

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def 				__a							(       __A						):





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

    return orig_key





def 				__a							(       __A			,							__A						):





    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__         =    orig_state_dict.pop(__A						)

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

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

    return orig_state_dict





def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    torch.load(__A			,							map_location="cpu"						)['''model_state_dict''']
    SCREAMING_SNAKE_CASE__         =    MraConfig.from_json_file(__A						)
    SCREAMING_SNAKE_CASE__         =    MraForMaskedLM(__A						)

    SCREAMING_SNAKE_CASE__         =    convert_checkpoint_helper(config.max_position_embeddings			,							__A						)

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

    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)










import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil






@pytest.fixture
def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Union[str, Any]:







							"""simple docstring"""





							_a  :    Tuple			=						tmp_path / '''text.txt'''
							_a  :    Dict			=						textwrap.dedent(
							    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        '''     )
							with open(UpperCAmelCase    ,	'''w'''    ,	encoding='''utf-8'''     ) as f:
														f.write(UpperCAmelCase     )
							return str(UpperCAmelCase     )






@pytest.fixture
def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[int]:







							"""simple docstring"""





							_a  :    List[Any]			=						tmp_path / '''text_with_image.txt'''
							with open(UpperCAmelCase    ,	'''w'''    ,	encoding='''utf-8'''     ) as f:
														f.write(UpperCAmelCase     )
							return str(UpperCAmelCase     )






@pytest.mark.parametrize('''keep_linebreaks'''    ,	[True, False]     )
def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Any:







							"""simple docstring"""





							with open(UpperCAmelCase    ,	encoding='''utf-8'''     ) as f:
														_a  :    str			=						f.read().splitlines(keepends=UpperCAmelCase     )
							_a  :    Tuple			=						Text(keep_linebreaks=UpperCAmelCase    ,	encoding='''utf-8'''     )
							_a  :    Optional[Any]			=						text._generate_tables([[text_file]]     )
							_a  :    str			=						pa.concat_tables([table for _, table in generator]     ).to_pydict()['''text''']
							assert generated_content == expected_content






@require_pil
def    UpperCamelCase__  (   UpperCAmelCase     )     ->		List[str]:







							"""simple docstring"""





							with open(UpperCAmelCase    ,	encoding='''utf-8'''     ) as f:
														_a  :    str			=						f.read().splitlines()[0]
							_a  :    Dict			=						Text(encoding='''utf-8'''    ,	features=Features({'''image''': Image()}     )     )
							_a  :    str			=						text._generate_tables([[text_file_with_image]]     )
							_a  :    int			=						pa.concat_tables([table for _, table in generator]     )
							assert pa_table.schema.field('''image'''     ).type == Image()()
							_a  :    Optional[int]			=						pa_table.to_pydict()['''image''']
							assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize('''sample_by'''    ,	['''line''', '''paragraph''', '''document''']     )
def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[Any]:







							"""simple docstring"""





							with open(UpperCAmelCase    ,	encoding='''utf-8'''     ) as f:
														_a  :    Any			=						f.read()
							if sample_by == "line":
														_a  :    Any			=						expected_content.splitlines()
							elif sample_by == "paragraph":
														_a  :    Tuple			=						expected_content.split('''\n\n'''     )
							elif sample_by == "document":
														_a  :    Dict			=						[expected_content]
							_a  :    Dict			=						Text(sample_by=UpperCAmelCase    ,	encoding='''utf-8'''    ,	chunksize=100     )
							_a  :    Optional[int]			=						text._generate_tables([[text_file]]     )
							_a  :    List[Any]			=						pa.concat_tables([table for _, table in generator]     ).to_pydict()['''text''']
							assert generated_content == expected_content