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					(		UpperCamelCase__				:   int    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   str="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       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   List[str]=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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   int       ):


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





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					*, UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Optional[Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			traverse_util.flatten_dict(variables['''target''']       )
	__UpperCAmelCase							=			{'''/'''.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
	__UpperCAmelCase							=			'''encoder/layers_0/mlp/wi_0/kernel''' in old
	print('''Split MLP:'''    ,					UpperCAmelCase__       )

	__UpperCAmelCase							=			collections.OrderedDict()

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

	# Encoder.
	for i in range(UpperCAmelCase__       ):
		# Block i, layer 0 (Self Attention).
		__UpperCAmelCase							=			tax_layer_norm_lookup(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''encoder'''    ,					'''pre_attention_layer_norm'''       )
		__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			tax_attention_lookup(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''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(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''encoder'''    ,					'''pre_mlp_layer_norm'''       )
		__UpperCAmelCase				,			__UpperCAmelCase							=			tax_mlp_lookup(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''encoder'''    ,					UpperCAmelCase__       )
		__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(UpperCAmelCase__       ):
			# Block i, layer 0 (Self Attention).
			__UpperCAmelCase							=			tax_layer_norm_lookup(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''decoder'''    ,					'''pre_self_attention_layer_norm'''       )
			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			tax_attention_lookup(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''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(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''decoder'''    ,					'''pre_cross_attention_layer_norm'''       )
			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			tax_attention_lookup(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''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(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''decoder'''    ,					'''pre_mlp_layer_norm'''       )
			__UpperCAmelCase				,			__UpperCAmelCase							=			tax_mlp_lookup(UpperCAmelCase__    ,					UpperCAmelCase__    ,					'''decoder'''    ,					UpperCAmelCase__       )
			__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       lowerCAmelCase					(		UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Dict       ):


	"""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       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			checkpoints.load_tax_checkpoint(UpperCAmelCase__       )
	__UpperCAmelCase							=			convert_tax_to_pytorch(UpperCAmelCase__    ,					num_layers=config.num_layers    ,					is_encoder_only=UpperCAmelCase__       )
	__UpperCAmelCase							=			make_state_dict(UpperCAmelCase__    ,					UpperCAmelCase__       )
	model.load_state_dict(UpperCAmelCase__    ,					strict=UpperCAmelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Optional[int] = False       ):


	"""simple docstring"""
	__UpperCAmelCase							=			TaConfig.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:
		__UpperCAmelCase							=			TaEncoderModel(UpperCAmelCase__       )
	else:
		__UpperCAmelCase							=			TaForConditionalGeneration(UpperCAmelCase__       )

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(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	:       List[str]						       =	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
					)

'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase	:       Optional[Any]						       =	{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	[
					    "LlamaForCausalLM",
					    "LlamaModel",
					    "LlamaPreTrainedModel",
					    "LlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

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