





'''simple docstring'''





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def     _UpperCamelCase     (a__   :Tuple   ,			a__   :Optional[Any]   ,			a__   :Optional[int]   ,			a__   :Dict="attention"       ):






							"""simple docstring"""





							UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
							UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
							UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
							UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
							return k, o, q, v





def     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :List[str]   ,			a__   :Union[str, Any]   ,			a__   :Optional[Any]=False       ):






							"""simple docstring"""





							if split_mlp_wi:
														UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
														UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
														UpperCamelCase__				     =			(wi_a, wi_a)
							else:
														UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

							UpperCamelCase__				     =			params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
							return wi, wo





def     _UpperCamelCase     (a__   :int   ,			a__   :Any   ,			a__   :List[Any]   ,			a__   :Dict       ):






							"""simple docstring"""





							return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]





def     _UpperCamelCase     (a__   :Tuple   ,			*, a__   :Tuple   ,			a__   :List[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			traverse_util.flatten_dict(variables["""target"""]       )
							UpperCamelCase__				     =			{'/'.join(SCREAMING_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
							UpperCamelCase__				     =			'encoder/layers_0/mlp/wi_0/kernel' in old
							print("""Split MLP:"""   ,			SCREAMING_SNAKE_CASE_       )

							UpperCamelCase__				     =			collections.OrderedDict()

							# Shared embeddings.
							UpperCamelCase__				     =			old['token_embedder/embedding']

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

														# Block i, layer 1 (MLP).
														UpperCamelCase__				     =			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			"""encoder"""   ,			"""pre_mlp_layer_norm"""       )
														UpperCamelCase__				     =			tax_mlp_lookup(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			"""encoder"""   ,			SCREAMING_SNAKE_CASE_       )
														UpperCamelCase__				     =			layer_norm
														if split_mlp_wi:
																					UpperCamelCase__				     =			wi[0].T
																					UpperCamelCase__				     =			wi[1].T
														else:
																					UpperCamelCase__				     =			wi.T
														UpperCamelCase__				     =			wo.T

							UpperCamelCase__				     =			old[
							    'encoder/relpos_bias/rel_embedding'
							].T
							UpperCamelCase__				     =			old['encoder/encoder_norm/scale']

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

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

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

														UpperCamelCase__				     =			old['decoder/decoder_norm/scale']
														UpperCamelCase__				     =			old[
														    'decoder/relpos_bias/rel_embedding'
														].T

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					UpperCamelCase__				     =			old['decoder/logits_dense/kernel'].T

							return new





def     _UpperCamelCase     (a__   :Any   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			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:
														UpperCamelCase__				     =			state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					UpperCamelCase__				     =			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."""       )
																					UpperCamelCase__				     =			state_dict['shared.weight']

							return state_dict





def     _UpperCamelCase     (a__   :int   ,			a__   :Optional[int]   ,			a__   :str   ,			a__   :Tuple       ):






							"""simple docstring"""





							UpperCamelCase__				     =			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_       )
							UpperCamelCase__				     =			convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_   ,			num_layers=config.num_layers   ,			is_encoder_only=SCREAMING_SNAKE_CASE_       )
							UpperCamelCase__				     =			make_state_dict(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_       )
							model.load_state_dict(SCREAMING_SNAKE_CASE_   ,			strict=SCREAMING_SNAKE_CASE_       )





def     _UpperCamelCase     (a__   :Dict   ,			a__   :Union[str, Any]   ,			a__   :Optional[int]   ,			a__   :Dict = False       ):






							"""simple docstring"""





							UpperCamelCase__				     =			TaConfig.from_json_file(SCREAMING_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:
														UpperCamelCase__				     =			TaEncoderModel(SCREAMING_SNAKE_CASE_       )
							else:
														UpperCamelCase__				     =			TaForConditionalGeneration(SCREAMING_SNAKE_CASE_       )

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

							# Save pytorch-model
							print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
							model.save_pretrained(SCREAMING_SNAKE_CASE_       )

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


if __name__ == "__main__":
					UpperCamelCase__											= 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
					)
					UpperCamelCase__											= parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
					)











import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[Any]						     =       """data2vec-audio"""






							def __init__(						self  , __lowerCAmelCase=32  , __lowerCAmelCase=768  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.0  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-5  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)  , __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)  , __lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)  , __lowerCAmelCase=False  , __lowerCAmelCase=16  , __lowerCAmelCase=19  , __lowerCAmelCase=5  , __lowerCAmelCase=0.05  , __lowerCAmelCase=10  , __lowerCAmelCase=2  , __lowerCAmelCase=0.0  , __lowerCAmelCase=10  , __lowerCAmelCase=0  , __lowerCAmelCase="sum"  , __lowerCAmelCase=False  , __lowerCAmelCase=False  , __lowerCAmelCase=256  , __lowerCAmelCase=(512, 512, 512, 512, 1500)  , __lowerCAmelCase=(5, 3, 3, 1, 1)  , __lowerCAmelCase=(1, 2, 3, 1, 1)  , __lowerCAmelCase=512  , __lowerCAmelCase=0  , __lowerCAmelCase=1  , __lowerCAmelCase=2  , __lowerCAmelCase=False  , __lowerCAmelCase=3  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=None  , **__lowerCAmelCase  , ):
														super().__init__(**__lowerCAmelCase  , pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  )
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			feat_extract_activation
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			conv_bias
														UpperCamelCase__				     =			num_conv_pos_embeddings
														UpperCamelCase__				     =			num_conv_pos_embedding_groups
														UpperCamelCase__				     =			conv_pos_kernel_size
														UpperCamelCase__				     =			len(self.conv_dim  )
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_dropout
														UpperCamelCase__				     =			attention_dropout
														UpperCamelCase__				     =			activation_dropout
														UpperCamelCase__				     =			feat_proj_dropout
														UpperCamelCase__				     =			final_dropout
														UpperCamelCase__				     =			layerdrop
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			use_weighted_layer_sum

														if (
														    (len(self.conv_stride  ) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
														    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																					    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																					    f""" {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,"""
																					    f""" `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														UpperCamelCase__				     =			mask_time_prob
														UpperCamelCase__				     =			mask_time_length
														UpperCamelCase__				     =			mask_time_min_masks
														UpperCamelCase__				     =			mask_feature_prob
														UpperCamelCase__				     =			mask_feature_length
														UpperCamelCase__				     =			mask_feature_min_masks

														# ctc loss
														UpperCamelCase__				     =			ctc_loss_reduction
														UpperCamelCase__				     =			ctc_zero_infinity

														# adapter
														UpperCamelCase__				     =			add_adapter
														UpperCamelCase__				     =			adapter_kernel_size
														UpperCamelCase__				     =			adapter_stride
														UpperCamelCase__				     =			num_adapter_layers
														UpperCamelCase__				     =			output_hidden_size or hidden_size

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														UpperCamelCase__				     =			classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			xvector_output_dim







							@property
							def 				_lowerCamelCase		(						self  ):
														return math.prod(self.conv_stride  )




