



'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase											=						logging.get_logger(__name__)
logging.set_verbosity_info()

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  if "xprophetnet" in prophetnet_checkpoint_path:
    __lowerCamelCase       :       Union[str, Any]           = XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = XLMProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       Optional[int]           = ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = ProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )

  __lowerCamelCase       :       Union[str, Any]           = ['key_proj', 'value_proj', 'query_proj']

  __lowerCamelCase       :       Optional[Any]           = {
      'self_attn': 'ngram_self_attn',
      'cross_attn': 'encoder_attn',
      'cross_attn_layer_norm': 'encoder_attn_layer_norm',
      'feed_forward_layer_norm': 'final_layer_norm',
      'feed_forward': '',
      'intermediate': 'fc1',
      'output': 'fc2',
      'key_proj': 'k_proj',
      'query_proj': 'q_proj',
      'value_proj': 'v_proj',
      'word_embeddings': 'embed_tokens',
      'embeddings_layer_norm': 'emb_layer_norm',
      'relative_pos_embeddings': 'relative_linear',
      'ngram_embeddings': 'ngram_input_embed',
      'position_embeddings': 'embed_positions',
  }

  for key in loading_info["missing_keys"]:
    __lowerCamelCase       :       Optional[int]           = key.split('.'       )

    if attributes[0] == "lm_head":
      __lowerCamelCase       :       Dict           = prophet
      __lowerCamelCase       :       List[Any]           = prophet_old
    else:
      __lowerCamelCase       :       Any           = prophet.prophetnet
      __lowerCamelCase       :       Any           = prophet_old.model

    __lowerCamelCase       :       Optional[Any]           = False
    for attribute in attributes:
      if attribute in mapping:
        __lowerCamelCase       :       Any           = mapping[attribute]
        if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
          __lowerCamelCase       :       int           = attribute
      elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Optional[int]           = attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.weight
        logger.info(F'{attribute} is initialized.'       )
        __lowerCamelCase       :       List[Any]           = True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.bias
        logger.info(F'{attribute} is initialized'       )
        __lowerCamelCase       :       Dict           = True
        break
      elif attribute in special_keys and hasattr(_lowerCAmelCase       ,'in_proj_weight'       ):
        __lowerCamelCase       :       Optional[Any]           = old_model.in_proj_weight.shape[0] // 3
        __lowerCamelCase       :       Optional[Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

        elif attribute == "key_proj":
          __lowerCamelCase       :       List[str]           = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
        elif attribute == "value_proj":
          __lowerCamelCase       :       str           = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
        __lowerCamelCase       :       Optional[int]           = True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.embed_positions.weight[:512, :]       )
        __lowerCamelCase       :       Dict           = True
        break

      if attribute.isdigit():
        __lowerCamelCase       :       List[str]           = model[int(_lowerCAmelCase       )]
        __lowerCamelCase       :       Union[str, Any]           = old_model[int(_lowerCAmelCase       )]
      else:
        __lowerCamelCase       :       Union[str, Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

        if old_attribute == "":
          __lowerCamelCase       :       str           = old_model
        else:
          if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
            raise ValueError(F'{old_model} does not have {old_attribute}'       )
          __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

    if not is_key_init:
      raise ValueError(F'{key} was not correctly initialized!'       )

  print(F'Saving model to {pytorch_dump_folder_path}'       )
  prophet.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=["""image_processor""", """tokenizer"""]
  a_												="""OwlViTImageProcessor"""
  a_												=("""CLIPTokenizer""", """CLIPTokenizerFast""")




  def __init__( self      :	Any			,	_a      :	str=None			,	_a      :	Optional[Any]=None			,	**_a      :	int   )					->					List[str]:
    __lowerCamelCase       :       List[Any]           = None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'			,	_a			,	)
      __lowerCamelCase       :       Tuple           = kwargs.pop('feature_extractor'   )

    __lowerCamelCase       :       int           = image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'   )
    if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'   )

    super().__init__(_a			,	_a   )




  def __call__( self      :	Tuple			,	_a      :	Tuple=None			,	_a      :	int=None			,	_a      :	List[Any]=None			,	_a      :	List[Any]="max_length"			,	_a      :	Union[str, Any]="np"			,	**_a      :	Union[str, Any]   )					->					List[str]:

    if text is None and query_images is None and images is None:
      raise ValueError(
          'You have to specify at least one text or query image or image. All three cannot be none.'   )

    if text is not None:
      if isinstance(_a			,	_a   ) or (isinstance(_a			,	_a   ) and not isinstance(text[0]			,	_a   )):
        __lowerCamelCase       :       Any           = [self.tokenizer(_a			,	padding=_a			,	return_tensors=_a			,	**_a   )]

      elif isinstance(_a			,	_a   ) and isinstance(text[0]			,	_a   ):
        __lowerCamelCase       :       List[Any]           = []

        # Maximum number of queries across batch
        __lowerCamelCase       :       str           = max([len(_a   ) for t in text]   )

        # Pad all batch samples to max number of text queries
        for t in text:
          if len(_a   ) != max_num_queries:
            __lowerCamelCase       :       List[Any]           = t + [' '] * (max_num_queries - len(_a   ))

          __lowerCamelCase       :       Dict           = self.tokenizer(_a			,	padding=_a			,	return_tensors=_a			,	**_a   )
          encodings.append(_a   )
      else:
        raise TypeError('Input text should be a string, a list of strings or a nested list of strings'   )

      if return_tensors == "np":
        __lowerCamelCase       :       Optional[Any]           = np.concatenate([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       str           = np.concatenate([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      elif return_tensors == "jax" and is_flax_available():
        import jax.numpy as jnp

        __lowerCamelCase       :       int           = jnp.concatenate([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       List[str]           = jnp.concatenate([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      elif return_tensors == "pt" and is_torch_available():
        import torch

        __lowerCamelCase       :       str           = torch.cat([encoding['input_ids'] for encoding in encodings]			,	dim=0   )
        __lowerCamelCase       :       Optional[Any]           = torch.cat([encoding['attention_mask'] for encoding in encodings]			,	dim=0   )

      elif return_tensors == "tf" and is_tf_available():
        import tensorflow as tf

        __lowerCamelCase       :       List[Any]           = tf.stack([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       int           = tf.stack([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      else:
        raise ValueError('Target return tensor type could not be returned'   )

      __lowerCamelCase       :       Any           = BatchEncoding()
      __lowerCamelCase       :       Dict           = input_ids
      __lowerCamelCase       :       str           = attention_mask

    if query_images is not None:
      __lowerCamelCase       :       Optional[int]           = BatchEncoding()
      __lowerCamelCase       :       List[Any]           = self.image_processor(
          _a			,	return_tensors=_a			,	**_a   ).pixel_values
      __lowerCamelCase       :       str           = query_pixel_values

    if images is not None:
      __lowerCamelCase       :       Union[str, Any]           = self.image_processor(_a			,	return_tensors=_a			,	**_a   )

    if text is not None and images is not None:
      __lowerCamelCase       :       Tuple           = image_features.pixel_values
      return encoding
    elif query_images is not None and images is not None:
      __lowerCamelCase       :       Tuple           = image_features.pixel_values
      return encoding
    elif text is not None or query_images is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**_a   )			,	tensor_type=_a   )




  def 						_lowercase     ( self      :	Optional[Any]			,	*_a      :	List[str]			,	**_a      :	Dict   )					->					int:
    return self.image_processor.post_process(*_a			,	**_a   )




  def 						_lowercase     ( self      :	str			,	*_a      :	str			,	**_a      :	List[str]   )					->					int:
    return self.image_processor.post_process_object_detection(*_a			,	**_a   )




  def 						_lowercase     ( self      :	int			,	*_a      :	List[Any]			,	**_a      :	Optional[int]   )					->					str:
    return self.image_processor.post_process_image_guided_detection(*_a			,	**_a   )




  def 						_lowercase     ( self      :	Tuple			,	*_a      :	Optional[Any]			,	**_a      :	List[Any]   )					->					Tuple:
    return self.tokenizer.batch_decode(*_a			,	**_a   )




  def 						_lowercase     ( self      :	str			,	*_a      :	Optional[Any]			,	**_a      :	str   )					->					Union[str, Any]:
    return self.tokenizer.decode(*_a			,	**_a   )




  @property
  def 						_lowercase     ( self      :	Any   )					->					Any:
    warnings.warn(
        '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,	_a			,	)
    return self.image_processor_class




  @property
  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Any:
    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,	_a			,	)
    return self.image_processor






