




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		):
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			int:
       UpperCAmelCase__							 =						tempfile.mkdtemp()

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

       UpperCAmelCase__							 =						{
           "do_resize": True,
           "size": 20,
           "do_center_crop": True,
           "crop_size": 18,
           "do_normalize": True,
           "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
           "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
       }
       UpperCAmelCase__							 =						os.path.join(self.tmpdirname ,      UpperCAmelCase_      )
       with open(self.image_processor_file ,      "w" ,      encoding="utf-8"      ) as fp:
        json.dump(UpperCAmelCase_ ,      UpperCAmelCase_      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      **lowerCamelCase   :Dict      )     ->			Dict:
       return BertTokenizer.from_pretrained(self.tmpdirname ,      **UpperCAmelCase_      )
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      **lowerCamelCase   :Tuple      )     ->			str:
       return BertTokenizerFast.from_pretrained(self.tmpdirname ,      **UpperCAmelCase_      )
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,      **UpperCAmelCase_      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			Dict:
       shutil.rmtree(self.tmpdirname      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[str]:
       UpperCAmelCase__							 =						[np.random.randint(255 ,      size=(3, 30, 400) ,      dtype=np.uinta      )]
       UpperCAmelCase__							 =						[Image.fromarray(np.moveaxis(UpperCAmelCase_ ,      0 ,      -1      )      ) for x in image_inputs]
       return image_inputs
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						self.get_tokenizer()
       UpperCAmelCase__							 =						self.get_rust_tokenizer()
       UpperCAmelCase__							 =						self.get_image_processor()

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

       UpperCAmelCase__							 =						AlignProcessor(tokenizer=UpperCAmelCase_ ,      image_processor=UpperCAmelCase_      )
       processor_fast.save_pretrained(self.tmpdirname      )
       UpperCAmelCase__							 =						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 ,      UpperCAmelCase_      )
       self.assertIsInstance(processor_fast.tokenizer ,      UpperCAmelCase_      )

       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 ,      UpperCAmelCase_      )
       self.assertIsInstance(processor_fast.image_processor ,      UpperCAmelCase_      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						AlignProcessor(tokenizer=self.get_tokenizer() ,      image_processor=self.get_image_processor()      )
       processor.save_pretrained(self.tmpdirname      )

       UpperCAmelCase__							 =						self.get_tokenizer(bos_token="(BOS)" ,      eos_token="(EOS)"      )
       UpperCAmelCase__							 =						self.get_image_processor(do_normalize=UpperCAmelCase_ ,      padding_value=1.0      )

       UpperCAmelCase__							 =						AlignProcessor.from_pretrained(
           self.tmpdirname ,      bos_token="(BOS)" ,      eos_token="(EOS)" ,      do_normalize=UpperCAmelCase_ ,      padding_value=1.0      )

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

       self.assertEqual(processor.image_processor.to_json_string() ,      image_processor_add_kwargs.to_json_string()      )
       self.assertIsInstance(processor.image_processor ,      UpperCAmelCase_      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			List[str]:
       UpperCAmelCase__							 =						self.get_image_processor()
       UpperCAmelCase__							 =						self.get_tokenizer()

       UpperCAmelCase__							 =						AlignProcessor(tokenizer=UpperCAmelCase_ ,      image_processor=UpperCAmelCase_      )

       UpperCAmelCase__							 =						self.prepare_image_inputs()

       UpperCAmelCase__							 =						image_processor(UpperCAmelCase_ ,      return_tensors="np"      )
       UpperCAmelCase__							 =						processor(images=UpperCAmelCase_ ,      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 	UpperCAmelCase_						(			self   :Any      )     ->			List[Any]:
       UpperCAmelCase__							 =						self.get_image_processor()
       UpperCAmelCase__							 =						self.get_tokenizer()

       UpperCAmelCase__							 =						AlignProcessor(tokenizer=UpperCAmelCase_ ,      image_processor=UpperCAmelCase_      )

       UpperCAmelCase__							 =						"lower newer"

       UpperCAmelCase__							 =						processor(text=UpperCAmelCase_      )

       UpperCAmelCase__							 =						tokenizer(UpperCAmelCase_ ,      padding="max_length" ,      max_length=64      )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key] ,      encoded_processor[key]      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.get_image_processor()
       UpperCAmelCase__							 =						self.get_tokenizer()

       UpperCAmelCase__							 =						AlignProcessor(tokenizer=UpperCAmelCase_ ,      image_processor=UpperCAmelCase_      )

       UpperCAmelCase__							 =						"lower newer"
       UpperCAmelCase__							 =						self.prepare_image_inputs()

       UpperCAmelCase__							 =						processor(text=UpperCAmelCase_ ,      images=UpperCAmelCase_      )

       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(UpperCAmelCase_      ):
        processor()
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						self.get_image_processor()
       UpperCAmelCase__							 =						self.get_tokenizer()

       UpperCAmelCase__							 =						AlignProcessor(tokenizer=UpperCAmelCase_ ,      image_processor=UpperCAmelCase_      )

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

       UpperCAmelCase__							 =						processor.batch_decode(UpperCAmelCase_      )
       UpperCAmelCase__							 =						tokenizer.batch_decode(UpperCAmelCase_      )

       self.assertListEqual(UpperCAmelCase_ ,      UpperCAmelCase_      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.get_image_processor()
       UpperCAmelCase__							 =						self.get_tokenizer()

       UpperCAmelCase__							 =						AlignProcessor(tokenizer=UpperCAmelCase_ ,      image_processor=UpperCAmelCase_      )

       UpperCAmelCase__							 =						"lower newer"
       UpperCAmelCase__							 =						self.prepare_image_inputs()

       UpperCAmelCase__							 =						processor(text=UpperCAmelCase_ ,      images=UpperCAmelCase_      )

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











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   Union[str, Any]		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   Union[str, Any]		=   os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
    _lowerCAmelCase						:   List[str]		=   F'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )





