




import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__A    :				Optional[Any]    						=	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm
		import run_image_classification
		import run_mlm
		import run_ner
		import run_qa as run_squad
		import run_summarization
		import run_swag
		import run_text_classification
		import run_translation


logging.basicConfig(level=logging.DEBUG)

__A    :				Optional[int]    						=	logging.getLogger()
def        lowerCamelCase_     (     ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       argparse.ArgumentParser()
			parser.add_argument("""-f""" )
			SCREAMING_SNAKE_CASE     =       parser.parse_args()
			return args.f
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       {}
			SCREAMING_SNAKE_CASE     =       os.path.join(SCREAMING_SNAKE_CASE       ,						"""all_results.json""" )
			if os.path.exists(SCREAMING_SNAKE_CASE ):
						with open(SCREAMING_SNAKE_CASE       ,						"""r""" ) as f:
									SCREAMING_SNAKE_CASE     =       json.load(SCREAMING_SNAKE_CASE )
			else:
						raise ValueError(f"""can't find {path}""" )
			return results
def        lowerCamelCase_     (     ):
			'''simple docstring'''
			return bool(tf.config.list_physical_devices("""GPU""" ) )


__A    :				str    						=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""      )
							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       str:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

										if is_cuda_available():
													testargs.append("""--fp16"""      )

										with patch.object(a							,  """argv"""							,  a      ):
													run_text_classification.main()
													# Reset the mixed precision policy so we don't break other tests
													tf.keras.mixed_precision.set_global_policy("""float32"""      )
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertGreaterEqual(result["""eval_accuracy"""]							,  0.75      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

										if len(tf.config.list_physical_devices("""GPU"""      )      ) > 1:
													# Skipping because there are not enough batches to train the model + would need a drop_last to work.
													return

										with patch.object(a							,  """argv"""							,  a      ):
													run_clm.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertLess(result["""eval_perplexity"""]							,  100      )





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

										with patch.object(a							,  """argv"""							,  a      ):
													run_mlm.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertLess(result["""eval_perplexity"""]							,  42      )





							def  _UpperCAmelCase  (       self							:						Any      )      ->       Dict:
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										SCREAMING_SNAKE_CASE     =       7 if get_gpu_count() > 1 else 2

										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

										with patch.object(a							,  """argv"""							,  a      ):
													run_ner.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertGreaterEqual(result["""accuracy"""]							,  0.75      )





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(a							,  """argv"""							,  a      ):
													run_squad.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertGreaterEqual(result["""f1"""]							,  30      )
													self.assertGreaterEqual(result["""exact"""]							,  30      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(a							,  """argv"""							,  a      ):
													run_swag.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertGreaterEqual(result["""val_accuracy"""]							,  0.8      )





							@slow
							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(a							,  """argv"""							,  a      ):
													run_summarization.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertGreaterEqual(result["""rouge1"""]							,  10      )
													self.assertGreaterEqual(result["""rouge2"""]							,  2      )
													self.assertGreaterEqual(result["""rougeL"""]							,  7      )
													self.assertGreaterEqual(result["""rougeLsum"""]							,  7      )





							@slow
							def  _UpperCAmelCase  (       self							:						str      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

										with patch.object(a							,  """argv"""							,  a      ):
													run_translation.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertGreaterEqual(result["""bleu"""]							,  30      )







							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE     =       f"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

										with patch.object(a							,  """argv"""							,  a      ):
													run_image_classification.main()
													SCREAMING_SNAKE_CASE     =       get_results(a      )
													self.assertGreaterEqual(result["""accuracy"""]							,  0.7      )












import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			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 "ff1" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff1"""       ,						"""intermediate.dense""" )
			if "ff2" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff2"""       ,						"""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 "cls" not in orig_key:
						SCREAMING_SNAKE_CASE     =       """nystromformer.""" + orig_key

			return orig_key
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

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

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

			return orig_state_dict
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.load(SCREAMING_SNAKE_CASE       ,						map_location="""cpu""" )["""model_state_dict"""]
			SCREAMING_SNAKE_CASE     =       NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       NystromformerForMaskedLM(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       convert_checkpoint_helper(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

			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__":
		__A    :				List[str]    						=	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    :				Union[str, Any]    						=	parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






