
"""simple docstring"""





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class A_      (lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						BartTokenizer
  SCREAMING_SNAKE_CASE__     :		str    	=						BartTokenizerFast
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						True
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						filter_roberta_detectors







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     super().setUp()
     UpperCAmelCase_   :   Optional[int]     	=       [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     UpperCAmelCase_   :   Optional[int]     	=       dict(zip(lowercase_							,  range(len(lowercase_    )    )    )    )
     UpperCAmelCase_   :   Tuple     	=       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     UpperCAmelCase_   :   Tuple     	=       {"unk_token": "<unk>"}

     UpperCAmelCase_   :   int     	=       os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES["vocab_file"]    )
     UpperCAmelCase_   :   Optional[Any]     	=       os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES["merges_file"]    )
     with open(self.vocab_file							,  "w"							,  encoding="utf-8"    ) as fp:
        fp.write(json.dumps(lowercase_    ) + "\n"    )
     with open(self.merges_file							,  "w"							,  encoding="utf-8"    ) as fp:
        fp.write("\n".join(lowercase_    )    )







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map    )
     return self.tokenizer_class.from_pretrained(self.tmpdirname							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map    )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     return "lower newer", "lower newer"







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return BartTokenizer.from_pretrained("facebook/bart-large"    )







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return BartTokenizerFast.from_pretrained("facebook/bart-large"    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
     UpperCAmelCase_   :   int     	=       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   str     	=       tokenizer(lowercase_							,  max_length=len(lowercase_    )							,  padding=lowercase_							,  return_tensors="pt"    )
        self.assertIsInstance(lowercase_							,  lowercase_    )

        self.assertEqual((2, 9)							,  batch.input_ids.shape    )
        self.assertEqual((2, 9)							,  batch.attention_mask.shape    )
        UpperCAmelCase_   :   Union[str, Any]     	=       batch.input_ids.tolist()[0]
        self.assertListEqual(lowercase_							,  lowercase_    )
        # Test that special tokens are reset







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Dict     	=       tokenizer(lowercase_							,  padding=lowercase_							,  return_tensors="pt"    )
        # check if input_ids are returned and no labels
        self.assertIn("input_ids"							,  lowercase_    )
        self.assertIn("attention_mask"							,  lowercase_    )
        self.assertNotIn("labels"							,  lowercase_    )
        self.assertNotIn("decoder_attention_mask"							,  lowercase_    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       [
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   List[Any]     	=       tokenizer(text_target=lowercase_							,  max_length=32							,  padding="max_length"							,  return_tensors="pt"    )
        self.assertEqual(32							,  targets["input_ids"].shape[1]    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Optional[int]     	=       tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"]							,  padding=lowercase_							,  truncation=lowercase_							,  return_tensors="pt"    )
        self.assertIsInstance(lowercase_							,  lowercase_    )
        self.assertEqual(batch.input_ids.shape							,  (2, 1024)    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       ["A long paragraph for summarization."]
     UpperCAmelCase_   :   List[str]     	=       [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Any     	=       tokenizer(lowercase_							,  return_tensors="pt"    )
        UpperCAmelCase_   :   int     	=       tokenizer(text_target=lowercase_							,  return_tensors="pt"    )
        UpperCAmelCase_   :   Optional[int]     	=       inputs["input_ids"]
        UpperCAmelCase_   :   Any     	=       targets["input_ids"]
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
           UpperCAmelCase_   :   List[str]     	=       self.rust_tokenizer_class.from_pretrained(lowercase_							,  **lowercase_    )
           UpperCAmelCase_   :   Optional[int]     	=       self.tokenizer_class.from_pretrained(lowercase_							,  **lowercase_    )
           UpperCAmelCase_   :   Optional[Any]     	=       "A, <mask> AllenNLP sentence."
           UpperCAmelCase_   :   List[Any]     	=       tokenizer_r.encode_plus(lowercase_							,  add_special_tokens=lowercase_							,  return_token_type_ids=lowercase_    )
           UpperCAmelCase_   :   Optional[int]     	=       tokenizer_p.encode_plus(lowercase_							,  add_special_tokens=lowercase_							,  return_token_type_ids=lowercase_    )

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]    )							,  sum(tokens_p["token_type_ids"]    )    )

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    )							,  sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    )							,  )

           UpperCAmelCase_   :   Union[str, Any]     	=       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
           UpperCAmelCase_   :   Tuple     	=       tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p["input_ids"]							,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )
           self.assertSequenceEqual(tokens_r["input_ids"]							,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )

           self.assertSequenceEqual(
               lowercase_							,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )
           self.assertSequenceEqual(
               lowercase_							,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )




'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







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

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

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

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

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

		print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )


if __name__ == "__main__":
							__lowercase	=    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.'''
							)
							__lowercase	=    parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






