
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 		_UpperCAmelCase (						UpperCamelCase:  str      ,				UpperCamelCase:  Optional[int]      ,				UpperCamelCase:  List[str]	):






	"""simple docstring"""


	return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]






def 		_UpperCAmelCase (						UpperCamelCase:  List[str]      ,				UpperCamelCase:  List[Any]      ,				UpperCamelCase:  List[str]      ,				UpperCamelCase:  Union[str, Any]="attention"	):






	"""simple docstring"""


	__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 		_UpperCAmelCase (						UpperCamelCase:  List[Any]      ,				UpperCamelCase:  Dict      ,				UpperCamelCase:  str      ,				UpperCamelCase:  Optional[Any]=False	):






	"""simple docstring"""


	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 		_UpperCAmelCase (						UpperCamelCase:  List[Any]      ,				UpperCamelCase:  Union[str, Any]      ,				UpperCamelCase:  Optional[Any]      ,				UpperCamelCase:  Optional[int]	):






	"""simple docstring"""


	return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]






def 		_UpperCAmelCase (						UpperCamelCase:  dict      ,				*, UpperCamelCase:  int      ,				UpperCamelCase:  bool      ,				UpperCamelCase:  bool = False	):






	"""simple docstring"""


	__lowerCAmelCase    =				traverse_util.flatten_dict(variables["target"]	)
	__lowerCAmelCase    =				{"/".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    =				"encoder/encoder/mlp/wi_0/kernel" in old
	print("Split MLP:"      ,				UpperCamelCase	)

	__lowerCAmelCase    =				collections.OrderedDict()

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

	# Encoder.
	for i in range(UpperCamelCase	):
		# Block i, layer 0 (Self Attention).
		__lowerCAmelCase    =				tax_layer_norm_lookup(UpperCamelCase      ,				UpperCamelCase      ,				"encoder"      ,				"pre_attention_layer_norm"	)
		__lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase    =				tax_attention_lookup(UpperCamelCase      ,				UpperCamelCase      ,				"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(UpperCamelCase      ,				UpperCamelCase      ,				"encoder"      ,				"pre_mlp_layer_norm"	)
		__lowerCAmelCase ,    __lowerCAmelCase    =				tax_mlp_lookup(UpperCamelCase      ,				UpperCamelCase      ,				"encoder"      ,				UpperCamelCase	)
		__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(
			    UpperCamelCase      ,				UpperCamelCase      ,				"encoder"	).T

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

	if not scalable_attention:
		__lowerCAmelCase    =				tax_relpos_bias_lookup(
		    UpperCamelCase      ,				0      ,				"encoder"	).T
		__lowerCAmelCase    =				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    =				tax_layer_norm_lookup(UpperCamelCase      ,				UpperCamelCase      ,				"decoder"      ,				"pre_self_attention_layer_norm"	)
			__lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase    =				tax_attention_lookup(UpperCamelCase      ,				UpperCamelCase      ,				"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(UpperCamelCase      ,				UpperCamelCase      ,				"decoder"      ,				"pre_cross_attention_layer_norm"	)
			__lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase    =				tax_attention_lookup(UpperCamelCase      ,				UpperCamelCase      ,				"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(UpperCamelCase      ,				UpperCamelCase      ,				"decoder"      ,				"pre_mlp_layer_norm"	)
			__lowerCAmelCase ,    __lowerCAmelCase    =				tax_mlp_lookup(UpperCamelCase      ,				UpperCamelCase      ,				"decoder"      ,				UpperCamelCase	)
			__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(UpperCamelCase      ,				UpperCamelCase      ,				"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 		_UpperCAmelCase (						UpperCamelCase:  Any      ,				UpperCamelCase:  bool	):






	"""simple docstring"""


	__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 		_UpperCAmelCase (						UpperCamelCase:  List[str]      ,				UpperCamelCase:  Optional[Any]      ,				UpperCamelCase:  str      ,				UpperCamelCase:  int      ,				UpperCamelCase:  List[Any]	):






	"""simple docstring"""


	__lowerCAmelCase    =				checkpoints.load_tax_checkpoint(UpperCamelCase	)
	__lowerCAmelCase    =				convert_tax_to_pytorch(
	    UpperCamelCase      ,				num_layers=config.num_layers      ,				is_encoder_only=UpperCamelCase      ,				scalable_attention=UpperCamelCase	)
	__lowerCAmelCase    =				make_state_dict(UpperCamelCase      ,				UpperCamelCase	)
	model.load_state_dict(UpperCamelCase      ,				strict=UpperCamelCase	)






def 		_UpperCAmelCase (						UpperCamelCase:  Optional[Any]      ,				UpperCamelCase:  List[str]      ,				UpperCamelCase:  List[Any]      ,				UpperCamelCase:  bool = False      ,				UpperCamelCase:  bool = False      ,				):






	"""simple docstring"""


	__lowerCAmelCase    =				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    =				UMTaEncoderModel(UpperCamelCase	)
	else:
		__lowerCAmelCase    =				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__":
					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
					)
					parser.add_argument(
					    "--scalable_attention",
					    action="store_true",
					    help="Whether the model uses scaled attention (umt5 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,
					    args.scalable_attention,
					)








'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    '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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											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}`.'''     )




