



"""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_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				int						)						->   Optional[int]:

  """simple docstring"""




  return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				List[str]="attention"						)						->   Any:

  """simple docstring"""




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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Union[str, Any]=False						)						->   Any:

  """simple docstring"""




  if split_mlp_wi:
    lowerCAmelCase_   :				str								= params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
    lowerCAmelCase_   :				Union[str, Any]								= params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
    lowerCAmelCase_   :				Tuple								= (wi_a, wi_a)
  else:
    lowerCAmelCase_   :				Any								= params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

  lowerCAmelCase_   :				List[str]								= params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
  return wi, wo

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[str]						)						->   Any:

  """simple docstring"""




  return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				dict    ,   *, lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				bool    ,   lowerCAmelCase__      :				bool = False						)						->   List[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= traverse_util.flatten_dict(variables['target']						)
  lowerCAmelCase_   :				Dict								= {'/'.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_   :				Optional[Any]								= 'encoder/encoder/mlp/wi_0/kernel' in old
  print('Split MLP:'    ,   lowerCAmelCase__						)

  lowerCAmelCase_   :				Optional[int]								= collections.OrderedDict()

  # Shared embeddings.
  lowerCAmelCase_   :				Any								= old['token_embedder/embedding']

  # Encoder.
  for i in range(lowerCAmelCase__						):
    # Block i, layer 0 (Self Attention).
    lowerCAmelCase_   :				List[Any]								= tax_layer_norm_lookup(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'encoder'    ,   'pre_attention_layer_norm'						)
    lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				List[str]								= tax_attention_lookup(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'encoder'    ,   'attention'						)
    lowerCAmelCase_   :				Union[str, Any]								= layer_norm
    lowerCAmelCase_   :				Any								= k.T
    lowerCAmelCase_   :				List[str]								= o.T
    lowerCAmelCase_   :				Any								= q.T
    lowerCAmelCase_   :				str								= v.T

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

  lowerCAmelCase_   :				List[str]								= old['encoder/encoder_norm/scale']

  if not scalable_attention:
    lowerCAmelCase_   :				List[str]								= tax_relpos_bias_lookup(
        lowerCAmelCase__    ,   0    ,   'encoder'						).T
    lowerCAmelCase_   :				Tuple								= 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_   :				List[Any]								= tax_layer_norm_lookup(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'decoder'    ,   'pre_self_attention_layer_norm'						)
      lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Dict								= tax_attention_lookup(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'decoder'    ,   'self_attention'						)
      lowerCAmelCase_   :				int								= layer_norm
      lowerCAmelCase_   :				Tuple								= k.T
      lowerCAmelCase_   :				Tuple								= o.T
      lowerCAmelCase_   :				Union[str, Any]								= q.T
      lowerCAmelCase_   :				List[Any]								= v.T

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

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

      if scalable_attention:
        # convert the rel_embedding of each layer
        lowerCAmelCase_   :				Optional[int]								= tax_relpos_bias_lookup(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'decoder'						).T

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

  return new

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				bool						)						->   Any:

  """simple docstring"""




  lowerCAmelCase_   :				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:
    lowerCAmelCase_   :				List[str]								= state_dict['shared.weight']

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

  return state_dict

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[Any]						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= checkpoints.load_tax_checkpoint(lowerCAmelCase__						)
  lowerCAmelCase_   :				Any								= convert_tax_to_pytorch(
      lowerCAmelCase__    ,   num_layers=config.num_layers    ,   is_encoder_only=lowerCAmelCase__    ,   scalable_attention=lowerCAmelCase__						)
  lowerCAmelCase_   :				Union[str, Any]								= make_state_dict(lowerCAmelCase__    ,   lowerCAmelCase__						)
  model.load_state_dict(lowerCAmelCase__    ,   strict=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				bool = False    ,   lowerCAmelCase__      :				bool = False    ,   )						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				int								= 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_   :				List[Any]								= UMTaEncoderModel(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				str								= 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__		:				int          =	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 inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= inspect.getfile(accelerate.test_utils       )
        lowerCAmelCase_   :				Dict								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
        lowerCAmelCase_   :				int								= os.path.sep.join(
            mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
        lowerCAmelCase_   :				Union[str, Any]								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				Optional[int]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				int								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
        print(F"Command: {cmd}"       )
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )





      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
        lowerCAmelCase_   :				Any								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
        with patch_environment(omp_num_threads=1    ,			cuda_visible_devices='0,1'       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )


if __name__ == "__main__":
     lowercase__		:				Dict          =	Accelerator()
     lowercase__		:				List[Any]          =	(accelerator.state.process_index + 2, 1_0)
     lowercase__		:				Any          =	torch.randint(0, 1_0, shape).to(accelerator.device)

     lowercase__		:				List[Any]          =	""""""

     lowercase__		:				Union[str, Any]          =	accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowercase__		:				Optional[Any]          =	accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowercase__		:				Any          =	accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)





