
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 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase="attention"    )	->						Tuple:
				UpperCamelCase_:				Tuple						=    params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
				UpperCamelCase_:				List[Any]						=    params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
				UpperCamelCase_:				int						=    params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
				UpperCamelCase_:				Union[str, Any]						=    params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
				return k, o, q, v







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=False    )	->						List[Any]:
				if split_mlp_wi:
								UpperCamelCase_:				Tuple						=    params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
								UpperCamelCase_:				Tuple						=    params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
								UpperCamelCase_:				List[str]						=    (wi_a, wi_a)
				else:
								UpperCamelCase_:				List[str]						=    params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

				UpperCamelCase_:				Optional[int]						=    params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
				return wi, wo







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Tuple:
				return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']







def 							A__       (    lowerCamelCase  ,     *, lowerCamelCase  ,     lowerCamelCase    )	->						str:
				UpperCamelCase_:				Optional[int]						=    traverse_util.flatten_dict(variables["""target"""]    )
				UpperCamelCase_:				Dict						=    {"""/""".join(SCREAMING_SNAKE_CASE_    ): v for k, v in old.items()}

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

				UpperCamelCase_:				List[Any]						=    collections.OrderedDict()

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

				# Encoder.
				for i in range(SCREAMING_SNAKE_CASE_    ):
								# Block i, layer 0 (Self Attention).
								UpperCamelCase_:				Any						=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """encoder"""  ,     """pre_attention_layer_norm"""    )
								UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[Any]						=    tax_attention_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """encoder"""  ,     """attention"""    )
								UpperCamelCase_:				List[Any]						=    layer_norm
								UpperCamelCase_:				List[str]						=    k.T
								UpperCamelCase_:				List[str]						=    o.T
								UpperCamelCase_:				Union[str, Any]						=    q.T
								UpperCamelCase_:				Optional[Any]						=    v.T

								# Block i, layer 1 (MLP).
								UpperCamelCase_:				Optional[int]						=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """encoder"""  ,     """pre_mlp_layer_norm"""    )
								UpperCamelCase_, UpperCamelCase_:				List[str]						=    tax_mlp_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """encoder"""  ,     SCREAMING_SNAKE_CASE_    )
								UpperCamelCase_:				List[Any]						=    layer_norm
								if split_mlp_wi:
												UpperCamelCase_:				int						=    wi[0].T
												UpperCamelCase_:				Optional[Any]						=    wi[1].T
								else:
												UpperCamelCase_:				Any						=    wi.T
								UpperCamelCase_:				Optional[int]						=    wo.T

				UpperCamelCase_:				List[str]						=    old[
				    """encoder/relpos_bias/rel_embedding"""
				].T
				UpperCamelCase_:				Optional[int]						=    old["""encoder/encoder_norm/scale"""]

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

												# Block i, layer 1 (Cross Attention).
												UpperCamelCase_:				Tuple						=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """decoder"""  ,     """pre_cross_attention_layer_norm"""    )
												UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    tax_attention_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """decoder"""  ,     """encoder_decoder_attention"""    )
												UpperCamelCase_:				str						=    layer_norm
												UpperCamelCase_:				List[str]						=    k.T
												UpperCamelCase_:				str						=    o.T
												UpperCamelCase_:				Tuple						=    q.T
												UpperCamelCase_:				Dict						=    v.T

												# Block i, layer 2 (MLP).
												UpperCamelCase_:				str						=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """decoder"""  ,     """pre_mlp_layer_norm"""    )
												UpperCamelCase_, UpperCamelCase_:				Optional[Any]						=    tax_mlp_lookup(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     """decoder"""  ,     SCREAMING_SNAKE_CASE_    )
												UpperCamelCase_:				List[str]						=    layer_norm
												if split_mlp_wi:
																UpperCamelCase_:				List[str]						=    wi[0].T
																UpperCamelCase_:				Tuple						=    wi[1].T
												else:
																UpperCamelCase_:				List[Any]						=    wi.T
												UpperCamelCase_:				List[str]						=    wo.T

								UpperCamelCase_:				Dict						=    old["""decoder/decoder_norm/scale"""]
								UpperCamelCase_:				Union[str, Any]						=    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_:				Any						=    old["""decoder/logits_dense/kernel"""].T

				return new







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				UpperCamelCase_:				Dict						=    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_:				Dict						=    state_dict["""shared.weight"""]

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												UpperCamelCase_:				List[Any]						=    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_:				Optional[Any]						=    state_dict["""shared.weight"""]

				return state_dict







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_:				Dict						=    checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_    )
				UpperCamelCase_:				List[Any]						=    convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_  ,     num_layers=config.num_layers  ,     is_encoder_only=SCREAMING_SNAKE_CASE_    )
				UpperCamelCase_:				Union[str, Any]						=    make_state_dict(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_    )
				model.load_state_dict(SCREAMING_SNAKE_CASE_  ,     strict=SCREAMING_SNAKE_CASE_    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase = False    )	->						Optional[Any]:
				UpperCamelCase_:				List[str]						=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE_    )
				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_:				Tuple						=    TaEncoderModel(SCREAMING_SNAKE_CASE_    )
				else:
								UpperCamelCase_:				Union[str, Any]						=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE_    )

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

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

				# Verify that we can load the checkpoint.
				model.from_pretrained(SCREAMING_SNAKE_CASE_    )
				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_    :			List[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'''
import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""mvp"""
					_UpperCamelCase							=		["""past_key_values"""]
					_UpperCamelCase							=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



					def __init__(	self     :						List[str]      ,       _lowercase     :						int=50_267      ,       _lowercase     :						str=1_024      ,       _lowercase     :						int=12      ,       _lowercase     :						int=4_096      ,       _lowercase     :						Tuple=16      ,       _lowercase     :						Optional[Any]=12      ,       _lowercase     :						Union[str, Any]=4_096      ,       _lowercase     :						List[Any]=16      ,       _lowercase     :						str=0.0      ,       _lowercase     :						List[str]=0.0      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Union[str, Any]=1_024      ,       _lowercase     :						str=0.1      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						Any=0.0      ,       _lowercase     :						List[str]=0.02      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						List[str]=False      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						Any=1      ,       _lowercase     :						Optional[Any]=0      ,       _lowercase     :						Dict=2      ,       _lowercase     :						int=True      ,       _lowercase     :						Any=2      ,       _lowercase     :						Tuple=2      ,       _lowercase     :						Optional[Any]=False      ,       _lowercase     :						List[str]=100      ,       _lowercase     :						List[str]=800      ,       **_lowercase     :						Dict      ,       )    ->							Dict:
								A_									= vocab_size
								A_									= max_position_embeddings
								A_									= d_model
								A_									= encoder_ffn_dim
								A_									= encoder_layers
								A_									= encoder_attention_heads
								A_									= decoder_ffn_dim
								A_									= decoder_layers
								A_									= decoder_attention_heads
								A_									= dropout
								A_									= attention_dropout
								A_									= activation_dropout
								A_									= activation_function
								A_									= init_std
								A_									= encoder_layerdrop
								A_									= decoder_layerdrop
								A_									= classifier_dropout
								A_									= use_cache
								A_									= encoder_layers
								A_									= scale_embedding  # scale factor will be sqrt(d_model) if True
								A_									= use_prompt
								A_									= prompt_length
								A_									= prompt_mid_dim

								super().__init__(
								    pad_token_id=_lowercase      ,       bos_token_id=_lowercase      ,       eos_token_id=_lowercase      ,       is_encoder_decoder=_lowercase      ,       decoder_start_token_id=_lowercase      ,       forced_eos_token_id=_lowercase      ,       **_lowercase      ,       )

								if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'      ,       _lowercase):
											A_									= self.bos_token_id
											warnings.warn(
											    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
											    'The config can simply be saved and uploaded again to be fixed.')




