase=True    ,					lowerCamelCase=1    ,					lowerCamelCase=0    ,					lowerCamelCase=2    ,					**lowerCamelCase    ,					)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							vocab_size
						__magic_name__ :							str		       =							max_position_embeddings
						__magic_name__ :							Union[str, Any]		       =							d_model
						__magic_name__ :							Dict		       =							encoder_ffn_dim
						__magic_name__ :							List[Any]		       =							encoder_layers
						__magic_name__ :							Optional[int]		       =							encoder_attention_heads
						__magic_name__ :							Dict		       =							decoder_ffn_dim
						__magic_name__ :							List[str]		       =							decoder_layers
						__magic_name__ :							Optional[Any]		       =							decoder_attention_heads
						__magic_name__ :							List[Any]		       =							dropout
						__magic_name__ :							List[str]		       =							attention_dropout
						__magic_name__ :							Optional[int]		       =							activation_dropout
						__magic_name__ :							Dict		       =							activation_function
						__magic_name__ :							Tuple		       =							init_std
						__magic_name__ :							Optional[int]		       =							encoder_layerdrop
						__magic_name__ :							int		       =							decoder_layerdrop
						__magic_name__ :							Union[str, Any]		       =							use_cache
						__magic_name__ :							Optional[int]		       =							encoder_layers
						__magic_name__ :							Dict		       =							scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=lowerCamelCase    ,					bos_token_id=lowerCamelCase    ,					eos_token_id=lowerCamelCase    ,					is_encoder_decoder=lowerCamelCase    ,					decoder_start_token_id=lowerCamelCase    ,					**lowerCamelCase    ,					)





class 		A__    (       __SCREAMING_SNAKE_CASE			):






	@property
	def 			lowercase				(     self						)				->       Mapping[str, Mapping[int, str]]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]						)

						if self.use_past:
											__magic_name__ :							Optional[Any]		       =							{0: '''batch'''}
											__magic_name__ :							Any		       =							{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
											__magic_name__ :							Union[str, Any]		       =							{0: '''batch''', 1: '''decoder_sequence'''}
											__magic_name__ :							Optional[Any]		       =							{0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
											self.fill_with_past_key_values_(lowerCamelCase    ,					direction='''inputs'''						)
						return common_inputs






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase = -1    ,					lowerCamelCase = -1    ,					lowerCamelCase = False    ,					lowerCamelCase = None    ,					)				->       Mapping[str, Any]:




						"""simple docstring"""






						__magic_name__ :							int		       =							compute_effective_axis_dimension(
						    lowerCamelCase    ,					fixed_dimension=OnnxConfig.default_fixed_batch    ,					num_token_to_add=0						)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__magic_name__ :							str		       =							tokenizer.num_special_tokens_to_add(lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							compute_effective_axis_dimension(
						    lowerCamelCase    ,					fixed_dimension=OnnxConfig.default_fixed_sequence    ,					num_token_to_add=lowerCamelCase						)

						# Generate dummy inputs according to compute batch and sequence
						__magic_name__ :							Optional[Any]		       =							[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
						__magic_name__ :							Any		       =							dict(tokenizer(lowerCamelCase    ,					return_tensors=lowerCamelCase						)						)
						return common_inputs






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase = -1    ,					lowerCamelCase = -1    ,					lowerCamelCase = False    ,					lowerCamelCase = None    ,					)				->       Mapping[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)

						# Generate decoder inputs
						__magic_name__ :							int		       =							seq_length if not self.use_past else 1
						__magic_name__ :							Optional[Any]		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							List[str]		       =							{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__magic_name__ :							Optional[Any]		       =							dict(**lowerCamelCase    ,					**lowerCamelCase						)

						if self.use_past:
											if not is_torch_available():
																raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
											else:
																import torch
											__magic_name__       ,			__magic_name__ :							List[Any]		       =							common_inputs['''input_ids'''].shape
											__magic_name__ :							List[str]		       =							common_inputs['''decoder_input_ids'''].shape[1]
											__magic_name__       ,			__magic_name__ :							Any		       =							self.num_attention_heads
											__magic_name__ :							Optional[int]		       =							(
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											__magic_name__ :							Tuple		       =							decoder_seq_length + 3
											__magic_name__ :							str		       =							(
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											__magic_name__ :							int		       =							torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase    ,					lowerCamelCase						)]    ,					dim=1						)

											__magic_name__ :							List[str]		       =							[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__magic_name__       ,			__magic_name__ :							int		       =							self.num_layers
											__magic_name__ :							List[Any]		       =							min(lowerCamelCase    ,					lowerCamelCase						)
											__magic_name__ :							int		       =							max(lowerCamelCase    ,					lowerCamelCase						) - min_num_layers
											__magic_name__ :							Optional[int]		       =							'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

											for _ in range(lowerCamelCase						):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(lowerCamelCase						),
																        torch.zeros(lowerCamelCase						),
																        torch.zeros(lowerCamelCase						),
																        torch.zeros(lowerCamelCase						),
																    )						)
											# TODO: test this.
											__magic_name__ :							Tuple		       =							encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
											for _ in range(lowerCamelCase    ,					lowerCamelCase						):
																common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase						), torch.zeros(lowerCamelCase						))						)
						return common_inputs





	lowerCamelCase__		:			List[str]       =_generate_dummy_inputs_for_default_and_seqaseq_lm



