


'''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 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  str						=  tempfile.mkdtemp()
    _a					:  List[Any]						=  SamImageProcessor()
    _a					:  int						=  SamProcessor(lowerCamelCase_      )
    processor.save_pretrained(self.tmpdirname      )




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    Optional[int]:
    return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCamelCase_      ).image_processor




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    shutil.rmtree(self.tmpdirname      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Optional[Any]						=  [np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta      )]

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

    return image_inputs




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

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

    _a					:  List[Any]						=  SamProcessor.from_pretrained(self.tmpdirname		,      do_normalize=lowerCamelCase_		,      padding_value=1.0      )

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




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  int						=  self.get_image_processor()

    _a					:  List[Any]						=  SamProcessor(image_processor=lowerCamelCase_      )

    _a					:  Optional[Any]						=  self.prepare_image_inputs()

    _a					:  List[str]						=  image_processor(lowerCamelCase_		,      return_tensors='np'      )
    _a					:  int						=  processor(images=lowerCamelCase_		,      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   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Optional[int]						=  self.get_image_processor()

    _a					:  Union[str, Any]						=  SamProcessor(image_processor=lowerCamelCase_      )
    _a					:  Union[str, Any]						=  [torch.ones((1, 3, 5, 5)      )]

    _a					:  Optional[int]						=  [[1_7_6_4, 2_6_4_6]]

    _a					:  Optional[int]						=  [[6_8_3, 1_0_2_4]]
    _a					:  Dict						=  processor.post_process_masks(lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )
    self.assertEqual(masks[0].shape		,      (1, 3, 1_7_6_4, 2_6_4_6)      )

    _a					:  List[str]						=  processor.post_process_masks(
        lowerCamelCase_		,      torch.tensor(lowerCamelCase_      )		,      torch.tensor(lowerCamelCase_      )      )
    self.assertEqual(masks[0].shape		,      (1, 3, 1_7_6_4, 2_6_4_6)      )

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

    self.assertEqual(masks[0].shape		,      (1, 3, 1_7_6_4, 2_6_4_6)      )

    _a					:  Optional[int]						=  [[1, 0], [0, 1]]
    with self.assertRaises(lowerCamelCase_      ):
       _a					:  str						=  processor.post_process_masks(lowerCamelCase_		,      np.array(lowerCamelCase_      )		,      np.array(lowerCamelCase_      )      )







@require_vision
@require_tf
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Optional[Any]						=  tempfile.mkdtemp()
    _a					:  int						=  SamImageProcessor()
    _a					:  List[str]						=  SamProcessor(lowerCamelCase_      )
    processor.save_pretrained(self.tmpdirname      )




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    str:
    return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCamelCase_      ).image_processor




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    shutil.rmtree(self.tmpdirname      )




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  List[Any]						=  [np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta      )]

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

    return image_inputs




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Dict						=  SamProcessor(image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

    _a					:  Any						=  self.get_image_processor(do_normalize=lowerCamelCase_		,      padding_value=1.0      )

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

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




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Any						=  self.get_image_processor()

    _a					:  Any						=  SamProcessor(image_processor=lowerCamelCase_      )

    _a					:  int						=  self.prepare_image_inputs()

    _a					:  Any						=  image_processor(lowerCamelCase_		,      return_tensors='np'      )
    _a					:  str						=  processor(images=lowerCamelCase_		,      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   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  Dict						=  self.get_image_processor()

    _a					:  Optional[Any]						=  SamProcessor(image_processor=lowerCamelCase_      )
    _a					:  Optional[int]						=  [tf.ones((1, 3, 5, 5)      )]

    _a					:  Any						=  [[1_7_6_4, 2_6_4_6]]

    _a					:  Tuple						=  [[6_8_3, 1_0_2_4]]
    _a					:  int						=  processor.post_process_masks(lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='tf'      )
    self.assertEqual(masks[0].shape		,      (1, 3, 1_7_6_4, 2_6_4_6)      )

    _a					:  Any						=  processor.post_process_masks(
        lowerCamelCase_		,      tf.convert_to_tensor(lowerCamelCase_      )		,      tf.convert_to_tensor(lowerCamelCase_      )		,      return_tensors='tf'		,      )
    self.assertEqual(masks[0].shape		,      (1, 3, 1_7_6_4, 2_6_4_6)      )

    # should also work with np
    _a					:  Optional[int]						=  [np.ones((1, 3, 5, 5)      )]
    _a					:  List[str]						=  processor.post_process_masks(
        lowerCamelCase_		,      np.array(lowerCamelCase_      )		,      np.array(lowerCamelCase_      )		,      return_tensors='tf'      )

    self.assertEqual(masks[0].shape		,      (1, 3, 1_7_6_4, 2_6_4_6)      )

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







@require_vision
@require_torchvision
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Any						=  tempfile.mkdtemp()
    _a					:  Dict						=  SamImageProcessor()
    _a					:  Tuple						=  SamProcessor(lowerCamelCase_      )
    processor.save_pretrained(self.tmpdirname      )




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    Union[str, Any]:
    return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCamelCase_      ).image_processor




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    shutil.rmtree(self.tmpdirname      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Dict						=  [np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta      )]

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

    return image_inputs




 @is_pt_tf_cross_test
 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Optional[int]						=  self.get_image_processor()

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

    _a					:  int						=  [[1_7_6_4, 2_6_4_6]]

    _a					:  int						=  [[6_8_3, 1_0_2_4]]
    _a					:  Optional[int]						=  processor.post_process_masks(
        lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='tf'      )
    _a					:  Optional[int]						=  processor.post_process_masks(
        lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      return_tensors='pt'      )

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




 @is_pt_tf_cross_test
 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Tuple						=  self.get_image_processor()

    _a					:  List[Any]						=  SamProcessor(image_processor=lowerCamelCase_      )

    _a					:  str						=  self.prepare_image_inputs()

    _a					:  Optional[int]						=  image_processor(lowerCamelCase_		,      return_tensors='pt'      )['pixel_values'].numpy()
    _a					:  Tuple						=  processor(images=lowerCamelCase_		,      return_tensors='pt'      )['pixel_values'].numpy()

    _a					:  Optional[Any]						=  image_processor(lowerCamelCase_		,      return_tensors='tf'      )['pixel_values'].numpy()
    _a					:  Dict						=  processor(images=lowerCamelCase_		,      return_tensors='tf'      )['pixel_values'].numpy()

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






'''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_      :		List[str]          =		logging.get_logger(__name__)
logging.set_verbosity_info()


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   if "xprophetnet" in prophetnet_checkpoint_path:
      _a					:  str						=  XLMProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Any						=  XLMProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )
   else:
      _a					:  Optional[Any]						=  ProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Dict						=  ProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )

   _a					:  Tuple						=  ['key_proj', 'value_proj', 'query_proj']

   _a					:  str						=  {
       '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"]:
      _a					:  Tuple						=  key.split('.'      )

      if attributes[0] == "lm_head":
         _a					:  str						=  prophet
         _a					:  Dict						=  prophet_old
      else:
         _a					:  Union[str, Any]						=  prophet.prophetnet
         _a					:  List[Any]						=  prophet_old.model

      _a					:  Dict						=  False
      for attribute in attributes:
         if attribute in mapping:
            _a					:  List[str]						=  mapping[attribute]
            if not hasattr(A		,	A      ) and len(A      ) > 0:
               _a					:  Any						=  attribute
         elif hasattr(A		,	A      ):
            _a					:  Dict						=  attribute

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

            elif attribute == "key_proj":
               _a					:  str						=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _a					:  List[Any]						=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _a					:  Optional[int]						=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _a					:  str						=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _a					:  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] == 5_1_2, "We want 512 position_embeddings."
            _a					:  Tuple						=  nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]      )
            _a					:  Any						=  True
            break

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

            if old_attribute == "":
               _a					:  List[str]						=  old_model
            else:
               if not hasattr(A		,	A      ):
                  raise ValueError(f'''{old_model} does not have {old_attribute}'''      )
               _a					:  Optional[int]						=  getattr(A		,	A      )

      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(A      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Any          =		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_      :		List[str]          =		parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


