





'''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)

_SCREAMING_SNAKE_CASE   =      logging.getLogger()




def 				_lowerCAmelCase  (			):
			__lowercase			  =							argparse.ArgumentParser()
			parser.add_argument('''-f'''			)
			__lowercase			  =							parser.parse_args()
			return args.f




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int			):
			__lowercase			  =							{}
			__lowercase			  =							os.path.join(lowerCamelCase_						,    '''all_results.json'''			)
			if os.path.exists(lowerCamelCase_			):
						with open(lowerCamelCase_						,    '''r'''			) as f:
									__lowercase			  =							json.load(lowerCamelCase_			)
			else:
						raise ValueError(f"can't find {path}"			)
			return results




def 				_lowerCAmelCase  (			):
			__lowercase			  =							torch.cuda.is_available() and torch_device == '''cuda'''
			return is_using_cuda and is_apex_available()


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






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

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







				'''simple docstring'''





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


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







				'''simple docstring'''





				shutil.rmtree(cls.tmpdir	)


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







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				self.assertGreaterEqual(result['''eval_accuracy''']       ,0.7_5	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''epoch_0'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''glue_no_trainer'''	)	)	)


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







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				self.assertLess(result['''perplexity''']       ,100	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''epoch_0'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''clm_no_trainer'''	)	)	)


	@mock.patch.dict(os.environ       ,{'''WANDB_MODE''': '''offline'''}	)
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				self.assertLess(result['''perplexity''']       ,42	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''epoch_0'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''mlm_no_trainer'''	)	)	)


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







				'''simple docstring'''





				__lowercase			  =							7 if get_gpu_count() > 1 else 2

				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				self.assertGreaterEqual(result['''eval_accuracy''']       ,0.7_5	)
				self.assertLess(result['''train_loss''']       ,0.5	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''epoch_0'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''ner_no_trainer'''	)	)	)


	@unittest.skip(reason='''Fix me @muellerzr'''	)
	@mock.patch.dict(os.environ       ,{'''WANDB_MODE''': '''offline'''}	)
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				# 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(_lowerCamelCase       ,'''epoch_0'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''qa_no_trainer'''	)	)	)


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







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				self.assertGreaterEqual(result['''eval_accuracy''']       ,0.8	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''swag_no_trainer'''	)	)	)


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







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				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(_lowerCamelCase       ,'''epoch_0'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''summarization_no_trainer'''	)	)	)


	@slow
	@mock.patch.dict(os.environ       ,{'''WANDB_MODE''': '''offline'''}	)
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				self.assertGreaterEqual(result['''eval_bleu''']       ,30	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''epoch_0'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''translation_no_trainer'''	)	)	)


	@slow
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							logging.StreamHandler(sys.stdout	)
				logger.addHandler(_lowerCamelCase	)

				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				self.assertGreaterEqual(result['''eval_overall_accuracy''']       ,0.1_0	)


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







				'''simple docstring'''





				__lowercase			  =							self.get_auto_remove_tmp_dir()
				__lowercase			  =							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	)
				__lowercase			  =							get_results(_lowerCamelCase	)
				# The base model scores a 25%
				self.assertGreaterEqual(result['''eval_accuracy''']       ,0.6	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''step_1'''	)	)	)
				self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase       ,'''image_classification_no_trainer'''	)	)	)












'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''BlenderbotTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





