

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 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  def 				lowercase_		(							self						:		Union[str, Any] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE__   =   [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       SCREAMING_SNAKE_CASE__   =   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] ) )

       SCREAMING_SNAKE_CASE__   =   {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
           '''image_std''': [0.26862954, 0.26130258, 0.27577711],
       }
       SCREAMING_SNAKE_CASE__   =   os.path.join(self.tmpdirname     ,       _lowerCamelCase )
       with open(self.image_processor_file     ,       '''w'''     ,       encoding='''utf-8''' ) as fp:
            json.dump(_lowerCamelCase     ,       _lowerCamelCase )






  def 				lowercase_		(							self						:		Dict     ,       **__lowerCamelCase						:		Tuple )		->							Union[str, Any]:
       return BertTokenizer.from_pretrained(self.tmpdirname     ,       **_lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[int]     ,       **__lowerCamelCase						:		Optional[int] )		->							Tuple:
       return BertTokenizerFast.from_pretrained(self.tmpdirname     ,       **_lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[Any]     ,       **__lowerCamelCase						:		Tuple )		->							Dict:
       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname     ,       **_lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple )		->							str:
       shutil.rmtree(self.tmpdirname )






  def 				lowercase_		(							self						:		int )		->							str:
       SCREAMING_SNAKE_CASE__   =   [np.random.randint(255     ,       size=(3, 30, 400)     ,       dtype=np.uinta )]
       SCREAMING_SNAKE_CASE__   =   [Image.fromarray(np.moveaxis(_lowerCamelCase     ,       0     ,       -1 ) ) for x in image_inputs]
       return image_inputs






  def 				lowercase_		(							self						:		int )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()
       SCREAMING_SNAKE_CASE__   =   self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__   =   self.get_image_processor()

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

       SCREAMING_SNAKE_CASE__   =   AlignProcessor(tokenizer=_lowerCamelCase     ,       image_processor=_lowerCamelCase )
       processor_fast.save_pretrained(self.tmpdirname )
       SCREAMING_SNAKE_CASE__   =   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     ,       _lowerCamelCase )
       self.assertIsInstance(processor_fast.tokenizer     ,       _lowerCamelCase )

       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     ,       _lowerCamelCase )
       self.assertIsInstance(processor_fast.image_processor     ,       _lowerCamelCase )






  def 				lowercase_		(							self						:		List[Any] )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   AlignProcessor(tokenizer=self.get_tokenizer()     ,       image_processor=self.get_image_processor() )
       processor.save_pretrained(self.tmpdirname )

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

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

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

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






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.get_image_processor()
       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()

       SCREAMING_SNAKE_CASE__   =   AlignProcessor(tokenizer=_lowerCamelCase     ,       image_processor=_lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__   =   image_processor(_lowerCamelCase     ,       return_tensors='''np''' )
       SCREAMING_SNAKE_CASE__   =   processor(images=_lowerCamelCase     ,       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 				lowercase_		(							self						:		Dict )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   self.get_image_processor()
       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()

       SCREAMING_SNAKE_CASE__   =   AlignProcessor(tokenizer=_lowerCamelCase     ,       image_processor=_lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   '''lower newer'''

       SCREAMING_SNAKE_CASE__   =   processor(text=_lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   tokenizer(_lowerCamelCase     ,       padding='''max_length'''     ,       max_length=64 )

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






  def 				lowercase_		(							self						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.get_image_processor()
       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()

       SCREAMING_SNAKE_CASE__   =   AlignProcessor(tokenizer=_lowerCamelCase     ,       image_processor=_lowerCamelCase )

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

       SCREAMING_SNAKE_CASE__   =   processor(text=_lowerCamelCase     ,       images=_lowerCamelCase )

       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(_lowerCamelCase ):
            processor()






  def 				lowercase_		(							self						:		List[str] )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   self.get_image_processor()
       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()

       SCREAMING_SNAKE_CASE__   =   AlignProcessor(tokenizer=_lowerCamelCase     ,       image_processor=_lowerCamelCase )

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

       SCREAMING_SNAKE_CASE__   =   processor.batch_decode(_lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   tokenizer.batch_decode(_lowerCamelCase )

       self.assertListEqual(_lowerCamelCase     ,       _lowerCamelCase )







  def 				lowercase_		(							self						:		Tuple )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   self.get_image_processor()
       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()

       SCREAMING_SNAKE_CASE__   =   AlignProcessor(tokenizer=_lowerCamelCase     ,       image_processor=_lowerCamelCase )

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

       SCREAMING_SNAKE_CASE__   =   processor(text=_lowerCamelCase     ,       images=_lowerCamelCase )

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





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_SCREAMING_SNAKE_CASE					:  Tuple             =      data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE					:  Dict             =      data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      data_utils
_SCREAMING_SNAKE_CASE					:  Any             =      data_utils
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(_A				,		'''rb'''    ) as fp:
               SCREAMING_SNAKE_CASE__   =   pickle.load(_A				,		encoding='''latin1'''    )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''    )
          SCREAMING_SNAKE_CASE__   =   corpus.vocab.__dict__
          torch.save(_A				,		_A    )

          SCREAMING_SNAKE_CASE__   =   corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''				,		_A    )
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F'''Save dataset to {pytorch_dataset_dump_path}'''    )
          torch.save(_A				,		_A    )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )

          print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig()
          else:
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig.from_json_file(_A    )
          print(F'''Building PyTorch model from configuration: {config}'''    )
          SCREAMING_SNAKE_CASE__   =   TransfoXLLMHeadModel(_A    )

          SCREAMING_SNAKE_CASE__   =   load_tf_weights_in_transfo_xl(_A				,		_A				,		_A    )
          # Save pytorch-model
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          print(F'''Save PyTorch model to {os.path.abspath(_A    )}'''    )
          torch.save(model.state_dict()				,		_A    )
          print(F'''Save configuration file to {os.path.abspath(_A    )}'''    )
          with open(_A				,		'''w'''				,		encoding='''utf-8'''    ) as f:
               f.write(config.to_json_string()    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Any             =      argparse.ArgumentParser()
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
       )
       parser.add_argument(
           '''--tf_checkpoint_path''',
           default='''''',
           type=str,
           help='''An optional path to a TensorFlow checkpoint path to be converted.''',
       )
       parser.add_argument(
           '''--transfo_xl_config_file''',
           default='''''',
           type=str,
           help=(
               '''An optional config json file corresponding to the pre-trained BERT model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--transfo_xl_dataset_file''',
           default='''''',
           type=str,
           help='''An optional dataset file to be converted in a vocabulary.''',
       )
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      parser.parse_args()
       convert_transfo_xl_checkpoint_to_pytorch(
           args.tf_checkpoint_path,
           args.transfo_xl_config_file,
           args.pytorch_dump_folder_path,
           args.transfo_xl_dataset_file,
       )


