"""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 						SCREAMING_SNAKE_CASE					(  )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				argparse.ArgumentParser()
				parser.add_argument("-f"       )
				SCREAMING_SNAKE_CASE__		  =				parser.parse_args()
				return args.f




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCAmelCase  ,					"all_results.json"       )
				if os.path.exists(__UpperCAmelCase       ):
								with open(__UpperCAmelCase  ,					"r"       ) as f:
												SCREAMING_SNAKE_CASE__		  =				json.load(__UpperCAmelCase       )
				else:
								raise ValueError(F"""can't find {path}"""       )
				return results




def 						SCREAMING_SNAKE_CASE					(  )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@classmethod
					def 			lowerCAmelCase_					(						cls			:				List[str]		)							->       str:
									# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
									SCREAMING_SNAKE_CASE__		  =				tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE__		  =				os.path.join(cls.tmpdir   , "default_config.yml"		)
									write_basic_config(save_location=cls.configPath		)
									SCREAMING_SNAKE_CASE__		  =				["accelerate", "launch", "--config_file", cls.configPath]



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Union[str, Any]		)							->       Tuple:
									shutil.rmtree(cls.tmpdir		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.75		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "glue_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									self.assertLess(result["perplexity"]   , 100		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "clm_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									self.assertLess(result["perplexity"]   , 42		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "mlm_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[str]:
									# 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"""
            {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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.75		)
									self.assertLess(result["train_loss"]   , 0.5		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "ner_no_trainer"		)		)		)



					@unittest.skip(reason="Fix me @muellerzr"		)
					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									# 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(_snake_case   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "qa_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.8		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "swag_no_trainer"		)		)		)



					@slow
					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									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(_snake_case   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "summarization_no_trainer"		)		)		)



					@slow
					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				int		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									self.assertGreaterEqual(result["eval_bleu"]   , 30		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "translation_no_trainer"		)		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				logging.StreamHandler(sys.stdout		)
									logger.addHandler(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									self.assertGreaterEqual(result["eval_overall_accuracy"]   , 0.10		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_snake_case		)
									# The base model scores a 25%
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.6		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "step_1"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_snake_case   , "image_classification_no_trainer"		)		)		)



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




