
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   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__="attention"		):
					'''simple docstring'''
					lowercase						     =						lowercase						     =						np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]		)
					lowercase						     =						k_tmp.reshape(k_tmp.shape[0]			,      k_tmp.shape[1] * k_tmp.shape[2]		)
					lowercase						     =						np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]		)
					lowercase						     =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,      o_tmp.shape[2]		)
					lowercase						     =						np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]		)
					lowercase						     =						q_tmp.reshape(q_tmp.shape[0]			,      q_tmp.shape[1] * q_tmp.shape[2]		)
					lowercase						     =						np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]		)
					lowercase						     =						v_tmp.reshape(v_tmp.shape[0]			,      v_tmp.shape[1] * v_tmp.shape[2]		)
					return k, o, q, v



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=False		):
					'''simple docstring'''
					if split_mlp_wi:
										lowercase						     =						params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
										lowercase						     =						params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
										lowercase						     =						(wi_a, wi_a)
					else:
										lowercase						     =						params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

					lowercase						     =						params[f'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
					return wi, wo



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def 	UpperCamelCase   (							lowerCAmelCase__			,      *, lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__ = False		):
					'''simple docstring'''
					lowercase						     =						traverse_util.flatten_dict(variables['''target''']		)
					lowercase						     =						{'''/'''.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
					lowercase						     =						'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''			,      lowerCAmelCase__		)

					lowercase						     =						collections.OrderedDict()

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

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

										# Block i, layer 1 (MLP).
										lowercase						     =						tax_layer_norm_lookup(lowerCAmelCase__			,      lowerCAmelCase__			,      '''encoder'''			,      '''pre_mlp_layer_norm'''		)
										lowercase     ,						lowercase						     =						tax_mlp_lookup(lowerCAmelCase__			,      lowerCAmelCase__			,      '''encoder'''			,      lowerCAmelCase__		)
										lowercase						     =						layer_norm
										if split_mlp_wi:
															lowercase						     =						wi[0].T
															lowercase						     =						wi[1].T
										else:
															lowercase						     =						wi.T
										lowercase						     =						wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															lowercase						     =						tax_relpos_bias_lookup(
															    lowerCAmelCase__			,      lowerCAmelCase__			,      '''encoder'''		).T

					lowercase						     =						old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										lowercase						     =						tax_relpos_bias_lookup(
										    lowerCAmelCase__			,      0			,      '''encoder'''		).T
										lowercase						     =						tax_relpos_bias_lookup(
										    lowerCAmelCase__			,      0			,      '''decoder'''		).T

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

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

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

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				lowercase						     =						tax_relpos_bias_lookup(lowerCAmelCase__			,      lowerCAmelCase__			,      '''decoder'''		).T

										lowercase						     =						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:
															lowercase						     =						old['''decoder/logits_dense/kernel'''].T

					return new



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						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:
										lowercase						     =						state_dict['''shared.weight''']

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

					return state_dict



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						checkpoints.load_tax_checkpoint(lowerCAmelCase__		)
					lowercase						     =						convert_tax_to_pytorch(
					    lowerCAmelCase__			,      num_layers=config.num_layers			,      is_encoder_only=lowerCAmelCase__			,      scalable_attention=lowerCAmelCase__		)
					lowercase						     =						make_state_dict(lowerCAmelCase__			,      lowerCAmelCase__		)
					model.load_state_dict(lowerCAmelCase__			,      strict=lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__ = False			,      lowerCAmelCase__ = False			,      ):
					'''simple docstring'''
					lowercase						     =						MTaConfig.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:
										lowercase						     =						UMTaEncoderModel(lowerCAmelCase__		)
					else:
										lowercase						     =						UMTaForConditionalGeneration(lowerCAmelCase__		)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(lowerCAmelCase__			,      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__":
							lowercase__		:Union[str, 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
							)
							parser.add_argument(
							    "--scalable_attention",
							    action="store_true",
							    help="Whether the model uses scaled attention (umt5 model)",
							    default=False,
							)
							lowercase__		:Tuple			  =   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,
							)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:Dict			  =   {
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[str]			  =   [
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							lowercase__		:Tuple			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




