



"""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 						__lowerCAmelCase    (		__UpperCamelCase   :      str       ,			__UpperCamelCase   :      Dict       ,			__UpperCamelCase   :      List[Any]     ):


						'''simple docstring'''






						return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      str       ,			__UpperCamelCase   :      Optional[Any]       ,			__UpperCamelCase   :      List[Any]="attention"     ):


						'''simple docstring'''






						snake_case_ :			str            =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]     )
						snake_case_ :			Optional[Any]            =      k_tmp.reshape(k_tmp.shape[0]       ,			k_tmp.shape[1] * k_tmp.shape[2]     )
						snake_case_ :			Union[str, Any]            =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]     )
						snake_case_ :			Optional[int]            =      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]       ,			o_tmp.shape[2]     )
						snake_case_ :			Union[str, Any]            =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]     )
						snake_case_ :			Optional[Any]            =      q_tmp.reshape(q_tmp.shape[0]       ,			q_tmp.shape[1] * q_tmp.shape[2]     )
						snake_case_ :			int            =      np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]     )
						snake_case_ :			List[str]            =      v_tmp.reshape(v_tmp.shape[0]       ,			v_tmp.shape[1] * v_tmp.shape[2]     )
						return k, o, q, v
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      Optional[Any]       ,			__UpperCamelCase   :      Dict       ,			__UpperCamelCase   :      Union[str, Any]=False     ):


						'''simple docstring'''






						if split_mlp_wi:
												snake_case_ :			str            =      params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
												snake_case_ :			Any            =      params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
												snake_case_ :			Union[str, Any]            =      (wi_a, wi_a)
						else:
												snake_case_ :			Dict            =      params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

						snake_case_ :			List[str]            =      params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
						return wi, wo
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      Dict       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      str     ):


						'''simple docstring'''






						return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]
def 						__lowerCAmelCase    (		__UpperCamelCase   :      dict       ,			*, __UpperCamelCase   :      int       ,			__UpperCamelCase   :      bool       ,			__UpperCamelCase   :      bool = False     ):


						'''simple docstring'''






						snake_case_ :			str            =      traverse_util.flatten_dict(variables["""target"""]     )
						snake_case_ :			str            =      {"""/""".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
						snake_case_ :			Union[str, Any]            =      """encoder/encoder/mlp/wi_0/kernel""" in old
						print("""Split MLP:"""       ,			__UpperCamelCase     )

						snake_case_ :			List[str]            =      collections.OrderedDict()

						# Shared embeddings.
						snake_case_ :			int            =      old["""token_embedder/embedding"""]

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

												# Block i, layer 1 (MLP).
												snake_case_ :			Optional[int]            =      tax_layer_norm_lookup(__UpperCamelCase       ,			__UpperCamelCase       ,			"""encoder"""       ,			"""pre_mlp_layer_norm"""     )
												snake_case_      ,		snake_case_ :			int            =      tax_mlp_lookup(__UpperCamelCase       ,			__UpperCamelCase       ,			"""encoder"""       ,			__UpperCamelCase     )
												snake_case_ :			Dict            =      layer_norm
												if split_mlp_wi:
																		snake_case_ :			str            =      wi[0].T
																		snake_case_ :			int            =      wi[1].T
												else:
																		snake_case_ :			List[str]            =      wi.T
												snake_case_ :			str            =      wo.T
												if scalable_attention:
																		# convert the rel_embedding of each layer
																		snake_case_ :			Optional[int]            =      tax_relpos_bias_lookup(
																		    __UpperCamelCase       ,			__UpperCamelCase       ,			"""encoder"""     ).T

						snake_case_ :			Dict            =      old["""encoder/encoder_norm/scale"""]

						if not scalable_attention:
												snake_case_ :			Optional[int]            =      tax_relpos_bias_lookup(
												    __UpperCamelCase       ,			0       ,			"""encoder"""     ).T
												snake_case_ :			str            =      tax_relpos_bias_lookup(
												    __UpperCamelCase       ,			0       ,			"""decoder"""     ).T

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

																		# Block i, layer 1 (Cross Attention).
																		snake_case_ :			Union[str, Any]            =      tax_layer_norm_lookup(__UpperCamelCase       ,			__UpperCamelCase       ,			"""decoder"""       ,			"""pre_cross_attention_layer_norm"""     )
																		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_ :			Optional[int]            =      tax_attention_lookup(__UpperCamelCase       ,			__UpperCamelCase       ,			"""decoder"""       ,			"""encoder_decoder_attention"""     )
																		snake_case_ :			Dict            =      layer_norm
																		snake_case_ :			Tuple            =      k.T
																		snake_case_ :			int            =      o.T
																		snake_case_ :			List[Any]            =      q.T
																		snake_case_ :			Optional[Any]            =      v.T

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

																		if scalable_attention:
																								# convert the rel_embedding of each layer
																								snake_case_ :			Optional[Any]            =      tax_relpos_bias_lookup(__UpperCamelCase       ,			__UpperCamelCase       ,			"""decoder"""     ).T

												snake_case_ :			List[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:
																		snake_case_ :			Optional[Any]            =      old["""decoder/logits_dense/kernel"""].T

						return new
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Union[str, Any]       ,			__UpperCamelCase   :      bool     ):


						'''simple docstring'''






						snake_case_ :			int            =      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:
												snake_case_ :			Any            =      state_dict["""shared.weight"""]

						if not is_encoder_only:
												if "decoder.embed_tokens.weight" not in state_dict:
																		snake_case_ :			Tuple            =      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."""     )
																		snake_case_ :			Union[str, Any]            =      state_dict["""shared.weight"""]

						return state_dict
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      str       ,			__UpperCamelCase   :      List[str]       ,			__UpperCamelCase   :      Tuple       ,			__UpperCamelCase   :      Any     ):


						'''simple docstring'''






						snake_case_ :			Optional[Any]            =      checkpoints.load_tax_checkpoint(__UpperCamelCase     )
						snake_case_ :			str            =      convert_tax_to_pytorch(
						    __UpperCamelCase       ,			num_layers=config.num_layers       ,			is_encoder_only=__UpperCamelCase       ,			scalable_attention=__UpperCamelCase     )
						snake_case_ :			Optional[Any]            =      make_state_dict(__UpperCamelCase       ,			__UpperCamelCase     )
						model.load_state_dict(__UpperCamelCase       ,			strict=__UpperCamelCase     )
def 						__lowerCAmelCase    (		__UpperCamelCase   :      str       ,			__UpperCamelCase   :      Union[str, Any]       ,			__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      bool = False       ,			__UpperCamelCase   :      bool = False       ,			):


						'''simple docstring'''






						snake_case_ :			int            =      MTaConfig.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:
												snake_case_ :			Optional[Any]            =      UMTaEncoderModel(__UpperCamelCase     )
						else:
												snake_case_ :			int            =      UMTaForConditionalGeneration(__UpperCamelCase     )

						# Load weights from tf checkpoint
						load_tax_weights_in_ta(__UpperCamelCase       ,			__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
					)
					parser.add_argument(
					    '''--scalable_attention''',
					    action='''store_true''',
					    help='''Whether the model uses scaled attention (umt5 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,
					    args.scalable_attention,
					)








'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							42






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :Tuple		,			lowercase_ :int = 3		,			lowercase_ :int = 3		,			lowercase_ :Tuple[str] = ("DownEncoderBlock2D",)		,			lowercase_ :Tuple[str] = ("UpDecoderBlock2D",)		,			lowercase_ :Tuple[int] = (64,)		,			lowercase_ :int = 1		,			lowercase_ :str = "silu"		,			lowercase_ :int = 3		,			lowercase_ :int = 32		,			lowercase_ :int = 2_56		,			lowercase_ :int = 32		,			lowercase_ :Optional[int] = None		,			lowercase_ :float = 0.1_8_2_1_5		,			lowercase_ :str = "group"		,			)->				Optional[int]:
     super().__init__()

     # pass init params to Encoder
     A__							=     Encoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			down_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			double_z=lowercase_		,			)

     A__							=     vq_embed_dim if vq_embed_dim is not None else latent_channels

     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)
     A__							=     VectorQuantizer(lowercase_		,			lowercase_		,			beta=0.2_5		,			remap=lowercase_		,			sane_index_shape=lowercase_				)
     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)

     # pass init params to Decoder
     A__							=     Decoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			up_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			norm_type=lowercase_		,			)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				VQEncoderOutput:
     A__							=     self.encoder(lowercase_				)
     A__							=     self.quant_conv(lowercase_				)

     if not return_dict:
         return (h,)

     return VQEncoderOutput(latents=lowercase_				)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = False		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     # also go through quantization layer
     if not force_not_quantize:
         A__,      A__,      A__							=     self.quantize(lowercase_				)
     else:
         A__							=     h
     A__							=     self.post_quant_conv(lowercase_				)
     A__							=     self.decoder(lowercase_		,			quant if self.config.norm_type == "spatial" else None				)

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     A__							=     sample
     A__							=     self.encode(lowercase_				).latents
     A__							=     self.decode(lowercase_				).sample

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



