




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    tempfile.mkdtemp()

          __UpperCamelCase			=    CLIPImageProcessor()
          __UpperCamelCase			=    BertTokenizer.from_pretrained(
              '''hf-internal-testing/tiny-random-BertModel'''			,							model_input_names=['''input_ids''', '''attention_mask''']					)

          __UpperCamelCase			=    GitProcessor(snake_case			,							snake_case					)

          processor.save_pretrained(self.tmpdirname					)







      def    snake_case						(  self       :			Optional[Any]			,							**snake_case       :			List[Any]					):
          return AutoProcessor.from_pretrained(self.tmpdirname			,							**snake_case					).tokenizer







      def    snake_case						(  self       :			Optional[int]			,							**snake_case       :			Dict					):
          return AutoProcessor.from_pretrained(self.tmpdirname			,							**snake_case					).image_processor







      def    snake_case						(  self       :			List[Any]					):
          shutil.rmtree(self.tmpdirname					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta					)]

          __UpperCamelCase			=    [Image.fromarray(np.moveaxis(snake_case			,							0			,							-1					)					) for x in image_inputs]

          return image_inputs







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    GitProcessor(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=snake_case			,							padding_value=1.0					)

          __UpperCamelCase			=    GitProcessor.from_pretrained(
              self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=snake_case			,							padding_value=1.0					)

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

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







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    self.prepare_image_inputs()

          __UpperCamelCase			=    image_processor(snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(images=snake_case			,							return_tensors='''np'''					)

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







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    '''lower newer'''

          __UpperCamelCase			=    processor(text=snake_case					)

          __UpperCamelCase			=    tokenizer(snake_case			,							return_token_type_ids=snake_case					)

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







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

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

          __UpperCamelCase			=    processor(text=snake_case			,							images=snake_case					)

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

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







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

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

          __UpperCamelCase			=    processor.batch_decode(snake_case					)
          __UpperCamelCase			=    tokenizer.batch_decode(snake_case					)

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

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

          __UpperCamelCase			=    processor(text=snake_case			,							images=snake_case					)

          # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
          self.assertListEqual(list(inputs.keys()					)			,							['''input_ids''', '''attention_mask''', '''pixel_values''']					)












import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






