

'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					tempfile.mkdtemp()

   __lowercase				    =					[
       '''[UNK]''',
       '''[CLS]''',
       '''[SEP]''',
       '''[PAD]''',
       '''[MASK]''',
       '''want''',
       '''##want''',
       '''##ed''',
       '''wa''',
       '''un''',
       '''runn''',
       '''##ing''',
       ''',''',
       '''low''',
       '''lowest''',
   ]
   __lowercase				    =					os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''vocab_file''']     )
   with open(self.vocab_file   ,'''w'''   ,encoding='''utf-8'''     ) as vocab_writer:
    vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]     )     )

   __lowercase				    =					{
       '''do_resize''': True,
       '''size''': 2_0,
       '''do_center_crop''': True,
       '''crop_size''': 1_8,
       '''do_normalize''': True,
       '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
       '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
   }
   __lowercase				    =					os.path.join(self.tmpdirname   ,__lowercase     )
   with open(self.image_processor_file   ,'''w'''   ,encoding='''utf-8'''     ) as fp:
    json.dump(__lowercase   ,__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,**lowercase__  :	List[Any]     ):
   return BertTokenizer.from_pretrained(self.tmpdirname   ,**__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,**lowercase__  :	int     ):
   return BertTokenizerFast.from_pretrained(self.tmpdirname   ,**__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,**lowercase__  :	str     ):
   return EfficientNetImageProcessor.from_pretrained(self.tmpdirname   ,**__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   shutil.rmtree(self.tmpdirname     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					[np.random.randint(2_5_5   ,size=(3, 3_0, 4_0_0)   ,dtype=np.uinta     )]
   __lowercase				    =					[Image.fromarray(np.moveaxis(__lowercase   ,0   ,-1     )     ) for x in image_inputs]
   return image_inputs






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					self.get_tokenizer()
   __lowercase				    =					self.get_rust_tokenizer()
   __lowercase				    =					self.get_image_processor()

   __lowercase				    =					AlignProcessor(tokenizer=__lowercase   ,image_processor=__lowercase     )
   processor_slow.save_pretrained(self.tmpdirname     )
   __lowercase				    =					AlignProcessor.from_pretrained(self.tmpdirname   ,use_fast=__lowercase     )

   __lowercase				    =					AlignProcessor(tokenizer=__lowercase   ,image_processor=__lowercase     )
   processor_fast.save_pretrained(self.tmpdirname     )
   __lowercase				    =					AlignProcessor.from_pretrained(self.tmpdirname     )

   self.assertEqual(processor_slow.tokenizer.get_vocab()   ,tokenizer_slow.get_vocab()     )
   self.assertEqual(processor_fast.tokenizer.get_vocab()   ,tokenizer_fast.get_vocab()     )
   self.assertEqual(tokenizer_slow.get_vocab()   ,tokenizer_fast.get_vocab()     )
   self.assertIsInstance(processor_slow.tokenizer   ,__lowercase     )
   self.assertIsInstance(processor_fast.tokenizer   ,__lowercase     )

   self.assertEqual(processor_slow.image_processor.to_json_string()   ,image_processor.to_json_string()     )
   self.assertEqual(processor_fast.image_processor.to_json_string()   ,image_processor.to_json_string()     )
   self.assertIsInstance(processor_slow.image_processor   ,__lowercase     )
   self.assertIsInstance(processor_fast.image_processor   ,__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					AlignProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()     )
   processor.save_pretrained(self.tmpdirname     )

   __lowercase				    =					self.get_tokenizer(bos_token='''(BOS)'''   ,eos_token='''(EOS)'''     )
   __lowercase				    =					self.get_image_processor(do_normalize=__lowercase   ,padding_value=1.0     )

   __lowercase				    =					AlignProcessor.from_pretrained(
       self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=__lowercase   ,padding_value=1.0     )

   self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()     )
   self.assertIsInstance(processor.tokenizer   ,__lowercase     )

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






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.get_image_processor()
   __lowercase				    =					self.get_tokenizer()

   __lowercase				    =					AlignProcessor(tokenizer=__lowercase   ,image_processor=__lowercase     )

   __lowercase				    =					self.prepare_image_inputs()

   __lowercase				    =					image_processor(__lowercase   ,return_tensors='''np'''     )
   __lowercase				    =					processor(images=__lowercase   ,return_tensors='''np'''     )

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






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					self.get_image_processor()
   __lowercase				    =					self.get_tokenizer()

   __lowercase				    =					AlignProcessor(tokenizer=__lowercase   ,image_processor=__lowercase     )

   __lowercase				    =					'''lower newer'''

   __lowercase				    =					processor(text=__lowercase     )

   __lowercase				    =					tokenizer(__lowercase   ,padding='''max_length'''   ,max_length=6_4     )

   for key in encoded_tok.keys():
    self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					self.get_image_processor()
   __lowercase				    =					self.get_tokenizer()

   __lowercase				    =					AlignProcessor(tokenizer=__lowercase   ,image_processor=__lowercase     )

   __lowercase				    =					'''lower newer'''
   __lowercase				    =					self.prepare_image_inputs()

   __lowercase				    =					processor(text=__lowercase   ,images=__lowercase     )

   self.assertListEqual(list(inputs.keys()     )   ,['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']     )

   # test if it raises when no input is passed
   with pytest.raises(__lowercase     ):
    processor()






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					self.get_image_processor()
   __lowercase				    =					self.get_tokenizer()

   __lowercase				    =					AlignProcessor(tokenizer=__lowercase   ,image_processor=__lowercase     )

   __lowercase				    =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   __lowercase				    =					processor.batch_decode(__lowercase     )
   __lowercase				    =					tokenizer.batch_decode(__lowercase     )

   self.assertListEqual(__lowercase   ,__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.get_image_processor()
   __lowercase				    =					self.get_tokenizer()

   __lowercase				    =					AlignProcessor(tokenizer=__lowercase   ,image_processor=__lowercase     )

   __lowercase				    =					'''lower newer'''
   __lowercase				    =					self.prepare_image_inputs()

   __lowercase				    =					processor(text=__lowercase   ,images=__lowercase     )

   self.assertListEqual(list(inputs.keys()     )   ,processor.model_input_names     )








"""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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if "xprophetnet" in prophetnet_checkpoint_path:
														__lowerCamelCase   :   Optional[Any]       =XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =XLMProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =ProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''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   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

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

																					if attribute == "weight":
																												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																												__lowerCamelCase   :   str       =old_model.weight
																												logger.info(F'{attribute} is initialized.' )
																												__lowerCamelCase   :   Any       =True
																												break
																					elif attribute == "bias":
																												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																												__lowerCamelCase   :   Union[str, Any]       =old_model.bias
																												logger.info(F'{attribute} is initialized' )
																												__lowerCamelCase   :   str       =True
																												break
																					elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE     ,    '''in_proj_weight''' ):
																												__lowerCamelCase   :   int       =old_model.in_proj_weight.shape[0] // 3
																												__lowerCamelCase   :   Union[str, Any]       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
																												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   :   List[str]       =nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
																																			__lowerCamelCase   :   str       =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   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   Dict       =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   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

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

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

														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(SCREAMING_SNAKE_CASE )


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)




