



'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
   import torch

if is_tf_available():
   import tensorflow as tf






@require_vision
@require_torchvision
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Any   )					->					int:
    __lowerCamelCase       :       Tuple           = tempfile.mkdtemp()
    __lowerCamelCase       :       Tuple           = SamImageProcessor()
    __lowerCamelCase       :       List[str]           = SamProcessor(_a   )
    processor.save_pretrained(self.tmpdirname   )




  def 						_lowercase     ( self      :	Dict			,	**_a      :	Any   )					->					Optional[Any]:
    return AutoProcessor.from_pretrained(self.tmpdirname			,	**_a   ).image_processor




  def 						_lowercase     ( self      :	List[str]   )					->					Dict:
    shutil.rmtree(self.tmpdirname   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Dict:
    __lowerCamelCase       :       Tuple           = [np.random.randint(255			,	size=(3, 30, 400)			,	dtype=np.uinta   )]

    __lowerCamelCase       :       List[str]           = [Image.fromarray(np.moveaxis(_a			,	0			,	-1   )   ) for x in image_inputs]

    return image_inputs




  def 						_lowercase     ( self      :	Dict   )					->					List[str]:
    __lowerCamelCase       :       List[str]           = SamProcessor(image_processor=self.get_image_processor()   )
    processor.save_pretrained(self.tmpdirname   )

    __lowerCamelCase       :       Optional[Any]           = self.get_image_processor(do_normalize=_a			,	padding_value=1.0   )

    __lowerCamelCase       :       Union[str, Any]           = SamProcessor.from_pretrained(self.tmpdirname			,	do_normalize=_a			,	padding_value=1.0   )

    self.assertEqual(processor.image_processor.to_json_string()			,	image_processor_add_kwargs.to_json_string()   )
    self.assertIsInstance(processor.image_processor			,	_a   )




  def 						_lowercase     ( self      :	str   )					->					str:
    __lowerCamelCase       :       List[str]           = self.get_image_processor()

    __lowerCamelCase       :       Union[str, Any]           = SamProcessor(image_processor=_a   )

    __lowerCamelCase       :       Optional[int]           = self.prepare_image_inputs()

    __lowerCamelCase       :       List[str]           = image_processor(_a			,	return_tensors='np'   )
    __lowerCamelCase       :       Optional[int]           = processor(images=_a			,	return_tensors='np'   )

    input_feat_extract.pop('original_sizes'   )  # pop original_sizes as it is popped in the processor
    input_feat_extract.pop('reshaped_input_sizes'   )  # pop original_sizes as it is popped in the processor

    for key in input_feat_extract.keys():
      self.assertAlmostEqual(input_feat_extract[key].sum()			,	input_processor[key].sum()			,	delta=1e-2   )




  @require_torch
  def 						_lowercase     ( self      :	Optional[Any]   )					->					Any:
    __lowerCamelCase       :       Tuple           = self.get_image_processor()

    __lowerCamelCase       :       List[str]           = SamProcessor(image_processor=_a   )
    __lowerCamelCase       :       Dict           = [torch.ones((1, 3, 5, 5)   )]

    __lowerCamelCase       :       Dict           = [[1764, 2646]]

    __lowerCamelCase       :       Union[str, Any]           = [[683, 1024]]
    __lowerCamelCase       :       List[str]           = processor.post_process_masks(_a			,	_a			,	_a   )
    self.assertEqual(masks[0].shape			,	(1, 3, 1764, 2646)   )

    __lowerCamelCase       :       int           = processor.post_process_masks(
        _a			,	torch.tensor(_a   )			,	torch.tensor(_a   )   )
    self.assertEqual(masks[0].shape			,	(1, 3, 1764, 2646)   )

    # should also work with np
    __lowerCamelCase       :       Optional[int]           = [np.ones((1, 3, 5, 5)   )]
    __lowerCamelCase       :       List[Any]           = processor.post_process_masks(_a			,	np.array(_a   )			,	np.array(_a   )   )

    self.assertEqual(masks[0].shape			,	(1, 3, 1764, 2646)   )

    __lowerCamelCase       :       List[str]           = [[1, 0], [0, 1]]
    with self.assertRaises(_a   ):
      __lowerCamelCase       :       str           = processor.post_process_masks(_a			,	np.array(_a   )			,	np.array(_a   )   )






@require_vision
@require_tf
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	int   )					->					Optional[int]:
    __lowerCamelCase       :       Dict           = tempfile.mkdtemp()
    __lowerCamelCase       :       Optional[int]           = SamImageProcessor()
    __lowerCamelCase       :       Tuple           = SamProcessor(_a   )
    processor.save_pretrained(self.tmpdirname   )




  def 						_lowercase     ( self      :	Optional[Any]			,	**_a      :	str   )					->					Tuple:
    return AutoProcessor.from_pretrained(self.tmpdirname			,	**_a   ).image_processor




  def 						_lowercase     ( self      :	str   )					->					Union[str, Any]:
    shutil.rmtree(self.tmpdirname   )




  def 						_lowercase     ( self      :	Dict   )					->					List[str]:
    __lowerCamelCase       :       Optional[int]           = [np.random.randint(255			,	size=(3, 30, 400)			,	dtype=np.uinta   )]

    __lowerCamelCase       :       int           = [Image.fromarray(np.moveaxis(_a			,	0			,	-1   )   ) for x in image_inputs]

    return image_inputs




  def 						_lowercase     ( self      :	str   )					->					Union[str, Any]:
    __lowerCamelCase       :       int           = SamProcessor(image_processor=self.get_image_processor()   )
    processor.save_pretrained(self.tmpdirname   )

    __lowerCamelCase       :       Optional[int]           = self.get_image_processor(do_normalize=_a			,	padding_value=1.0   )

    __lowerCamelCase       :       Optional[int]           = SamProcessor.from_pretrained(self.tmpdirname			,	do_normalize=_a			,	padding_value=1.0   )

    self.assertEqual(processor.image_processor.to_json_string()			,	image_processor_add_kwargs.to_json_string()   )
    self.assertIsInstance(processor.image_processor			,	_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					List[Any]:
    __lowerCamelCase       :       int           = self.get_image_processor()

    __lowerCamelCase       :       Optional[Any]           = SamProcessor(image_processor=_a   )

    __lowerCamelCase       :       List[str]           = self.prepare_image_inputs()

    __lowerCamelCase       :       str           = image_processor(_a			,	return_tensors='np'   )
    __lowerCamelCase       :       int           = processor(images=_a			,	return_tensors='np'   )

    input_feat_extract.pop('original_sizes'   )  # pop original_sizes as it is popped in the processor
    input_feat_extract.pop('reshaped_input_sizes'   )  # pop reshaped_input_sizes as it is popped in the processor

    for key in input_feat_extract.keys():
      self.assertAlmostEqual(input_feat_extract[key].sum()			,	input_processor[key].sum()			,	delta=1e-2   )




  @require_tf
  def 						_lowercase     ( self      :	List[str]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = self.get_image_processor()

    __lowerCamelCase       :       str           = SamProcessor(image_processor=_a   )
    __lowerCamelCase       :       str           = [tf.ones((1, 3, 5, 5)   )]

    __lowerCamelCase       :       Dict           = [[1764, 2646]]

    __lowerCamelCase       :       int           = [[683, 1024]]
    __lowerCamelCase       :       List[str]           = processor.post_process_masks(_a			,	_a			,	_a			,	return_tensors='tf'   )
    self.assertEqual(masks[0].shape			,	(1, 3, 1764, 2646)   )

    __lowerCamelCase       :       Optional[Any]           = processor.post_process_masks(
        _a			,	tf.convert_to_tensor(_a   )			,	tf.convert_to_tensor(_a   )			,	return_tensors='tf'			,	)
    self.assertEqual(masks[0].shape			,	(1, 3, 1764, 2646)   )

    # should also work with np
    __lowerCamelCase       :       Dict           = [np.ones((1, 3, 5, 5)   )]
    __lowerCamelCase       :       Tuple           = processor.post_process_masks(
        _a			,	np.array(_a   )			,	np.array(_a   )			,	return_tensors='tf'   )

    self.assertEqual(masks[0].shape			,	(1, 3, 1764, 2646)   )

    __lowerCamelCase       :       Optional[Any]           = [[1, 0], [0, 1]]
    with self.assertRaises(tf.errors.InvalidArgumentError   ):
      __lowerCamelCase       :       List[Any]           = processor.post_process_masks(
          _a			,	np.array(_a   )			,	np.array(_a   )			,	return_tensors='tf'   )






@require_vision
@require_torchvision
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	str   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[int]           = tempfile.mkdtemp()
    __lowerCamelCase       :       Optional[Any]           = SamImageProcessor()
    __lowerCamelCase       :       Dict           = SamProcessor(_a   )
    processor.save_pretrained(self.tmpdirname   )




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




  def 						_lowercase     ( self      :	int   )					->					str:
    shutil.rmtree(self.tmpdirname   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Optional[Any]:
    __lowerCamelCase       :       List[Any]           = [np.random.randint(255			,	size=(3, 30, 400)			,	dtype=np.uinta   )]

    __lowerCamelCase       :       Optional[int]           = [Image.fromarray(np.moveaxis(_a			,	0			,	-1   )   ) for x in image_inputs]

    return image_inputs




  @is_pt_tf_cross_test
  def 						_lowercase     ( self      :	Any   )					->					Optional[Any]:
    __lowerCamelCase       :       int           = self.get_image_processor()

    __lowerCamelCase       :       List[str]           = SamProcessor(image_processor=_a   )
    __lowerCamelCase       :       int           = np.random.randint(0			,	2			,	size=(1, 3, 5, 5)   ).astype(np.floataa   )
    __lowerCamelCase       :       Tuple           = [tf.convert_to_tensor(_a   )]
    __lowerCamelCase       :       Optional[int]           = [torch.tensor(_a   )]

    __lowerCamelCase       :       Union[str, Any]           = [[1764, 2646]]

    __lowerCamelCase       :       Union[str, Any]           = [[683, 1024]]
    __lowerCamelCase       :       int           = processor.post_process_masks(
        _a			,	_a			,	_a			,	return_tensors='tf'   )
    __lowerCamelCase       :       List[Any]           = processor.post_process_masks(
        _a			,	_a			,	_a			,	return_tensors='pt'   )

    self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()   )   )




  @is_pt_tf_cross_test
  def 						_lowercase     ( self      :	Optional[int]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Union[str, Any]           = self.get_image_processor()

    __lowerCamelCase       :       Tuple           = SamProcessor(image_processor=_a   )

    __lowerCamelCase       :       int           = self.prepare_image_inputs()

    __lowerCamelCase       :       int           = image_processor(_a			,	return_tensors='pt'   )['pixel_values'].numpy()
    __lowerCamelCase       :       str           = processor(images=_a			,	return_tensors='pt'   )['pixel_values'].numpy()

    __lowerCamelCase       :       Dict           = image_processor(_a			,	return_tensors='tf'   )['pixel_values'].numpy()
    __lowerCamelCase       :       str           = processor(images=_a			,	return_tensors='tf'   )['pixel_values'].numpy()

    self.assertTrue(np.allclose(_a			,	_a   )   )
    self.assertTrue(np.allclose(_a			,	_a   )   )
    self.assertTrue(np.allclose(_a			,	_a   )   )











'''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)






