
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_						: List[str]         =  logging.getLogger()







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







def        A__					(						lowerCamelCase						)  ->	int:
    UpperCamelCase_:							List[str]	=						{}
    UpperCamelCase_:							List[str]	=						os.path.join(lowerCamelCase     ,    """all_results.json"""						)
    if os.path.exists(lowerCamelCase						):
        with open(lowerCamelCase     ,    """r"""						) as f:
            UpperCamelCase_:							int	=						json.load(lowerCamelCase						)
    else:
        raise ValueError(F'''can\'t find {path}'''						)
    return results







def        A__					(						)  ->	Any:
    UpperCamelCase_:							Tuple	=						torch.cuda.is_available() and torch_device == """cuda"""
    return is_using_cuda and is_apex_available()


lowerCamelCase_						: List[Any]         =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class        _UpperCamelCase				(					_A  ):






   '''simple docstring'''

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

   @classmethod
   def       lowerCAmelCase__	(       cls :       Optional[Any]      ):
       shutil.rmtree(cls.tmpdir      )

   @mock.patch.dict(os.environ						,	{"""WANDB_MODE""": """offline"""}      )
   def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
       UpperCamelCase_:							Dict	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							Optional[Any]	=						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      )
       UpperCamelCase_:							List[Any]	=						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 :       int      ):
       UpperCamelCase_:							Union[str, Any]	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							Dict	=						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      )
       UpperCamelCase_:							List[str]	=						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 :       int      ):
       UpperCamelCase_:							str	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							Tuple	=						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      )
       UpperCamelCase_:							str	=						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 :       Dict      ):
       # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
       UpperCamelCase_:							List[str]	=						7 if get_gpu_count() > 1 else 2

       UpperCamelCase_:							str	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							str	=						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      )
       UpperCamelCase_:							Union[str, Any]	=						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      ):
       UpperCamelCase_:							Dict	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							List[Any]	=						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      )
       UpperCamelCase_:							Optional[int]	=						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 :       List[Any]      ):
       UpperCamelCase_:							str	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							Tuple	=						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      )
       UpperCamelCase_:							Optional[int]	=						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 :       int      ):
       UpperCamelCase_:							Tuple	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							Tuple	=						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      )
       UpperCamelCase_:							List[str]	=						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 :       Any      ):
       UpperCamelCase_:							Optional[int]	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							Union[str, Any]	=						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      )
       UpperCamelCase_:							Optional[Any]	=						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 :       Dict      ):
       UpperCamelCase_:							List[Any]	=						logging.StreamHandler(sys.stdout      )
       logger.addHandler(snake_case_      )

       UpperCamelCase_:							List[str]	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							Optional[Any]	=						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      )
       UpperCamelCase_:							int	=						get_results(snake_case_      )
       self.assertGreaterEqual(result["""eval_overall_accuracy"""]						,	0.10      )




   @mock.patch.dict(os.environ						,	{"""WANDB_MODE""": """offline"""}      )
   def       lowerCAmelCase__	(       self :       Any      ):
       UpperCamelCase_:							Optional[int]	=						self.get_auto_remove_tmp_dir()
       UpperCamelCase_:							int	=						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      )
       UpperCamelCase_:							str	=						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"""      )      )      )








from manim import *

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						Rectangle(height=0.5						,	width=0.5      )
							UpperCamelCase_:							Dict	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0      )

							UpperCamelCase_:							Tuple	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Tuple	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Union[str, Any]	=						VGroup(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Optional[Any]	=						Text("""CPU"""						,	font_size=24      )
							UpperCamelCase_:							int	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							cpu.move_to([-2.5, -0.5, 0]      )
							self.add(snake_case_      )

							UpperCamelCase_:							Optional[int]	=						[mem.copy() for i in range(1      )]
							UpperCamelCase_:							Dict	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Optional[int]	=						Text("""GPU"""						,	font_size=24      )
							UpperCamelCase_:							Optional[int]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							gpu.align_to(snake_case_						,	snake_case_      )
							gpu.set_x(gpu.get_x() - 1      )

							self.add(snake_case_      )

							UpperCamelCase_:							Dict	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )

							UpperCamelCase_:							Any	=						Text("""Model"""						,	font_size=24      )
							UpperCamelCase_:							Optional[Any]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							model.move_to([3, -1.0, 0]      )

							self.play(
							    Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	)

							UpperCamelCase_:							List[Any]	=						MarkupText(
							    f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''						,	font_size=24						,	)

							UpperCamelCase_:							Optional[Any]	=						Square(side_length=2.2      )
							key.move_to([-5, 2, 0]      )

							UpperCamelCase_:							Union[str, Any]	=						MarkupText(
							    f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''						,	font_size=18						,	)

							key_text.move_to([-5, 2.4, 0]      )

							step_a.move_to([2, 2, 0]      )
							self.play(Write(snake_case_						,	run_time=2.5      )						,	Write(snake_case_      )						,	Write(snake_case_      )      )

							self.add(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Tuple	=						[]
							for i, rect in enumerate(snake_case_      ):
											UpperCamelCase_:							Tuple	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0.0      ).set_fill(snake_case_						,	opacity=0.7      )
											cpu_target.move_to(snake_case_      )
											cpu_target.generate_target()
											UpperCamelCase_:							int	=						0.46 / 4
											UpperCamelCase_:							Optional[int]	=						0.46 / 3

											if i == 0:
															cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )						,	buff=0.02						,	direction=snake_case_      )
															cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
											elif i == 3:
															cpu_target.target.next_to(cpu_targs[0].target						,	direction=snake_case_						,	buff=0.0      )
											else:
															cpu_target.target.next_to(cpu_targs[i - 1].target						,	direction=snake_case_						,	buff=0.0      )
											cpu_targs.append(snake_case_      )

											first_animations.append(rect.animate(run_time=0.5      ).set_stroke(snake_case_      )      )
											second_animations.append(MoveToTarget(snake_case_						,	run_time=1.5      )      )

							self.play(*snake_case_      )
							self.play(*snake_case_      )

							self.wait()






