



"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=None							) ->						Optional[int]:







  '''simple docstring'''


  assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
  lowercase_								= nn.Parameter(__lowerCAmelCase							)
  if bias is not None:
    assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
    lowercase_								= nn.Parameter(__lowerCAmelCase							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Optional[int]:







  '''simple docstring'''


  lowercase_								= np.asarray(weights[0]							)
  lowercase_								= np.asarray(weights[1]							)
  lowercase_								= np.asarray(weights[2]							)

  set_param(
      torch_layer.self_attention.query_key ,  torch.tensor(__lowerCAmelCase							).transpose(1 ,  2							).contiguous().view(-1 ,  __lowerCAmelCase							) ,  )
  set_param(
      torch_layer.self_attention.value ,  torch.tensor(__lowerCAmelCase							).transpose(1 ,  2							).contiguous().view(-1 ,  __lowerCAmelCase							) ,  )
  set_param(
      torch_layer.output.dense ,  torch.tensor(__lowerCAmelCase							).view(-1 ,  __lowerCAmelCase							).contiguous().transpose(0 ,  1							) ,  )


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  lowercase_								= np.asarray(weights[0]							)
  lowercase_								= np.asarray(weights[1]							)
  lowercase_								= np.asarray(weights[2]							)
  lowercase_								= np.asarray(weights[3]							)

  set_param(
      torch_layer.self_attention.query ,  torch.tensor(__lowerCAmelCase							).transpose(1 ,  2							).contiguous().view(-1 ,  __lowerCAmelCase							) ,  )
  set_param(
      torch_layer.self_attention.key ,  torch.tensor(__lowerCAmelCase							).transpose(1 ,  2							).contiguous().view(-1 ,  __lowerCAmelCase							) ,  )
  set_param(
      torch_layer.self_attention.value ,  torch.tensor(__lowerCAmelCase							).transpose(1 ,  2							).contiguous().view(-1 ,  __lowerCAmelCase							) ,  )
  set_param(
      torch_layer.output.dense ,  torch.tensor(__lowerCAmelCase							).view(-1 ,  __lowerCAmelCase							).contiguous().transpose(0 ,  1							) ,  )


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  lowercase_								= weights[0][0][0]
  lowercase_								= np.asarray(layer_norm_a[0]							)
  lowercase_								= np.asarray(layer_norm_a[1]							)
  set_param(
      torch_block.attention.layer_norm ,  torch.tensor(__lowerCAmelCase							) ,  torch.tensor(__lowerCAmelCase							) ,  )

  # lsh weights + output
  lowercase_								= weights[0][1]
  if len(__lowerCAmelCase							) < 4:
    set_layer_weights_in_torch_lsh(__lowerCAmelCase ,  torch_block.attention ,  __lowerCAmelCase							)
  else:
    set_layer_weights_in_torch_local(__lowerCAmelCase ,  torch_block.attention ,  __lowerCAmelCase							)

  # intermediate weighs
  lowercase_								= weights[2][0][1][2]

  # Chunked Feed Forward
  if len(__lowerCAmelCase							) == 4:
    lowercase_								= intermediate_weights[2]

  # layernorm 2
  lowercase_								= np.asarray(intermediate_weights[0][0]							)
  lowercase_								= np.asarray(intermediate_weights[0][1]							)
  set_param(
      torch_block.feed_forward.layer_norm ,  torch.tensor(__lowerCAmelCase							) ,  torch.tensor(__lowerCAmelCase							) ,  )

  # intermediate dense
  lowercase_								= np.asarray(intermediate_weights[1][0]							)
  lowercase_								= np.asarray(intermediate_weights[1][1]							)
  set_param(
      torch_block.feed_forward.dense.dense ,  torch.tensor(__lowerCAmelCase							).transpose(0 ,  1							).contiguous() ,  torch.tensor(__lowerCAmelCase							) ,  )

  # intermediate out
  lowercase_								= np.asarray(intermediate_weights[4][0]							)
  lowercase_								= np.asarray(intermediate_weights[4][1]							)
  set_param(
      torch_block.feed_forward.output.dense ,  torch.tensor(__lowerCAmelCase							).transpose(0 ,  1							).contiguous() ,  torch.tensor(__lowerCAmelCase							) ,  )


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  lowercase_								= torch_model.reformer

  # word embeds
  lowercase_								= np.asarray(weights[1]							)
  set_param(
      torch_model_reformer.embeddings.word_embeddings ,  torch.tensor(__lowerCAmelCase							) ,  )

  if isinstance(weights[3] ,  __lowerCAmelCase							):
    lowercase_								= torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights							)							):
      lowercase_								= np.asarray(weights[3][emb_idx][0]							)
      assert (
          position_embeddings.weights[emb_idx].shape == emb_weights.shape
      ), F'''{position_embeddings[emb_idx]} emb does not match'''
      lowercase_								= nn.Parameter(torch.tensor(__lowerCAmelCase							)							)

  lowercase_								= weights[5]
  assert len(torch_model_reformer.encoder.layers							) * 4 == len(
      __lowerCAmelCase							), "HF and trax model do not have the same number of layers"
  for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
    lowercase_								= trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
    set_block_weights_in_torch(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)

  # output layer norm
  lowercase_								= np.asarray(weights[7][0]							)
  lowercase_								= np.asarray(weights[7][1]							)
  set_param(
      torch_model_reformer.encoder.layer_norm ,  torch.tensor(__lowerCAmelCase							) ,  torch.tensor(__lowerCAmelCase							) ,  )

  # output embeddings
  lowercase_								= np.asarray(weights[9][0]							)
  lowercase_								= np.asarray(weights[9][1]							)
  set_param(
      torch_model.lm_head.decoder ,  torch.tensor(__lowerCAmelCase							).transpose(0 ,  1							).contiguous() ,  torch.tensor(__lowerCAmelCase							) ,  )


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= ReformerConfig.from_json_file(__lowerCAmelCase							)
  print(F'''Building PyTorch model from configuration: {config}'''							)
  lowercase_								= ReformerModelWithLMHead(__lowerCAmelCase							)

  with open(__lowerCAmelCase ,  """rb"""							) as f:
    lowercase_								= pickle.load(__lowerCAmelCase							)["""weights"""]

  set_model_weights_in_torch(__lowerCAmelCase ,  __lowerCAmelCase ,  config.hidden_size							)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
  torch.save(model.state_dict() ,  __lowerCAmelCase							)


if __name__ == "__main__":
       UpperCAmelCase						:		Dict			= argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained Reformer model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       UpperCAmelCase						:		Optional[Any]			= parser.parse_args()
       convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






"""simple docstring"""
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

UpperCAmelCase						:		Optional[Any]			= logging.get_logger(__name__)


@add_end_docstrings(__UpperCAmelCase   )
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				Optional[int] ,  **lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)

      if self.framework == "tf":
        raise ValueError(F'''The {self.__class__} is only available in PyTorch.''')

      requires_backends(self ,  """vision""")
      self.check_model_type(lowerCAmelCase_)

    def __call__(				self			:				Dict ,  lowerCAmelCase_			:				Union[str, "Image.Image", List[Dict[str, Any]]] ,  lowerCAmelCase_			:				Union[str, List[str]] = None ,  **lowerCAmelCase_			:				Dict ,  ):







      """simple docstring"""







      if "text_queries" in kwargs:
        lowercase_								= kwargs.pop("""text_queries""")

      if isinstance(lowerCAmelCase_ ,  (str, Image.Image)):
        lowercase_								= {"""image""": image, """candidate_labels""": candidate_labels}
      else:
        lowercase_								= image
      lowercase_								= super().__call__(lowerCAmelCase_ ,  **lowerCAmelCase_)
      return results

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  **lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= {}
      if "threshold" in kwargs:
        lowercase_								= kwargs["""threshold"""]
      if "top_k" in kwargs:
        lowercase_								= kwargs["""top_k"""]
      return {}, {}, postprocess_params

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= load_image(inputs["""image"""])
      lowercase_								= inputs["""candidate_labels"""]
      if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_):
        lowercase_								= candidate_labels.split(""",""")

      lowercase_								= torch.tensor([[image.height, image.width]] ,  dtype=torch.intaa)
      for i, candidate_label in enumerate(lowerCAmelCase_):
        lowercase_								= self.tokenizer(lowerCAmelCase_ ,  return_tensors=self.framework)
        lowercase_								= self.image_processor(lowerCAmelCase_ ,  return_tensors=self.framework)
        yield {
            "is_last": i == len(lowerCAmelCase_) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= model_inputs.pop("""target_size""")
      lowercase_								= model_inputs.pop("""candidate_label""")
      lowercase_								= model_inputs.pop("""is_last""")

      lowercase_								= self.model(**lowerCAmelCase_)

      lowercase_								= {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
      return model_outputs

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Tuple=0.1 ,  lowerCAmelCase_			:				List[str]=None):







      """simple docstring"""







      lowercase_								= []
      for model_output in model_outputs:
        lowercase_								= model_output["""candidate_label"""]
        lowercase_								= BaseModelOutput(lowerCAmelCase_)
        lowercase_								= self.image_processor.post_process_object_detection(
            outputs=lowerCAmelCase_ ,  threshold=lowerCAmelCase_ ,  target_sizes=model_output["""target_size"""])[0]

        for index in outputs["scores"].nonzero():
          lowercase_								= outputs["""scores"""][index].item()
          lowercase_								= self._get_bounding_box(outputs["""boxes"""][index][0])

          lowercase_								= {"""score""": score, """label""": label, """box""": box}
          results.append(lowerCAmelCase_)

      lowercase_								= sorted(lowerCAmelCase_ ,  key=lambda lowerCAmelCase_: x["score"] ,  reverse=lowerCAmelCase_)
      if top_k:
        lowercase_								= results[:top_k]

      return results




    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				"torch.Tensor"):







      """simple docstring"""







      if self.framework != "pt":
        raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""")
      lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= box.int().tolist()
      lowercase_								= {
          """xmin""": xmin,
          """ymin""": ymin,
          """xmax""": xmax,
          """ymax""": ymax,
      }
      return bbox

