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,  __A,  __A							)					->    Optional[int]:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A="attention"							)					->    int:







      '''simple docstring'''


      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,  __A,  __A,  __A=False							)					->    Any:







      '''simple docstring'''


      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,  __A,  __A,  __A							)					->    Union[str, Any]:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  *, __A,  __A,  __A = False							)					->    Optional[int]:







      '''simple docstring'''


      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,  __A							)					->    str:







      '''simple docstring'''


      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,  __A,  __A,  __A,  __A							)					->    Tuple:







      '''simple docstring'''


      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,  __A,  __A,  __A = False,  __A = False,  )					->    int:







      '''simple docstring'''


      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__":
      UpperCamelCase__    							= 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,
      )
      UpperCamelCase__    							= 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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



