




"""simple docstring"""




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__="attention"				)						->					List[Any]:
			lowerCamelCase			      =					lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					k_tmp.reshape(k_tmp.shape[0]       , k_tmp.shape[1] * k_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]       , o_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					q_tmp.reshape(q_tmp.shape[0]       , q_tmp.shape[1] * q_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					v_tmp.reshape(v_tmp.shape[0]       , v_tmp.shape[1] * v_tmp.shape[2]				)
			return k, o, q, v



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__=False				)						->					List[str]:
			if split_mlp_wi:
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
						lowerCamelCase			      =					(wi_a, wi_a)
			else:
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

			lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
			return wi, wo



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					Tuple:
			return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def     a__ (				snake_case__       , *, snake_case__       , snake_case__       , snake_case__ = False				)						->					Dict:
			lowerCamelCase			      =					traverse_util.flatten_dict(variables["""target"""]				)
			lowerCamelCase			      =					{"""/""".join(snake_case__				): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			lowerCamelCase			      =					"""encoder/encoder/mlp/wi_0/kernel""" in old
			print("""Split MLP:"""       , snake_case__				)

			lowerCamelCase			      =					collections.OrderedDict()

			# Shared embeddings.
			lowerCamelCase			      =					old["""token_embedder/embedding"""]

			# Encoder.
			for i in range(snake_case__				):
						# Block i, layer 0 (Self Attention).
						lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """encoder"""       , """pre_attention_layer_norm"""				)
						lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """encoder"""       , """attention"""				)
						lowerCamelCase			      =					layer_norm
						lowerCamelCase			      =					k.T
						lowerCamelCase			      =					o.T
						lowerCamelCase			      =					q.T
						lowerCamelCase			      =					v.T

						# Block i, layer 1 (MLP).
						lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """encoder"""       , """pre_mlp_layer_norm"""				)
						lowerCamelCase      ,   lowerCamelCase			      =					tax_mlp_lookup(snake_case__       , snake_case__       , """encoder"""       , snake_case__				)
						lowerCamelCase			      =					layer_norm
						if split_mlp_wi:
									lowerCamelCase			      =					wi[0].T
									lowerCamelCase			      =					wi[1].T
						else:
									lowerCamelCase			      =					wi.T
						lowerCamelCase			      =					wo.T
						if scalable_attention:
									# convert the rel_embedding of each layer
									lowerCamelCase			      =					tax_relpos_bias_lookup(
									    snake_case__       , snake_case__       , """encoder"""				).T

			lowerCamelCase			      =					old["""encoder/encoder_norm/scale"""]

			if not scalable_attention:
						lowerCamelCase			      =					tax_relpos_bias_lookup(
						    snake_case__       , 0       , """encoder"""				).T
						lowerCamelCase			      =					tax_relpos_bias_lookup(
						    snake_case__       , 0       , """decoder"""				).T

			if not is_encoder_only:
						# Decoder.
						for i in range(snake_case__				):
									# Block i, layer 0 (Self Attention).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_self_attention_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """decoder"""       , """self_attention"""				)
									lowerCamelCase			      =					layer_norm
									lowerCamelCase			      =					k.T
									lowerCamelCase			      =					o.T
									lowerCamelCase			      =					q.T
									lowerCamelCase			      =					v.T

									# Block i, layer 1 (Cross Attention).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_cross_attention_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """decoder"""       , """encoder_decoder_attention"""				)
									lowerCamelCase			      =					layer_norm
									lowerCamelCase			      =					k.T
									lowerCamelCase			      =					o.T
									lowerCamelCase			      =					q.T
									lowerCamelCase			      =					v.T

									# Block i, layer 2 (MLP).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_mlp_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase			      =					tax_mlp_lookup(snake_case__       , snake_case__       , """decoder"""       , snake_case__				)
									lowerCamelCase			      =					layer_norm
									if split_mlp_wi:
												lowerCamelCase			      =					wi[0].T
												lowerCamelCase			      =					wi[1].T
									else:
												lowerCamelCase			      =					wi.T
									lowerCamelCase			      =					wo.T

									if scalable_attention:
												# convert the rel_embedding of each layer
												lowerCamelCase			      =					tax_relpos_bias_lookup(snake_case__       , snake_case__       , """decoder"""				).T

						lowerCamelCase			      =					old["""decoder/decoder_norm/scale"""]

						# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
						if "decoder/logits_dense/kernel" in old:
									lowerCamelCase			      =					old["""decoder/logits_dense/kernel"""].T

			return new



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

			# Add what is missing.
			if "encoder.embed_tokens.weight" not in state_dict:
						lowerCamelCase			      =					state_dict["""shared.weight"""]

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCamelCase			      =					state_dict["""shared.weight"""]

						if "lm_head.weight" not in state_dict:  # For old 1.0 models.
									print("""Using shared word embeddings as lm_head."""				)
									lowerCamelCase			      =					state_dict["""shared.weight"""]

			return state_dict



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					checkpoints.load_tax_checkpoint(snake_case__				)
			lowerCamelCase			      =					convert_tax_to_pytorch(
			    snake_case__       , num_layers=config.num_layers       , is_encoder_only=snake_case__       , scalable_attention=snake_case__				)
			lowerCamelCase			      =					make_state_dict(snake_case__       , snake_case__				)
			model.load_state_dict(snake_case__       , strict=snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__ = False       , snake_case__ = False       , )						->					str:
			lowerCamelCase			      =					MTaConfig.from_json_file(snake_case__				)
			print(F'Building PyTorch model from configuration: {config}'				)
			# Non-v1.1 checkpoints could also use T5Model, but this works for all.
			# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
			if is_encoder_only:
						lowerCamelCase			      =					UMTaEncoderModel(snake_case__				)
			else:
						lowerCamelCase			      =					UMTaForConditionalGeneration(snake_case__				)

			# Load weights from tf checkpoint
			load_tax_weights_in_ta(snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'				)
			model.save_pretrained(snake_case__				)

			# Verify that we can load the checkpoint.
			model.from_pretrained(snake_case__				)
			print("""Done"""				)


if __name__ == "__main__":
					lowerCAmelCase							:			Optional[int]     							=							argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
					# Required parameters
					parser.add_argument(
					    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
					)
					parser.add_argument(
					    """--scalable_attention""",
					    action="""store_true""",
					    help="""Whether the model uses scaled attention (umt5 model)""",
					    default=False,
					)
					lowerCAmelCase							:			int     							=							parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path,
					    args.config_file,
					    args.pytorch_dump_path,
					    args.is_encoder_only,
					    args.scalable_attention,
					)









"""simple docstring"""




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowerCAmelCase							:			List[str]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["audio_values", "audio_mask"]





				def __init__(       self		, _a=2_048		, _a=1		, _a=[16, 16]		, _a=128		, _a=44_100		, _a=86		, _a=2_048		, _a=0.0		, **_a		, ):





							"""simple docstring"""
							super().__init__(
							    feature_size=_a		, sampling_rate=_a		, padding_value=_a		, **_a		, )

							lowerCamelCase			      =					spectrogram_length
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					feature_size // self.patch_size[1]
							lowerCamelCase			      =					n_fft
							lowerCamelCase			      =					sampling_rate // hop_length_to_sampling_rate
							lowerCamelCase			      =					sampling_rate
							lowerCamelCase			      =					padding_value
							lowerCamelCase			      =					mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2		, num_mel_filters=_a		, min_frequency=0.0		, max_frequency=22_050.0		, sampling_rate=_a		, norm="""slaney"""		, mel_scale="""slaney"""		, ).T





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					spectrogram(
							    _a		, window_function(self.n_fft		, """hann""" )		, frame_length=self.n_fft		, hop_length=self.hop_length		, power=2.0		, mel_filters=self.mel_filters.T		, log_mel="""dB"""		, db_range=80.0		, )
							lowerCamelCase			      =					log_spec[:, :-1]
							lowerCamelCase			      =					log_spec - 20.0
							lowerCamelCase			      =					np.clip(log_spec / 40.0		, -2.0		, 0.0 ) + 1.0
							return log_spec





				def __call__(       self		, _a		, _a = None		, _a = True		, _a = None		, _a = False		, _a = False		, **_a		, ):





							"""simple docstring"""

							if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
													raise ValueError(
													    """This feature extractor is set to support sampling rate"""
													    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
													    f' with {self.sampling_rate} and not {sampling_rate}.' )
							else:
										logger.warning(
										    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
										    """Failing to do so can result in silent errors that might be hard to debug.""" )

							lowerCamelCase			      =					isinstance(_a		, np.ndarray ) and len(raw_speech.shape ) > 1
							if is_batched_numpy and len(raw_speech.shape ) > 2:
										raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
							lowerCamelCase			      =					is_batched_numpy or (
							    isinstance(_a		, (list, tuple) ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list) ))
							)
							if is_batched:
										lowerCamelCase			      =					[np.asarray([speech]		, dtype=np.floataa ).T for speech in raw_speech]
							elif not is_batched and not isinstance(_a		, np.ndarray ):
										lowerCamelCase			      =					np.asarray(_a		, dtype=np.floataa )
							elif isinstance(_a		, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
										lowerCamelCase			      =					raw_speech.astype(np.floataa )
							# always return batch
							if not is_batched:
										lowerCamelCase			      =					[np.asarray([raw_speech] ).T]

							# Convert audio signals to log mel spectrograms, truncate by time axis
							lowerCamelCase			      =					[
							    self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
							]
							if isinstance(audio_features[0]		, _a ):
										lowerCamelCase			      =					[np.asarray(_a		, dtype=np.floataa ) for feature in audio_features]

							# Create audio attention mask
							lowerCamelCase			      =					max(
							    [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
							if return_attention_mask:
										lowerCamelCase			      =					[
										    (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
										    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
										    for feature in audio_features
										]
										lowerCamelCase			      =					np.array(_a ).astype(np.floataa )

							# convert into correct format for padding
							lowerCamelCase			      =					max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
							lowerCamelCase			      =					np.ones([len(_a ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
							lowerCamelCase			      =					padded_audio_features * self.padding_value
							for i in range(len(_a ) ):
										lowerCamelCase			      =					audio_features[i]
										lowerCamelCase			      =					feature

							# return as BatchFeature
							if return_attention_mask:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features}

							lowerCamelCase			      =					BatchFeature(data=_a		, tensor_type=_a )
							return encoded_inputs



