
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)

_lowerCamelCase	:    Dict          =      logging.getLogger()






def 	a_		(	)			->  Tuple:
			_snake_case          =      argparse.ArgumentParser()
			parser.add_argument('-f'			)
			_snake_case          =      parser.parse_args()
			return args.f






def 	a_		(	__lowercase       :     Optional[int]			)			->  List[Any]:
			_snake_case          =      {}
			_snake_case          =      os.path.join(_UpperCAmelCase	, 'all_results.json'			)
			if os.path.exists(_UpperCAmelCase			):
						with open(_UpperCAmelCase	, 'r'			) as f:
									_snake_case          =      json.load(_UpperCAmelCase			)
			else:
						raise ValueError(f'''can\'t find {path}'''			)
			return results






def 	a_		(	)			->  Union[str, Any]:
			_snake_case          =      torch.cuda.is_available() and torch_device == 'cuda'
			return is_using_cuda and is_apex_available()


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




class        SCREAMING_SNAKE_CASE__					(     SCREAMING_SNAKE_CASE_ ):





	'''simple docstring'''



	@classmethod
	def 			A							(  cls     :   Any     ):


				'''simple docstring'''


				_snake_case          =      tempfile.mkdtemp()
				_snake_case          =      os.path.join(cls.tmpdir      ,  'default_config.yml'     )
				write_basic_config(save_location=cls.configPath     )
				_snake_case          =      ['accelerate', 'launch', '--config_file', cls.configPath]



	@classmethod
	def 			A							(  cls     :   List[str]     ):


				'''simple docstring'''


				shutil.rmtree(cls.tmpdir     )



	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

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

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				self.assertGreaterEqual(result['eval_accuracy']      ,  0.75     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'epoch_0'     )     )     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'glue_no_trainer'     )     )     )



	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.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     )
				_snake_case          =      get_results(__a     )
				self.assertLess(result['perplexity']      ,  100     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'epoch_0'     )     )     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'clm_no_trainer'     )     )     )



	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				self.assertLess(result['perplexity']      ,  42     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'epoch_0'     )     )     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'mlm_no_trainer'     )     )     )



	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      7 if get_gpu_count() > 1 else 2

				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				self.assertGreaterEqual(result['eval_accuracy']      ,  0.75     )
				self.assertLess(result['train_loss']      ,  0.5     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'epoch_0'     )     )     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'ner_no_trainer'     )     )     )



	@unittest.skip(reason='Fix me @muellerzr'     )
	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				# 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(__a      ,  'epoch_0'     )     )     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'qa_no_trainer'     )     )     )



	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				self.assertGreaterEqual(result['eval_accuracy']      ,  0.8     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'swag_no_trainer'     )     )     )



	@slow
	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				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(__a      ,  'epoch_0'     )     )     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'summarization_no_trainer'     )     )     )



	@slow
	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/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}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				self.assertGreaterEqual(result['eval_bleu']      ,  30     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'epoch_0'     )     )     )
				self.assertTrue(os.path.exists(os.path.join(__a      ,  'translation_no_trainer'     )     )     )



	@slow
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      logging.StreamHandler(sys.stdout     )
				logger.addHandler(__a     )

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

				run_command(self._launch_args + testargs     )
				_snake_case          =      get_results(__a     )
				self.assertGreaterEqual(result['eval_overall_accuracy']      ,  0.10     )





	@mock.patch.dict(os.environ      ,  {'WANDB_MODE': 'offline'}     )
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      self.get_auto_remove_tmp_dir()
				_snake_case          =      f'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

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

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

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)