
"""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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase="attention"	):
	__lowerCAmelCase    :		str		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		Union[str, Any]		    =   k_tmp.reshape(k_tmp.shape[0] ,						k_tmp.shape[1] * k_tmp.shape[2]	)
	__lowerCAmelCase    :		str		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		List[str]		    =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,						o_tmp.shape[2]	)
	__lowerCAmelCase    :		Tuple		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		Tuple		    =   q_tmp.reshape(q_tmp.shape[0] ,						q_tmp.shape[1] * q_tmp.shape[2]	)
	__lowerCAmelCase    :		Optional[int]		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		int		    =   v_tmp.reshape(v_tmp.shape[0] ,						v_tmp.shape[1] * v_tmp.shape[2]	)
	return k, o, q, v
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	if split_mlp_wi:
		__lowerCAmelCase    :		List[str]		    =   params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
		__lowerCAmelCase    :		str		    =   params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
		__lowerCAmelCase    :		Tuple		    =   (wi_a, wi_a)
	else:
		__lowerCAmelCase    :		int		    =   params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

	__lowerCAmelCase    :		Union[str, Any]		    =   params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
	return wi, wo
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def 					__lowerCAmelCase       (_UpperCamelCase ,						*, _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase = False	):
	__lowerCAmelCase    :		List[Any]		    =   traverse_util.flatten_dict(variables['target']	)
	__lowerCAmelCase    :		str		    =   {'/'.join(_UpperCamelCase	): v for k, v in old.items()}

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

	__lowerCAmelCase    :		List[str]		    =   collections.OrderedDict()

	# Shared embeddings.
	__lowerCAmelCase    :		Optional[Any]		    =   old['token_embedder/embedding']

	# Encoder.
	for i in range(_UpperCamelCase	):
		# Block i, layer 0 (Self Attention).
		__lowerCAmelCase    :		Optional[int]		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						'pre_attention_layer_norm'	)
		__lowerCAmelCase    :		Dict		    =   tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						'attention'	)
		__lowerCAmelCase    :		Any		    =   layer_norm
		__lowerCAmelCase    :		str		    =   k.T
		__lowerCAmelCase    :		List[Any]		    =   o.T
		__lowerCAmelCase    :		Union[str, Any]		    =   q.T
		__lowerCAmelCase    :		Union[str, Any]		    =   v.T

		# Block i, layer 1 (MLP).
		__lowerCAmelCase    :		Optional[Any]		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						'pre_mlp_layer_norm'	)
		__lowerCAmelCase    :		Union[str, Any]		    =   tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						_UpperCamelCase	)
		__lowerCAmelCase    :		List[str]		    =   layer_norm
		if split_mlp_wi:
			__lowerCAmelCase    :		str		    =   wi[0].T
			__lowerCAmelCase    :		List[str]		    =   wi[1].T
		else:
			__lowerCAmelCase    :		List[str]		    =   wi.T
		__lowerCAmelCase    :		Any		    =   wo.T
		if scalable_attention:
			# convert the rel_embedding of each layer
			__lowerCAmelCase    :		Dict		    =   tax_relpos_bias_lookup(
			    _UpperCamelCase ,						_UpperCamelCase ,						'encoder'	).T

	__lowerCAmelCase    :		Tuple		    =   old['encoder/encoder_norm/scale']

	if not scalable_attention:
		__lowerCAmelCase    :		Dict		    =   tax_relpos_bias_lookup(
		    _UpperCamelCase ,						0 ,						'encoder'	).T
		__lowerCAmelCase    :		Union[str, Any]		    =   tax_relpos_bias_lookup(
		    _UpperCamelCase ,						0 ,						'decoder'	).T

	if not is_encoder_only:
		# Decoder.
		for i in range(_UpperCamelCase	):
			# Block i, layer 0 (Self Attention).
			__lowerCAmelCase    :		Dict		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'pre_self_attention_layer_norm'	)
			__lowerCAmelCase    :		List[Any]		    =   tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'self_attention'	)
			__lowerCAmelCase    :		List[str]		    =   layer_norm
			__lowerCAmelCase    :		Union[str, Any]		    =   k.T
			__lowerCAmelCase    :		Any		    =   o.T
			__lowerCAmelCase    :		Optional[int]		    =   q.T
			__lowerCAmelCase    :		Optional[Any]		    =   v.T

			# Block i, layer 1 (Cross Attention).
			__lowerCAmelCase    :		str		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'pre_cross_attention_layer_norm'	)
			__lowerCAmelCase    :		List[str]		    =   tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'encoder_decoder_attention'	)
			__lowerCAmelCase    :		Optional[Any]		    =   layer_norm
			__lowerCAmelCase    :		List[str]		    =   k.T
			__lowerCAmelCase    :		Optional[int]		    =   o.T
			__lowerCAmelCase    :		Optional[int]		    =   q.T
			__lowerCAmelCase    :		Optional[Any]		    =   v.T

			# Block i, layer 2 (MLP).
			__lowerCAmelCase    :		Tuple		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'pre_mlp_layer_norm'	)
			__lowerCAmelCase    :		Any		    =   tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						_UpperCamelCase	)
			__lowerCAmelCase    :		int		    =   layer_norm
			if split_mlp_wi:
				__lowerCAmelCase    :		Dict		    =   wi[0].T
				__lowerCAmelCase    :		Any		    =   wi[1].T
			else:
				__lowerCAmelCase    :		Optional[Any]		    =   wi.T
			__lowerCAmelCase    :		Dict		    =   wo.T

			if scalable_attention:
				# convert the rel_embedding of each layer
				__lowerCAmelCase    :		List[Any]		    =   tax_relpos_bias_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder'	).T

		__lowerCAmelCase    :		int		    =   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    :		Tuple		    =   old['decoder/logits_dense/kernel'].T

	return new
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   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    :		int		    =   state_dict['shared.weight']

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			__lowerCAmelCase    :		List[str]		    =   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    :		List[Any]		    =   state_dict['shared.weight']

	return state_dict
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[int]		    =   checkpoints.load_tax_checkpoint(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   convert_tax_to_pytorch(
	    _UpperCamelCase ,						num_layers=config.num_layers ,						is_encoder_only=_UpperCamelCase ,						scalable_attention=_UpperCamelCase	)
	__lowerCAmelCase    :		Any		    =   make_state_dict(_UpperCamelCase ,						_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase ,						strict=_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase = False ,						_UpperCamelCase = False ,						):
	__lowerCAmelCase    :		int		    =   MTaConfig.from_json_file(_UpperCamelCase	)
	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    :		Optional[int]		    =   UMTaEncoderModel(_UpperCamelCase	)
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   UMTaForConditionalGeneration(_UpperCamelCase	)

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

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

	# Verify that we can load the checkpoint.
	model.from_pretrained(_UpperCamelCase	)
	print('Done'	)


if __name__ == "__main__":
					lowerCamelCase__           =	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__           =	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 ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			'mctct'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=80_65 ,	_SCREAMING_SNAKE_CASE=15_36 ,	_SCREAMING_SNAKE_CASE=36 ,	_SCREAMING_SNAKE_CASE=61_44 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=3_84 ,	_SCREAMING_SNAKE_CASE=9_20 ,	_SCREAMING_SNAKE_CASE=1E-5 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=(7,) ,	_SCREAMING_SNAKE_CASE=(3,) ,	_SCREAMING_SNAKE_CASE=80 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="sum" ,	_SCREAMING_SNAKE_CASE=False ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE ,	pad_token_id=_SCREAMING_SNAKE_CASE ,	bos_token_id=_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   vocab_size
			__lowerCAmelCase    :		Any		    =   hidden_size
			__lowerCAmelCase    :		int		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   intermediate_size
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		str		    =   attention_head_dim
			__lowerCAmelCase    :		int		    =   max_position_embeddings
			__lowerCAmelCase    :		List[Any]		    =   layer_norm_eps
			__lowerCAmelCase    :		Tuple		    =   layerdrop
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Dict		    =   initializer_range
			__lowerCAmelCase    :		Any		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		List[str]		    =   pad_token_id
			__lowerCAmelCase    :		Union[str, Any]		    =   bos_token_id
			__lowerCAmelCase    :		int		    =   eos_token_id
			__lowerCAmelCase    :		Optional[int]		    =   conv_glu_dim
			__lowerCAmelCase    :		List[Any]		    =   conv_dropout
			__lowerCAmelCase    :		Tuple		    =   num_conv_layers
			__lowerCAmelCase    :		Tuple		    =   input_feat_per_channel
			__lowerCAmelCase    :		Optional[int]		    =   input_channels
			__lowerCAmelCase    :		str		    =   conv_channels
			__lowerCAmelCase    :		str		    =   ctc_loss_reduction
			__lowerCAmelCase    :		Tuple		    =   ctc_zero_infinity

			# prevents config testing fail with exporting to json
			__lowerCAmelCase    :		List[str]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   list(_SCREAMING_SNAKE_CASE				)

			if len(self.conv_kernel				) != self.num_conv_layers:
				raise ValueError(
				    'Configuration for convolutional module is incorrect. '
				    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
				    f"but is `len(config.conv_kernel) = {len(self.conv_kernel				)}`, "
				    f"`config.num_conv_layers = {self.num_conv_layers}`."				)