




"""simple docstring"""


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)

a_       							=    logging.getLogger()






def      a__ (				)	->	List[str]:
				_A					  =			argparse.ArgumentParser()
				parser.add_argument("-f"   )
				_A					  =			parser.parse_args()
				return args.f






def      a__ (				__lowercase   )	->	Tuple:
				_A					  =			{}
				_A					  =			os.path.join(__lowercase     ,			"all_results.json"   )
				if os.path.exists(__lowercase   ):
								with open(__lowercase     ,			"r"   ) as f:
												_A					  =			json.load(__lowercase   )
				else:
								raise ValueError(f"""can\'t find {path}"""   )
				return results






def      a__ (				)	->	str:
				_A					  =			torch.cuda.is_available() and torch_device == "cuda"
				return is_using_cuda and is_apex_available()


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




class snake_case      (  UpperCamelCase__):







		@classmethod
		def  a_ (					cls      :       Dict		)       ->						str:





						'''simple docstring'''







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







		@classmethod
		def  a_ (					cls      :       List[str]		)       ->						List[Any]:





						'''simple docstring'''







						shutil.rmtree(cls.tmpdir		)







		@mock.patch.dict(os.environ    ,   {"WANDB_MODE": "offline"}		)
		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			get_results(a__		)
						self.assertGreaterEqual(result["eval_accuracy"]    ,   0.7_5		)
						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      :       Tuple		)       ->						str:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			get_results(a__		)
						self.assertLess(result["perplexity"]    ,   1_00		)
						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      :       Any		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			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      :       Dict		)       ->						str:





						'''simple docstring'''







						_A					  =			7 if get_gpu_count() > 1 else 2

						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			get_results(a__		)
						self.assertGreaterEqual(result["eval_accuracy"]    ,   0.7_5		)
						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      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			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      :       Tuple		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			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      :       Union[str, Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			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      :       Optional[int]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			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      :       Dict		)       ->						Any:





						'''simple docstring'''







						_A					  =			logging.StreamHandler(sys.stdout		)
						logger.addHandler(a__		)

						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			get_results(a__		)
						self.assertGreaterEqual(result["eval_overall_accuracy"]    ,   0.1_0		)







		@mock.patch.dict(os.environ    ,   {"WANDB_MODE": "offline"}		)
		def  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			self.get_auto_remove_tmp_dir()
						_A					  =			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		)
						_A					  =			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"		)		)		)



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "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[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

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

				return model


if __name__ == "__main__":
		a_       							=    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")
		a_       							=    parser.parse_args()
		a_       							=    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)