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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase="attention"						):
					__a		 =       params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
					__a		 =       params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
					__a		 =       params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
					__a		 =       params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
					return k, o, q, v
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=False						):
					if split_mlp_wi:
										__a		 =       params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
										__a		 =       params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
										__a		 =       (wi_a, wi_a)
					else:
										__a		 =       params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

					__a		 =       params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
					return wi, wo
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']
def 							lowerCAmelCase(     __lowerCamelCase				,    *, __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       traverse_util.flatten_dict(variables['target']						)
					__a		 =       {'/'.join(__lowerCamelCase						): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					__a		 =       'encoder/layers_0/mlp/wi_0/kernel' in old
					print('Split MLP:'				,    __lowerCamelCase						)

					__a		 =       collections.OrderedDict()

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

					# Encoder.
					for i in range(__lowerCamelCase						):
										# Block i, layer 0 (Self Attention).
										__a		 =       tax_layer_norm_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'encoder'				,    'pre_attention_layer_norm'						)
										__a ,				__a ,				__a ,				__a		 =       tax_attention_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'encoder'				,    'attention'						)
										__a		 =       layer_norm
										__a		 =       k.T
										__a		 =       o.T
										__a		 =       q.T
										__a		 =       v.T

										# Block i, layer 1 (MLP).
										__a		 =       tax_layer_norm_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'encoder'				,    'pre_mlp_layer_norm'						)
										__a ,				__a		 =       tax_mlp_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'encoder'				,    __lowerCamelCase						)
										__a		 =       layer_norm
										if split_mlp_wi:
															__a		 =       wi[0].T
															__a		 =       wi[1].T
										else:
															__a		 =       wi.T
										__a		 =       wo.T

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

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

															# Block i, layer 1 (Cross Attention).
															__a		 =       tax_layer_norm_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'decoder'				,    'pre_cross_attention_layer_norm'						)
															__a ,				__a ,				__a ,				__a		 =       tax_attention_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'decoder'				,    'encoder_decoder_attention'						)
															__a		 =       layer_norm
															__a		 =       k.T
															__a		 =       o.T
															__a		 =       q.T
															__a		 =       v.T

															# Block i, layer 2 (MLP).
															__a		 =       tax_layer_norm_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'decoder'				,    'pre_mlp_layer_norm'						)
															__a ,				__a		 =       tax_mlp_lookup(__lowerCamelCase				,    __lowerCamelCase				,    'decoder'				,    __lowerCamelCase						)
															__a		 =       layer_norm
															if split_mlp_wi:
																				__a		 =       wi[0].T
																				__a		 =       wi[1].T
															else:
																				__a		 =       wi.T
															__a		 =       wo.T

										__a		 =       old['decoder/decoder_norm/scale']
										__a		 =       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:
															__a		 =       old['decoder/logits_dense/kernel'].T

					return new
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       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:
										__a		 =       state_dict['shared.weight']

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

					return state_dict
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       checkpoints.load_tax_checkpoint(__lowerCamelCase						)
					__a		 =       convert_tax_to_pytorch(__lowerCamelCase				,    num_layers=config.num_layers				,    is_encoder_only=__lowerCamelCase						)
					__a		 =       make_state_dict(__lowerCamelCase				,    __lowerCamelCase						)
					model.load_state_dict(__lowerCamelCase				,    strict=__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase = False						):
					__a		 =       TaConfig.from_json_file(__lowerCamelCase						)
					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:
										__a		 =       TaEncoderModel(__lowerCamelCase						)
					else:
										__a		 =       TaForConditionalGeneration(__lowerCamelCase						)

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

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

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


if __name__ == "__main__":
	lowerCamelCase_    :		Any	       =	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
	)
	lowerCamelCase_    :		Union[str, Any]	       =	parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""SpeechEncoderDecoderModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




