





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   a_   (			lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Optional[Any]     ):
   return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]

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

def   a_   (			lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      str=False     ):
   if split_mlp_wi:
      __lowerCAmelCase							     =     params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
      __lowerCAmelCase							     =     params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
      __lowerCAmelCase							     =     (wi_a, wi_a)
   else:
      __lowerCAmelCase							     =     params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

   __lowerCAmelCase							     =     params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
   return wi, wo

def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      int     ):
   return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]

def   a_   (			lowerCAmelCase_				:      dict,     *, lowerCAmelCase_				:      int,     lowerCAmelCase_				:      bool,     lowerCAmelCase_				:      bool = False     ):
   __lowerCAmelCase							     =     traverse_util.flatten_dict(variables['target']     )
   __lowerCAmelCase							     =     {'/'.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
   __lowerCAmelCase							     =     'encoder/encoder/mlp/wi_0/kernel' in old
   print('Split MLP:',     lowerCAmelCase_     )

   __lowerCAmelCase							     =     collections.OrderedDict()

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

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

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

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

   if not scalable_attention:
      __lowerCAmelCase							     =     tax_relpos_bias_lookup(
          lowerCAmelCase_,     0,     'encoder'     ).T
      __lowerCAmelCase							     =     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).
         __lowerCAmelCase							     =     tax_layer_norm_lookup(lowerCAmelCase_,     lowerCAmelCase_,     'decoder',     'pre_self_attention_layer_norm'     )
         __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     tax_attention_lookup(lowerCAmelCase_,     lowerCAmelCase_,     'decoder',     'self_attention'     )
         __lowerCAmelCase							     =     layer_norm
         __lowerCAmelCase							     =     k.T
         __lowerCAmelCase							     =     o.T
         __lowerCAmelCase							     =     q.T
         __lowerCAmelCase							     =     v.T

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

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

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

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

   return new

def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      bool     ):
   __lowerCAmelCase							     =     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:
      __lowerCAmelCase							     =     state_dict['shared.weight']

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

   return state_dict

def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      List[Any]     ):
   __lowerCAmelCase							     =     checkpoints.load_tax_checkpoint(lowerCAmelCase_     )
   __lowerCAmelCase							     =     convert_tax_to_pytorch(
       lowerCAmelCase_,     num_layers=config.num_layers,     is_encoder_only=lowerCAmelCase_,     scalable_attention=lowerCAmelCase_     )
   __lowerCAmelCase							     =     make_state_dict(lowerCAmelCase_,     lowerCAmelCase_     )
   model.load_state_dict(lowerCAmelCase_,     strict=lowerCAmelCase_     )

def   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      bool = False,     lowerCAmelCase_				:      bool = False,     ):
   __lowerCAmelCase							     =     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:
      __lowerCAmelCase							     =     UMTaEncoderModel(lowerCAmelCase_     )
   else:
      __lowerCAmelCase							     =     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__":
     _snake_case  :   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,
     )
     _snake_case  :   List[str]						   =  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 dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		int = 1_6 ,  lowerCAmelCase_			:		int = 8_8 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 3_2 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		str = "geglu" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  )							->						Dict:
     super().__init__()
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     attention_head_dim
     __lowerCAmelCase							     =     num_attention_heads * attention_head_dim

     __lowerCAmelCase							     =     in_channels

     __lowerCAmelCase							     =     torch.nn.GroupNorm(num_groups=lowerCAmelCase_ ,  num_channels=lowerCAmelCase_ ,  eps=1e-6 ,  affine=lowerCAmelCase_					)
     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 3. Define transformers blocks
     __lowerCAmelCase							     =     nn.ModuleList(
         [
             BasicTransformerBlock(
                 lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  dropout=lowerCAmelCase_ ,  cross_attention_dim=lowerCAmelCase_ ,  activation_fn=lowerCAmelCase_ ,  attention_bias=lowerCAmelCase_ ,  double_self_attention=lowerCAmelCase_ ,  norm_elementwise_affine=lowerCAmelCase_ ,  )
             for d in range(lowerCAmelCase_					)
         ]					)

     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[int]=1 ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		bool = True ,  )							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     hidden_states.shape
     __lowerCAmelCase							     =     batch_frames // num_frames

     __lowerCAmelCase							     =     hidden_states

     __lowerCAmelCase							     =     hidden_states[None, :].reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  2 ,  1 ,  3 ,  4					)

     __lowerCAmelCase							     =     self.norm(lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  3 ,  4 ,  2 ,  1					).reshape(batch_size * height * width ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.proj_in(lowerCAmelCase_					)

     # 2. Blocks
     for block in self.transformer_blocks:
        __lowerCAmelCase							     =     block(
            lowerCAmelCase_ ,  encoder_hidden_states=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  cross_attention_kwargs=lowerCAmelCase_ ,  class_labels=lowerCAmelCase_ ,  )

     # 3. Output
     __lowerCAmelCase							     =     self.proj_out(lowerCAmelCase_					)
     __lowerCAmelCase							     =     (
         hidden_states[None, None, :]
         .reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
         .permute(0 ,  3 ,  4 ,  1 ,  2					)
         .contiguous()
     )
     __lowerCAmelCase							     =     hidden_states.reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=lowerCAmelCase_					)

