import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[Any]:
  a__:					Optional[int]      							=     [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'encoder.embed_positions._float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(lowerCamelCase__		,					lowerCamelCase__       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Any:
  a__:					Any      							=     list(s_dict.keys()       )
  for key in keys:
    if "transformer_layers" in key:
      a__:					List[Any]      							=     s_dict.pop(lowerCamelCase__       )
    elif "subsample" in key:
      a__:					Tuple      							=     s_dict.pop(lowerCamelCase__       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__							,   a__:					Optional[int]      							=     emb.weight.shape
  a__:					List[Any]      							=     nn.Linear(lowerCamelCase__		,					lowerCamelCase__		,					bias=lowerCamelCase__       )
  a__:					Dict      							=     emb.weight.data
  return lin_layer







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[Any]:
  a__:					List[str]      							=     torch.load(lowerCamelCase__		,					map_location='cpu'       )
  a__:					List[Any]      							=     mam_aaa['args']
  a__:					str      							=     mam_aaa['model']
  a__:					List[Any]      							=     state_dict['decoder.output_projection.weight']

  remove_ignore_keys_(lowerCamelCase__       )
  rename_keys(lowerCamelCase__       )

  a__:					List[Any]      							=     state_dict['decoder.embed_tokens.weight'].shape[0]

  a__:					Union[str, Any]      							=     args.share_decoder_input_output_embed

  a__:					Optional[Any]      							=     [int(lowerCamelCase__       ) for i in args.conv_kernel_sizes.split(','       )]
  a__:					str      							=     SpeechaTextConfig(
      vocab_size=lowerCamelCase__		,					max_source_positions=args.max_source_positions		,					max_target_positions=args.max_target_positions		,					encoder_layers=args.encoder_layers		,					decoder_layers=args.decoder_layers		,					encoder_attention_heads=args.encoder_attention_heads		,					decoder_attention_heads=args.decoder_attention_heads		,					encoder_ffn_dim=args.encoder_ffn_embed_dim		,					decoder_ffn_dim=args.decoder_ffn_embed_dim		,					d_model=args.encoder_embed_dim		,					dropout=args.dropout		,					attention_dropout=args.attention_dropout		,					activation_dropout=args.activation_dropout		,					activation_function='relu'		,					num_conv_layers=len(lowerCamelCase__       )		,					conv_channels=args.conv_channels		,					conv_kernel_sizes=lowerCamelCase__		,					input_feat_per_channel=args.input_feat_per_channel		,					input_channels=args.input_channels		,					tie_word_embeddings=lowerCamelCase__		,					num_beams=5		,					max_length=200		,					use_cache=lowerCamelCase__		,					decoder_start_token_id=2		,					early_stopping=lowerCamelCase__		,					)

  a__:					Optional[int]      							=     SpeechaTextForConditionalGeneration(lowerCamelCase__       )
  a__							,   a__:					Optional[int]      							=     model.model.load_state_dict(lowerCamelCase__		,					strict=lowerCamelCase__       )
  if len(lowerCamelCase__       ) > 0 and not set(lowerCamelCase__       ) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
        F' but all the following weights are missing {missing}'       )

  if tie_embeds:
    a__:					Any      							=     make_linear_from_emb(model.model.decoder.embed_tokens       )
  else:
    a__:					List[str]      							=     lm_head_weights

  model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 lowercase__ 	=     parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_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, Pipeline


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     Dict[str, Any]
lowercase__ 	=     List[Prediction]




@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      *lowercase   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





        super().__init__(*lowercase   ,      **lowercase)

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

        requires_backends(self   ,      'vision')
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        a__:					Optional[Any]      							=     {}
        if "threshold" in kwargs:
          a__:					Dict      							=     kwargs['threshold']
        return {}, {}, postprocess_kwargs



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      Union[Predictions, List[Prediction]]:





        '''simple docstring'''





        return super().__call__(*lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     load_image(lowercase)
        a__:					List[Any]      							=     torch.IntTensor([[image.height, image.width]])
        a__:					Any      							=     self.image_processor(images=[image]   ,      return_tensors='pt')
        if self.tokenizer is not None:
          a__:					Any      							=     self.tokenizer(text=inputs['words']   ,      boxes=inputs['boxes']   ,      return_tensors='pt')
        a__:					List[str]      							=     target_size
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					Any      							=     model_inputs.pop('target_size')
        a__:					Union[str, Any]      							=     self.model(**lowercase)
        a__:					List[str]      							=     outputs.__class__({'target_size': target_size, **outputs})
        if self.tokenizer is not None:
          a__:					Union[str, Any]      							=     model_inputs['bbox']
        return model_outputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=0.9)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     model_outputs['target_size']
        if self.tokenizer is not None:
          # This is a LayoutLMForTokenClassification variant.
          # The OCR got the boxes and the model classified the words.
          a__							,   a__:					str      							=     target_size[0].tolist()

          def unnormalize(lowercase):
            return self._get_bounding_box(
                torch.Tensor(
                    [
                        (width * bbox[0] / 10_00),
                        (height * bbox[1] / 10_00),
                        (width * bbox[2] / 10_00),
                        (height * bbox[3] / 10_00),
                    ]))

          a__							,   a__:					Optional[Any]      							=     model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
          a__:					str      							=     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
          a__:					Union[str, Any]      							=     [unnormalize(lowercase) for bbox in model_outputs['bbox'].squeeze(0)]
          a__:					Dict      							=     ['score', 'label', 'box']
          a__:					Any      							=     [dict(zip(lowercase   ,      lowercase)) for vals in zip(scores.tolist()   ,      lowercase   ,      lowercase) if vals[0] > threshold]
        else:
          # This is a regular ForObjectDetectionModel
          a__:					List[str]      							=     self.image_processor.post_process_object_detection(lowercase   ,      lowercase   ,      lowercase)
          a__:					Tuple      							=     raw_annotations[0]
          a__:					List[str]      							=     raw_annotation['scores']
          a__:					int      							=     raw_annotation['labels']
          a__:					int      							=     raw_annotation['boxes']

          a__:					List[Any]      							=     scores.tolist()
          a__:					Any      							=     [self.model.config.idalabel[label.item()] for label in labels]
          a__:					Dict      							=     [self._get_bounding_box(lowercase) for box in boxes]

          # {"scores": [...], ...} --> [{"score":x, ...}, ...]
          a__:					Optional[Any]      							=     ['score', 'label', 'box']
          a__:					List[Any]      							=     [
              dict(zip(lowercase   ,      lowercase))
              for vals in zip(raw_annotation['scores']   ,      raw_annotation['labels']   ,      raw_annotation['boxes'])
          ]

        return annotation



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict[str, int]:





        '''simple docstring'''





        if self.framework != "pt":
          raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
        a__							,   a__							,   a__							,   a__:					List[Any]      							=     box.int().tolist()
        a__:					Optional[int]      							=     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox


