

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def  snake_case_(_UpperCamelCase	)				->      str:



  """simple docstring"""






  if "model" in orig_key:
    _snake_case		=				orig_key.replace('''model.'''					,			''''''	)
  if "norm1" in orig_key:
    _snake_case		=				orig_key.replace('''norm1'''					,			'''attention.output.LayerNorm'''	)
  if "norm2" in orig_key:
    _snake_case		=				orig_key.replace('''norm2'''					,			'''output.LayerNorm'''	)
  if "norm" in orig_key:
    _snake_case		=				orig_key.replace('''norm'''					,			'''LayerNorm'''	)
  if "transformer" in orig_key:
    _snake_case		=				orig_key.split('''.'''	)[0].split('''_'''	)[-1]
    _snake_case		=				orig_key.replace(F"""transformer_{layer_num}"""					,			F"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    _snake_case		=				orig_key.replace('''mha.attn'''					,			'''attention.self'''	)
  if "mha" in orig_key:
    _snake_case		=				orig_key.replace('''mha'''					,			'''attention'''	)
  if "W_q" in orig_key:
    _snake_case		=				orig_key.replace('''W_q'''					,			'''self.query'''	)
  if "W_k" in orig_key:
    _snake_case		=				orig_key.replace('''W_k'''					,			'''self.key'''	)
  if "W_v" in orig_key:
    _snake_case		=				orig_key.replace('''W_v'''					,			'''self.value'''	)
  if "ff1" in orig_key:
    _snake_case		=				orig_key.replace('''ff1'''					,			'''intermediate.dense'''	)
  if "ff2" in orig_key:
    _snake_case		=				orig_key.replace('''ff2'''					,			'''output.dense'''	)
  if "ff" in orig_key:
    _snake_case		=				orig_key.replace('''ff'''					,			'''output.dense'''	)
  if "mlm_class" in orig_key:
    _snake_case		=				orig_key.replace('''mlm.mlm_class'''					,			'''cls.predictions.decoder'''	)
  if "mlm" in orig_key:
    _snake_case		=				orig_key.replace('''mlm'''					,			'''cls.predictions.transform'''	)
  if "cls" not in orig_key:
    _snake_case		=				'''nystromformer.''' + orig_key

  return orig_key



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Any:



  """simple docstring"""






  for key in orig_state_dict.copy().keys():
    _snake_case		=				orig_state_dict.pop(_lowerCAmelCase	)

    if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
      continue
    else:
      _snake_case		=				val

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

  return orig_state_dict



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				torch.load(_lowerCAmelCase					,			map_location='''cpu'''	)['''model_state_dict''']
  _snake_case		=				NystromformerConfig.from_json_file(_lowerCAmelCase	)
  _snake_case		=				NystromformerForMaskedLM(_lowerCAmelCase	)

  _snake_case		=				convert_checkpoint_helper(_lowerCAmelCase					,			_lowerCAmelCase	)

  model.load_state_dict(_lowerCAmelCase	)
  model.eval()
  model.save_pretrained(_lowerCAmelCase	)

  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 Nystromformer pytorch checkpoint.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The json file for Nystromformer 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_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				lowercase__     =    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									if isinstance(model.config  ,			snake_case__     ):
														UpperCamelCase_	,     UpperCamelCase_			= list(tokenizer.lang_code_to_id.keys()     )[:2]
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang=snake_case__  ,			tgt_lang=snake_case__     )
									else:
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__     )
									return translator, ["Some string", "Some other text"]

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= translator("Some string"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string", "other string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}, {"translation_text": ANY(snake_case__     )}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)




class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation_cn_to_ar"     )

									# but we do for this one
									UpperCamelCase_			= pipeline(task="translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )
									UpperCamelCase_			= MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )

									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__     )
									# Missing src_lang, tgt_lang
									with self.assertRaises(snake_case__     ):
														translator("This is a test"     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="hi_IN"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "यह एक परीक्षण है"}]     )

									# src_lang, tgt_lang can be defined at pipeline call time
									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									UpperCamelCase_			= translator("This is a test"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									UpperCamelCase_			= pipeline(task="translation_cn_to_ar"  ,			model=snake_case__     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"cn"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"ar"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									with pytest.warns(snake_case__  ,			match=R".*translation_en_to_de.*"     ):
														UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__     )
									self.assertEqual(translator.task  ,			"translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation"     )
