




import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_									=	logging.getLogger()




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						argparse.ArgumentParser()
       parser.add_argument("""-f"""	)
       __lowerCamelCase   =						parser.parse_args()
       return args.f




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       __lowerCamelCase   =						{}
       __lowerCamelCase   =						os.path.join(_lowercase  ,			"""all_results.json"""	)
       if os.path.exists(_lowercase	):
              with open(_lowercase  ,			"""r"""	) as f:
                     __lowerCamelCase   =						json.load(_lowercase	)
       else:
              raise ValueError(f'can\'t find {path}'	)
       return results




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						torch.cuda.is_available() and torch_device == '''cuda'''
       return is_using_cuda and is_apex_available()


UpperCAmelCase_									=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class     lowerCamelCase__(					UpperCamelCase_):
      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[Any]   ):
             __lowerCamelCase   =						tempfile.mkdtemp()
             __lowerCamelCase   =						os.path.join(cls.tmpdir    ,       """default_config.yml"""   )
             write_basic_config(save_location=cls.configPath   )
             __lowerCamelCase   =						['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[Any]   ):
             shutil.rmtree(cls.tmpdir   )
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

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

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertGreaterEqual(result["""eval_accuracy"""]    ,       0.75   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """epoch_0"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """glue_no_trainer"""   )   )   )
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

             if torch.cuda.device_count() > 1:
                    # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                    return

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertLess(result["""perplexity"""]    ,       1_00   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """epoch_0"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """clm_no_trainer"""   )   )   )
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertLess(result["""perplexity"""]    ,       42   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """epoch_0"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """mlm_no_trainer"""   )   )   )
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						7 if get_gpu_count() > 1 else 2

             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertGreaterEqual(result["""eval_accuracy"""]    ,       0.75   )
             self.assertLess(result["""train_loss"""]    ,       0.5   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """epoch_0"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """ner_no_trainer"""   )   )   )
      @unittest.skip(reason="""Fix me @muellerzr"""   )
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
             self.assertGreaterEqual(result["""eval_f1"""]    ,       28   )
             self.assertGreaterEqual(result["""eval_exact"""]    ,       28   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """epoch_0"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """qa_no_trainer"""   )   )   )
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertGreaterEqual(result["""eval_accuracy"""]    ,       0.8   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """swag_no_trainer"""   )   )   )
      @slow
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertGreaterEqual(result["""eval_rouge1"""]    ,       10   )
             self.assertGreaterEqual(result["""eval_rouge2"""]    ,       2   )
             self.assertGreaterEqual(result["""eval_rougeL"""]    ,       7   )
             self.assertGreaterEqual(result["""eval_rougeLsum"""]    ,       7   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """epoch_0"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """summarization_no_trainer"""   )   )   )
      @slow
      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertGreaterEqual(result["""eval_bleu"""]    ,       30   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """epoch_0"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """translation_no_trainer"""   )   )   )
      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						logging.StreamHandler(sys.stdout   )
             logger.addHandler(UpperCamelCase__   )

             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             self.assertGreaterEqual(result["""eval_overall_accuracy"""]    ,       0.10   )




      @mock.patch.dict(os.environ    ,       {"""WANDB_MODE""": """offline"""}   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						self.get_auto_remove_tmp_dir()
             __lowerCamelCase   =						F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

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

             run_command(self._launch_args + testargs   )
             __lowerCamelCase   =						get_results(UpperCamelCase__   )
             # The base model scores a 25%
             self.assertGreaterEqual(result["""eval_accuracy"""]    ,       0.6   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """step_1"""   )   )   )
             self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,       """image_classification_no_trainer"""   )   )   )


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def       A	(    _lowercase						):
						if "model" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.replace('''model.'''		,  ''''''						)
						if "norm1" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''norm1'''		,  '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''norm2'''		,  '''output.LayerNorm'''						)
						if "norm" in orig_key:
												SCREAMING_SNAKE_CASE							:    Tuple							=							orig_key.replace('''norm'''		,  '''LayerNorm'''						)
						if "transformer" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												SCREAMING_SNAKE_CASE							:    List[str]							=							orig_key.replace(f"""transformer_{layer_num}"""		,  f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''mha.attn'''		,  '''attention.self'''						)
						if "mha" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mha'''		,  '''attention'''						)
						if "W_q" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''W_q'''		,  '''self.query'''						)
						if "W_k" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''W_k'''		,  '''self.key'''						)
						if "W_v" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''W_v'''		,  '''self.value'''						)
						if "ff1" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''ff1'''		,  '''intermediate.dense'''						)
						if "ff2" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''ff2'''		,  '''output.dense'''						)
						if "ff" in orig_key:
												SCREAMING_SNAKE_CASE							:    Dict							=							orig_key.replace('''ff'''		,  '''output.dense'''						)
						if "mlm_class" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mlm.mlm_class'''		,  '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''mlm'''		,  '''cls.predictions.transform'''						)
						if "cls" not in orig_key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							'''yoso.''' + orig_key

						return orig_key
def       A	(    _lowercase		,  _lowercase						):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							orig_state_dict.pop(_lowercase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							val

						SCREAMING_SNAKE_CASE							:    List[str]							=							orig_state_dict['''cls.predictions.decoder.bias''']
						SCREAMING_SNAKE_CASE							:    Dict							=							torch.arange(_lowercase						).expand((1, -1)						) + 2

						return orig_state_dict
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.load(_lowercase		,  map_location='''cpu'''						)['''model_state_dict''']
						SCREAMING_SNAKE_CASE							:    List[Any]							=							YosoConfig.from_json_file(_lowercase						)
						SCREAMING_SNAKE_CASE							:    str							=							YosoForMaskedLM(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							convert_checkpoint_helper(config.max_position_embeddings		,  _lowercase						)

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

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


if __name__ == "__main__":
					__UpperCamelCase		:       Tuple									=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					__UpperCamelCase		:       Optional[Any]									=     parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





