




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		:				Optional[Any]       			=		logging.getLogger()




def 			__magic_name__				( ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					Union[str, Any]   ):






			'''simple docstring'''







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




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			torch.cuda.is_available() and torch_device == "cuda"
			return is_using_cuda and is_apex_available()


__lowerCAmelCase		:				str       			=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



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





				@classmethod
				def 							__UpperCAmelCase							(      cls				:     Dict							)					->     List[Any]:
							shutil.rmtree(cls.tmpdir							)





				@mock.patch.dict(os.environ					,      {"WANDB_MODE": "offline"}							)
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     int:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			get_results(__lowerCamelCase							)
							self.assertGreaterEqual(result["eval_accuracy"]					,      0.75							)
							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				:     Tuple							)					->     Optional[int]:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			get_results(__lowerCamelCase							)
							self.assertLess(result["perplexity"]					,      1_00							)
							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				:     Any							)					->     Union[str, Any]:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			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				:     Optional[int]							)					->     List[str]:
							# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
							a    =			7 if get_gpu_count() > 1 else 2

							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			get_results(__lowerCamelCase							)
							self.assertGreaterEqual(result["eval_accuracy"]					,      0.75							)
							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				:     str							)					->     Union[str, Any]:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			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				:     str							)					->     Dict:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			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				:     Dict							)					->     Optional[Any]:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			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				:     Dict							)					->     List[Any]:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			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				:     Dict							)					->     int:
							a    =			logging.StreamHandler(sys.stdout							)
							logger.addHandler(__lowerCamelCase							)

							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			get_results(__lowerCamelCase							)
							self.assertGreaterEqual(result["eval_overall_accuracy"]					,      0.10							)





				@mock.patch.dict(os.environ					,      {"WANDB_MODE": "offline"}							)
				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[int]:
							a    =			self.get_auto_remove_tmp_dir()
							a    =			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							)
							a    =			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"							)							)							)












from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

			if rowsa != colsa:
						a    =			F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if colsa != 1:
						a    =			F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if rowsa != rowsa:
						a    =			(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
						)
						raise ValueError(A   )

			if len(A   ) != rowsa:
						a    =			(
						    "Number of initial values must be equal to number of rows in coefficient "
						    F"""matrix but received {len(A   )} and {rowsa}"""
						)
						raise ValueError(A   )

			if iterations <= 0:
						raise ValueError("Iterations must be at least 1"   )

			a    =			np.concatenate(
			    (coefficient_matrix, constant_matrix),  axis=1   )

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

			# Iterates the whole matrix for given number of times
			for _ in range(A   ):
						a    =			[]
						for row in range(A   ):
									a    =			0
									for col in range(A   ):
												if col == row:
															a    =			table[row][col]
												elif col == cols - 1:
															a    =			table[row][col]
												else:
															temp += (-1) * table[row][col] * init_val[col]
									a    =			(temp + val) / denom
									new_val.append(A   )
						a    =			new_val

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			0
						for j in range(0,  cols - 1   ):
									if i == j:
												continue
									else:
												total += table[i][j]

						if table[i][i] <= total:
									raise ValueError("Coefficient matrix is not strictly diagonally dominant"   )

			return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()






