




'''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							:       Optional[Any]						=			logging.getLogger()


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				argparse.ArgumentParser()
							parser.add_argument("-f"					)
							snake_case_      =				parser.parse_args()
							return args.f


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				{}
							snake_case_      =				os.path.join(lowerCAmelCase_							,    "all_results.json"					)
							if os.path.exists(lowerCAmelCase_					):
														with open(lowerCAmelCase_							,    "r"					) as f:
																					snake_case_      =				json.load(lowerCAmelCase_					)
							else:
														raise ValueError(f'can\'t find {path}'					)
							return results


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				torch.cuda.is_available() and torch_device == 'cuda'
							return is_using_cuda and is_apex_available()


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




class 			_snake_case		(     _UpperCamelCase							):




			@classmethod
			def        lowerCAmelCase__    ( cls							)    ->    int:

										'''simple docstring'''




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




			@classmethod
			def        lowerCAmelCase__    ( cls							)    ->    Optional[int]:

										'''simple docstring'''




										shutil.rmtree(cls.tmpdir							)




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

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				get_results(_UpperCAmelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]    ,							0.7_5							)
										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							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				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							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				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							)    ->    str:

										'''simple docstring'''




										snake_case_      =				7 if get_gpu_count() > 1 else 2

										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				get_results(_UpperCAmelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]    ,							0.7_5							)
										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							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				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							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				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							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				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							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				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							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				logging.StreamHandler(sys.stdout							)
										logger.addHandler(_UpperCAmelCase							)

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




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

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				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							)
										snake_case_      =				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 __future__ import annotations

from scipy.special import comb  # type: ignore




class 			_snake_case		:




			def __init__( self    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										snake_case_      =				len(a__							) - 1




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    list[float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										snake_case_      =				[]
										for i in range(len(self.list_of_points							)							):
																	# basis function for each i
																	output_values.append(
																	    comb(self.degree    ,							a__							) * ((1 - t) ** (self.degree - i)) * (t**i)							)
										# the basis must sum up to 1 for it to produce a valid Bezier curve.
										assert round(sum(a__							)    ,							5							) == 1
										return output_values




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    tuple[float, float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										snake_case_      =				self.basis_function(a__							)
										snake_case_      =				0.0
										snake_case_      =				0.0
										for i in range(len(self.list_of_points							)							):
																	# For all points, sum up the product of i-th basis function and i-th point.
																	x += basis_function[i] * self.list_of_points[i][0]
																	y += basis_function[i] * self.list_of_points[i][1]
										return (x, y)




			def        lowerCAmelCase__    ( self    ,							a__ = 0.0_1							)    ->    Any:

										'''simple docstring'''




										from matplotlib import pyplot as plt  # type: ignore

										snake_case_      =				[]  # x coordinates of points to plot
										snake_case_      =				[]  # y coordinates of points to plot

										snake_case_      =				0.0
										while t <= 1:
																	snake_case_      =				self.bezier_curve_function(a__							)
																	to_plot_x.append(value[0]							)
																	to_plot_y.append(value[1]							)
																	t += step_size

										snake_case_      =				[i[0] for i in self.list_of_points]
										snake_case_      =				[i[1] for i in self.list_of_points]

										plt.plot(
										    a__    ,							a__    ,							color="blue"    ,							label="Curve of Degree " + str(self.degree							)    ,							)
										plt.scatter(a__    ,							a__    ,							color="red"    ,							label="Control Points"							)
										plt.legend()
										plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
		BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
		BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3




