






'''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 		_UpperCAmelCase (							__A							:   Tuple						,		__A							:   Optional[Any]						,		__A							:   List[str]      ):
				return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]





def 		_UpperCAmelCase (							__A							:   Dict						,		__A							:   Union[str, Any]						,		__A							:   Dict						,		__A							:   Optional[int]="attention"      ):
				a_		:  Tuple											=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]      )
				a_		:  Tuple											=      k_tmp.reshape(k_tmp.shape[0]						,		k_tmp.shape[1] * k_tmp.shape[2]      )
				a_		:  str											=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]      )
				a_		:  Dict											=      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,		o_tmp.shape[2]      )
				a_		:  Tuple											=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]      )
				a_		:  Any											=      q_tmp.reshape(q_tmp.shape[0]						,		q_tmp.shape[1] * q_tmp.shape[2]      )
				a_		:  Dict											=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]      )
				a_		:  List[str]											=      v_tmp.reshape(v_tmp.shape[0]						,		v_tmp.shape[1] * v_tmp.shape[2]      )
				return k, o, q, v





def 		_UpperCAmelCase (							__A							:   Dict						,		__A							:   str						,		__A							:   Optional[int]						,		__A							:   Dict=False      ):
				if split_mlp_wi:
								a_		:  List[str]											=      params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
								a_		:  List[Any]											=      params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
								a_		:  Any											=      (wi_a, wi_a)
				else:
								a_		:  str											=      params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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





def 		_UpperCAmelCase (							__A							:   str						,		__A							:   int						,		__A							:   Any						,		__A							:   Dict      ):
				return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]





def 		_UpperCAmelCase (							__A							:   List[str]						,		*, __A							:   List[str]						,		__A							:   Any						,		__A							:   List[Any] = False      ):
				a_		:  List[Any]											=      traverse_util.flatten_dict(variables['''target''']      )
				a_		:  Optional[int]											=      {"""/""".join(__A      ): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				a_		:  Optional[int]											=      """encoder/encoder/mlp/wi_0/kernel""" in old
				print('''Split MLP:'''						,		__A      )

				a_		:  Tuple											=      collections.OrderedDict()

				# Shared embeddings.
				a_		:  List[Any]											=      old["""token_embedder/embedding"""]

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

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

				a_		:  List[Any]											=      old["""encoder/encoder_norm/scale"""]

				if not scalable_attention:
								a_		:  List[Any]											=      tax_relpos_bias_lookup(
								    __A						,		0						,		'''encoder'''      ).T
								a_		:  Any											=      tax_relpos_bias_lookup(
								    __A						,		0						,		'''decoder'''      ).T

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

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

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

												if scalable_attention:
																# convert the rel_embedding of each layer
																a_		:  Any											=      tax_relpos_bias_lookup(__A						,		__A						,		'''decoder'''      ).T

								a_		:  str											=      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:
												a_		:  Dict											=      old["""decoder/logits_dense/kernel"""].T

				return new





def 		_UpperCAmelCase (							__A							:   List[str]						,		__A							:   str      ):
				a_		:  List[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:
								a_		:  Union[str, Any]											=      state_dict["""shared.weight"""]

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												a_		:  int											=      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_		:  Union[str, Any]											=      state_dict["""shared.weight"""]

				return state_dict





def 		_UpperCAmelCase (							__A							:   List[Any]						,		__A							:   str						,		__A							:   Union[str, Any]						,		__A							:   Optional[Any]						,		__A							:   str      ):
				a_		:  str											=      checkpoints.load_tax_checkpoint(__A      )
				a_		:  Dict											=      convert_tax_to_pytorch(
				    __A						,		num_layers=config.num_layers						,		is_encoder_only=__A						,		scalable_attention=__A      )
				a_		:  Union[str, Any]											=      make_state_dict(__A						,		__A      )
				model.load_state_dict(__A						,		strict=__A      )





def 		_UpperCAmelCase (							__A							:   Tuple						,		__A							:   Any						,		__A							:   str						,		__A							:   Optional[Any] = False						,		__A							:   Dict = False						,		):
				a_		:  Dict											=      MTaConfig.from_json_file(__A      )
				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_		:  Dict											=      UMTaEncoderModel(__A      )
				else:
								a_		:  Dict											=      UMTaForConditionalGeneration(__A      )

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

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

				# Verify that we can load the checkpoint.
				model.from_pretrained(__A      )
				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'''







import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__lowerCAmelCase       		=      logging.get_logger(__name__)
logging.set_verbosity_info()





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):
				if "xprophetnet" in prophetnet_checkpoint_path:
								a_		:  Tuple											=      XLMProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Optional[Any]											=      XLMProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )
				else:
								a_		:  List[Any]											=      ProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Any											=      ProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )

				a_		:  str											=      ['''key_proj''', '''value_proj''', '''query_proj''']

				a_		:  Tuple											=      {
				    '''self_attn''': '''ngram_self_attn''',
				    '''cross_attn''': '''encoder_attn''',
				    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
				    '''feed_forward_layer_norm''': '''final_layer_norm''',
				    '''feed_forward''': '''''',
				    '''intermediate''': '''fc1''',
				    '''output''': '''fc2''',
				    '''key_proj''': '''k_proj''',
				    '''query_proj''': '''q_proj''',
				    '''value_proj''': '''v_proj''',
				    '''word_embeddings''': '''embed_tokens''',
				    '''embeddings_layer_norm''': '''emb_layer_norm''',
				    '''relative_pos_embeddings''': '''relative_linear''',
				    '''ngram_embeddings''': '''ngram_input_embed''',
				    '''position_embeddings''': '''embed_positions''',
				}

				for key in loading_info["missing_keys"]:
								a_		:  List[str]											=      key.split('''.'''      )

								if attributes[0] == "lm_head":
												a_		:  List[str]											=      prophet
												a_		:  Dict											=      prophet_old
								else:
												a_		:  str											=      prophet.prophetnet
												a_		:  int											=      prophet_old.model

								a_		:  str											=      False
								for attribute in attributes:
												if attribute in mapping:
																a_		:  Dict											=      mapping[attribute]
																if not hasattr(__A						,		__A      ) and len(__A      ) > 0:
																				a_		:  List[str]											=      attribute
												elif hasattr(__A						,		__A      ):
																a_		:  Union[str, Any]											=      attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																a_		:  Tuple											=      old_model.weight
																logger.info(f'{attribute} is initialized.'      )
																a_		:  Union[str, Any]											=      True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																a_		:  Union[str, Any]											=      old_model.bias
																logger.info(f'{attribute} is initialized'      )
																a_		:  Dict											=      True
																break
												elif attribute in special_keys and hasattr(__A						,		'''in_proj_weight'''      ):
																a_		:  Tuple											=      old_model.in_proj_weight.shape[0] // 3
																a_		:  Any											=      getattr(__A						,		__A      )
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				a_		:  Union[str, Any]											=      nn.Parameter(old_model.in_proj_weight[:embed_dim, :]      )
																				a_		:  Optional[Any]											=      nn.Parameter(old_model.in_proj_bias[:embed_dim]      )

																elif attribute == "key_proj":
																				a_		:  List[Any]											=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
																				a_		:  Optional[int]											=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
																elif attribute == "value_proj":
																				a_		:  Tuple											=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
																				a_		:  Any											=      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
																a_		:  Dict											=      True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
																a_		:  Union[str, Any]											=      nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
																a_		:  Optional[Any]											=      True
																break

												if attribute.isdigit():
																a_		:  Union[str, Any]											=      model[int(__A      )]
																a_		:  str											=      old_model[int(__A      )]
												else:
																a_		:  Tuple											=      getattr(__A						,		__A      )

																if old_attribute == "":
																				a_		:  List[str]											=      old_model
																else:
																				if not hasattr(__A						,		__A      ):
																								raise ValueError(f'{old_model} does not have {old_attribute}'      )
																				a_		:  Optional[Any]											=      getattr(__A						,		__A      )

								if not is_key_init:
												raise ValueError(f'{key} was not correctly initialized!'      )

				print(f'Saving model to {pytorch_dump_folder_path}'      )
				prophet.save_pretrained(__A      )


if __name__ == "__main__":
		__lowerCAmelCase       		=      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__lowerCAmelCase       		=      parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




