



"""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)

lowercase__			   =  logging.getLogger()





def       __magic_name__    (   ):
							__a		:		Optional[Any]									=       argparse.ArgumentParser()
							parser.add_argument("""-f"""					)
							__a		:		Dict									=       parser.parse_args()
							return args.f





def       __magic_name__    (   _lowerCamelCase				: Any					):
							__a		:		Tuple									=       {}
							__a		:		Optional[int]									=       os.path.join(__UpperCamelCase  ,       """all_results.json"""					)
							if os.path.exists(__UpperCamelCase					):
														with open(__UpperCamelCase  ,       """r"""					) as f:
																					__a		:		Optional[int]									=       json.load(__UpperCamelCase					)
							else:
														raise ValueError(F'''can\'t find {path}'''					)
							return results





def       __magic_name__    (   ):
							__a		:		str									=       torch.cuda.is_available() and torch_device == '''cuda'''
							return is_using_cuda and is_apex_available()


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







class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE_      ):



						@classmethod
						def     lowerCAmelCase__(cls							):





													'''simple docstring'''

													__a		:		Any									=       tempfile.mkdtemp()
													__a		:		List[Any]									=       os.path.join(cls.tmpdir						,     """default_config.yml"""							)
													write_basic_config(save_location=cls.configPath							)
													__a		:		int									=       ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



						@classmethod
						def     lowerCAmelCase__(cls							):





													'''simple docstring'''

													shutil.rmtree(cls.tmpdir							)



						@mock.patch.dict(os.environ						,     {"""WANDB_MODE""": """offline"""}							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.get_auto_remove_tmp_dir()
													__a		:		Optional[int]									=       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							)
													__a		:		Tuple									=       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							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.get_auto_remove_tmp_dir()
													__a		:		int									=       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							)
													__a		:		Optional[Any]									=       get_results(UpperCamelCase__							)
													self.assertLess(result["""perplexity"""]						,     100							)
													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							):





													'''simple docstring'''

													__a		:		Tuple									=       self.get_auto_remove_tmp_dir()
													__a		:		str									=       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							)
													__a		:		Optional[int]									=       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							):





													'''simple docstring'''

													__a		:		Optional[int]									=       7 if get_gpu_count() > 1 else 2

													__a		:		Dict									=       self.get_auto_remove_tmp_dir()
													__a		:		Dict									=       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							)
													__a		:		str									=       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							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.get_auto_remove_tmp_dir()
													__a		:		Any									=       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							)
													__a		:		Tuple									=       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							):





													'''simple docstring'''

													__a		:		Optional[int]									=       self.get_auto_remove_tmp_dir()
													__a		:		Dict									=       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							)
													__a		:		List[Any]									=       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							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.get_auto_remove_tmp_dir()
													__a		:		Tuple									=       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							)
													__a		:		Tuple									=       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							):





													'''simple docstring'''

													__a		:		Any									=       self.get_auto_remove_tmp_dir()
													__a		:		Optional[int]									=       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							)
													__a		:		Optional[int]									=       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							):





													'''simple docstring'''

													__a		:		List[Any]									=       logging.StreamHandler(sys.stdout							)
													logger.addHandler(UpperCamelCase__							)

													__a		:		Optional[int]									=       self.get_auto_remove_tmp_dir()
													__a		:		List[Any]									=       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							)
													__a		:		Dict									=       get_results(UpperCamelCase__							)
													self.assertGreaterEqual(result["""eval_overall_accuracy"""]						,     0.10							)





						@mock.patch.dict(os.environ						,     {"""WANDB_MODE""": """offline"""}							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.get_auto_remove_tmp_dir()
													__a		:		List[Any]									=       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							)
													__a		:		Dict									=       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"""							)							)							)






"""simple docstring"""

from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=0							):





													'''simple docstring'''

													__a		:		Any									=       1.0 if scale is None else scale
													__a		:		str									=       0.0 if loc is None else loc

													super().__init__(_lowercase						,     [AffineTransform(loc=self.loc						,     scale=self.scale						,     event_dim=_lowercase							)]							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.base_dist.mean * self.scale + self.loc



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.base_dist.variance * self.scale**2





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.variance.sqrt()







class SCREAMING_SNAKE_CASE__					(		nn.Module      ):



						def __init__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     **_lowercase							):





													'''simple docstring'''

													super().__init__(**_lowercase							)
													__a		:		str									=       args_dim
													__a		:		List[Any]									=       nn.ModuleList([nn.Linear(_lowercase						,     _lowercase							) for dim in args_dim.values()]							)
													__a		:		Dict									=       domain_map





						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		List[Any]									=       [proj(_lowercase							) for proj in self.proj]

													return self.domain_map(*_lowercase							)







class SCREAMING_SNAKE_CASE__					(		nn.Module      ):



						def __init__(self						,     _lowercase							):





													'''simple docstring'''

													super().__init__()
													__a		:		Optional[int]									=       function





						def     lowerCAmelCase__(self						,     _lowercase						,     *_lowercase							):





													'''simple docstring'''

													return self.function(_lowercase						,     *_lowercase							)







class SCREAMING_SNAKE_CASE__					:
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42



						def __init__(self						,     _lowercase = 1							):





													'''simple docstring'''

													__a		:		Optional[int]									=       dim
													__a		:		str									=       {k: dim * self.args_dim[k] for k in self.args_dim}



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													if self.dim == 1:
																				return self.distribution_class(*_lowercase							)
													else:
																				return Independent(self.distribution_class(*_lowercase							)						,     1							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None						,     ):





													'''simple docstring'''

													__a		:		Tuple									=       self._base_distribution(_lowercase							)
													if loc is None and scale is None:
																				return distr
													else:
																				return AffineTransformed(_lowercase						,     loc=_lowercase						,     scale=_lowercase						,     event_dim=self.event_dim							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return () if self.dim == 1 else (self.dim,)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return len(self.event_shape							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 0.0



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													return ParameterProjection(
													    in_features=_lowercase						,     args_dim=self.args_dim						,     domain_map=LambdaLayer(self.domain_map							)						,     )



						def     lowerCAmelCase__(self						,     *_lowercase							):





													'''simple docstring'''

													raise NotImplementedError()





						@staticmethod
						def     lowerCAmelCase__(_lowercase							):





													'''simple docstring'''

													return (x + torch.sqrt(torch.square(_lowercase							) + 4.0							)) / 2.0







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"df": 1, "loc": 1, "scale": 1}
						_lowerCAmelCase							=					StudentT



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		int									=       cls.squareplus(_lowercase							).clamp_min(torch.finfo(scale.dtype							).eps							)
													__a		:		Optional[Any]									=       2.0 + cls.squareplus(_lowercase							)
													return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"loc": 1, "scale": 1}
						_lowerCAmelCase							=					Normal



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		str									=       cls.squareplus(_lowercase							).clamp_min(torch.finfo(scale.dtype							).eps							)
													return loc.squeeze(-1							), scale.squeeze(-1							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"total_count": 1, "logits": 1}
						_lowerCAmelCase							=					NegativeBinomial



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       cls.squareplus(_lowercase							)
													return total_count.squeeze(-1							), logits.squeeze(-1							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a							,     __a		:		Optional[Any]									=       distr_args
													if self.dim == 1:
																				return self.distribution_class(total_count=_lowercase						,     logits=_lowercase							)
													else:
																				return Independent(self.distribution_class(total_count=_lowercase						,     logits=_lowercase							)						,     1							)





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None							):





													'''simple docstring'''

													__a							,     __a		:		List[Any]									=       distr_args

													if scale is not None:
																				# See scaling property of Gamma.
																				logits += scale.log()

													return self._base_distribution((total_count, logits)							)

