



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def  _lowerCAmelCase     (_lowerCAmelCase):
					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 "ff1" in orig_key:
										UpperCamelCase_			= orig_key.replace("ff1" ,						"intermediate.dense")
					if "ff2" in orig_key:
										UpperCamelCase_			= orig_key.replace("ff2" ,						"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 "cls" not in orig_key:
										UpperCamelCase_			= "nystromformer." + orig_key

					return orig_key



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					for key in orig_state_dict.copy().keys():
										UpperCamelCase_			= orig_state_dict.pop(_lowerCAmelCase)

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

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

					return orig_state_dict



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= torch.load(_lowerCAmelCase ,						map_location="cpu")["model_state_dict"]
					UpperCamelCase_			= NystromformerConfig.from_json_file(_lowerCAmelCase)
					UpperCamelCase_			= NystromformerForMaskedLM(_lowerCAmelCase)

					UpperCamelCase_			= 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__":
	UpperCAmelCase :				Optional[Any]							=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."""
	)
	UpperCAmelCase :				Union[str, Any]							=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"     )
