



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        __lowerCAmelCase		(	)	->    str:
					__lowerCAmelCase						 =argparse.ArgumentParser()
					parser.add_argument("""-f"""						)
					__lowerCAmelCase						 =parser.parse_args()
					return args.f






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






def        __lowerCAmelCase		(	)	->    Dict:
					__lowerCAmelCase						 =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     __a			(  SCREAMING_SNAKE_CASE					):



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



	@classmethod
	def 		UpperCamelCase				(    cls				:     Union[str, Any])->				Optional[int]:
						shutil.rmtree(cls.tmpdir)



	@mock.patch.dict(os.environ					,						{"""WANDB_MODE""": """offline"""})
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				List[Any]:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =get_results(snake_case_)
						self.assertGreaterEqual(result["""eval_accuracy"""]					,						0.7_5)
						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 		UpperCamelCase				(    self				:     int)->				Optional[int]:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =get_results(snake_case_)
						self.assertLess(result["""perplexity"""]					,						1_00)
						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 		UpperCamelCase				(    self				:     List[Any])->				Union[str, Any]:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     Tuple)->				Tuple:
						# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
						__lowerCAmelCase						 =7 if get_gpu_count() > 1 else 2

						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =get_results(snake_case_)
						self.assertGreaterEqual(result["""eval_accuracy"""]					,						0.7_5)
						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 		UpperCamelCase				(    self				:     Tuple)->				List[str]:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     str)->				Optional[int]:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     int)->				Tuple:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     str)->				Union[str, Any]:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     Any)->				Optional[int]:
						__lowerCAmelCase						 =logging.StreamHandler(sys.stdout)
						logger.addHandler(snake_case_)

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



	@mock.patch.dict(os.environ					,						{"""WANDB_MODE""": """offline"""})
	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						__lowerCAmelCase						 =self.get_auto_remove_tmp_dir()
						__lowerCAmelCase						 =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)
						__lowerCAmelCase						 =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     __a			(  SCREAMING_SNAKE_CASE					):



	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						__lowerCAmelCase						 =Rectangle(height=0.5					,						width=0.5)
						__lowerCAmelCase						 =Rectangle(height=0.4_6					,						width=0.4_6).set_stroke(width=0)

						__lowerCAmelCase						 =[mem.copy() for i in range(6)]
						__lowerCAmelCase						 =[mem.copy() for i in range(6)]
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =VGroup(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =Text("""CPU"""					,						font_size=24)
						__lowerCAmelCase						 =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_)

						__lowerCAmelCase						 =[mem.copy() for i in range(1)]
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =Text("""GPU"""					,						font_size=24)
						__lowerCAmelCase						 =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_)

						__lowerCAmelCase						 =[mem.copy() for i in range(6)]
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)

						__lowerCAmelCase						 =Text("""Model"""					,						font_size=24)
						__lowerCAmelCase						 =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)					,						)

						__lowerCAmelCase						 =MarkupText(
						    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""					,						font_size=24					,						)

						__lowerCAmelCase						 =Square(side_length=2.2)
						key.move_to([-5, 2, 0])

						__lowerCAmelCase						 =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_)

						__lowerCAmelCase						 =[]
						__lowerCAmelCase						 =[]
						__lowerCAmelCase						 =[]
						for i, rect in enumerate(snake_case_):
											__lowerCAmelCase						 =Rectangle(height=0.4_6					,						width=0.4_6).set_stroke(width=0.0).set_fill(snake_case_					,						opacity=0.7)
											cpu_target.move_to(snake_case_)
											cpu_target.generate_target()
											__lowerCAmelCase						 =0.4_6 / 4
											__lowerCAmelCase						 =0.4_6 / 3

											if i == 0:
																cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)					,						buff=0.0_2					,						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()




