import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


a_							=      logging.get_logger(__name__)


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def __init__(				self:			Tuple				,				*__lowerCAmelCase:			Dict				,				**__lowerCAmelCase:			Tuple   ) -> None:




    '''simple docstring'''




    warnings.warn(
        "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
        " Please use DeformableDetrImageProcessor instead."				,				__lowerCAmelCase				,				)
    super().__init__(*__lowerCAmelCase				,				**__lowerCAmelCase   )




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      (						A_   : int						,	A_   : Optional[Any]						,	A_   : Tuple					)    ->  List[str]:
  return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




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




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Union[str, Any]						,	A_   : Optional[Any]						,	A_   : int=False					)    ->  List[Any]:
  if split_mlp_wi:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    __UpperCAmelCase						    =       (wi_a, wi_a)
  else:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

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




def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : int						,	A_   : Optional[Any]						,	A_   : Optional[Any]					)    ->  Tuple:
  return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def       __lowerCAmelCase      (						A_   : dict						,	*, A_   : int						,	A_   : bool						,	A_   : bool = False					)    ->  List[str]:
  __UpperCAmelCase						    =       traverse_util.flatten_dict(variables["target"]					)
  __UpperCAmelCase						    =       {"/".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
  __UpperCAmelCase						    =       "encoder/encoder/mlp/wi_0/kernel" in old
  print("Split MLP:"						,	A_					)

  __UpperCAmelCase						    =       collections.OrderedDict()

  # Shared embeddings.
  __UpperCAmelCase						    =       old["token_embedder/embedding"]

  # Encoder.
  for i in range(A_					):
    # Block i, layer 0 (Self Attention).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_attention_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"encoder"						,	"attention"					)
    __UpperCAmelCase						    =       layer_norm
    __UpperCAmelCase						    =       k.T
    __UpperCAmelCase						    =       o.T
    __UpperCAmelCase						    =       q.T
    __UpperCAmelCase						    =       v.T

    # Block i, layer 1 (MLP).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_mlp_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"encoder"						,	A_					)
    __UpperCAmelCase						    =       layer_norm
    if split_mlp_wi:
      __UpperCAmelCase						    =       wi[0].T
      __UpperCAmelCase						    =       wi[1].T
    else:
      __UpperCAmelCase						    =       wi.T
    __UpperCAmelCase						    =       wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      __UpperCAmelCase						    =       tax_relpos_bias_lookup(
          A_						,	A_						,	"encoder"					).T

  __UpperCAmelCase						    =       old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"encoder"					).T
    __UpperCAmelCase						    =       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).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_self_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"self_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 1 (Cross Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_cross_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"encoder_decoder_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 2 (MLP).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_mlp_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"decoder"						,	A_					)
      __UpperCAmelCase						    =       layer_norm
      if split_mlp_wi:
        __UpperCAmelCase						    =       wi[0].T
        __UpperCAmelCase						    =       wi[1].T
      else:
        __UpperCAmelCase						    =       wi.T
      __UpperCAmelCase						    =       wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        __UpperCAmelCase						    =       tax_relpos_bias_lookup(A_						,	A_						,	"decoder"					).T

    __UpperCAmelCase						    =       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:
      __UpperCAmelCase						    =       old["decoder/logits_dense/kernel"].T

  return new




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : bool					)    ->  List[Any]:
  __UpperCAmelCase						    =       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						    =       state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __UpperCAmelCase						    =       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						    =       state_dict["shared.weight"]

  return state_dict




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : int						,	A_   : List[Any]						,	A_   : List[Any]						,	A_   : List[Any]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       checkpoints.load_tax_checkpoint(A_					)
  __UpperCAmelCase						    =       convert_tax_to_pytorch(
      A_						,	num_layers=config.num_layers						,	is_encoder_only=A_						,	scalable_attention=A_					)
  __UpperCAmelCase						    =       make_state_dict(A_						,	A_					)
  model.load_state_dict(A_						,	strict=A_					)




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : List[Any]						,	A_   : List[str]						,	A_   : bool = False						,	A_   : bool = False						,	)    ->  Optional[int]:
  __UpperCAmelCase						    =       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:
    __UpperCAmelCase						    =       UMTaEncoderModel(A_					)
  else:
    __UpperCAmelCase						    =       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__":
       a_							=      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,
       )
       a_							=      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,
       )



