
import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowercase__							:							List[str]													=     [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-generation",
        "text-classification",
        "token-classification",
        "language-modeling",
        "multiple-choice",
        "question-answering",
        "summarization",
        "translation",
        "image-classification",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
				import run_clm
				import run_image_classification
				import run_mlm
				import run_ner
				import run_qa as run_squad
				import run_summarization
				import run_swag
				import run_text_classification
				import run_translation


logging.basicConfig(level=logging.DEBUG)

lowercase__							:							str													=     logging.getLogger()




def 		A_	(							) ->      str:



						'''simple docstring'''

						__UpperCamelCase       					=     argparse.ArgumentParser()
						parser.add_argument('''-f'''	)
						__UpperCamelCase       					=     parser.parse_args()
						return args.f




def 		A_	(							snake_case							:				Dict	) ->      str:



						'''simple docstring'''

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




def 		A_	(							) ->      Any:



						'''simple docstring'''

						return bool(tf.config.list_physical_devices('''GPU'''	)	)


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






class     SCREAMING_SNAKE_CASE__      (							lowercase__      ):

							"""simple docstring"""






							@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''     )
							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													if is_cuda_available():
																			testargs.append('''--fp16'''     )

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_text_classification.main()
																			# Reset the mixed precision policy so we don't break other tests
																			tf.keras.mixed_precision.set_global_policy('''float32'''     )
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertGreaterEqual(result['''eval_accuracy''']       ,		0.7_5     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

													if len(tf.config.list_physical_devices('''GPU'''     )     ) > 1:
																			# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																			return

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_clm.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertLess(result['''eval_perplexity''']       ,		100     )



							def       A__						(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_mlm.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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        ".split()

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_mlm.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertLess(result['''eval_perplexity''']       ,		42     )



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     7 if get_gpu_count() > 1 else 2

													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\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        ".split()

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_ner.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertGreaterEqual(result['''accuracy''']       ,		0.7_5     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_qa.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            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_squad.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertGreaterEqual(result['''f1''']       ,		30     )
																			self.assertGreaterEqual(result['''exact''']       ,		30     )



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





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_swag.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            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_swag.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertGreaterEqual(result['''val_accuracy''']       ,		0.8     )



							@slow
							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_summarization.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            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_summarization.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertGreaterEqual(result['''rouge1''']       ,		10     )
																			self.assertGreaterEqual(result['''rouge2''']       ,		2     )
																			self.assertGreaterEqual(result['''rougeL''']       ,		7     )
																			self.assertGreaterEqual(result['''rougeLsum''']       ,		7     )



							@slow
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_translation.py\n            --model_name_or_path Rocketknight1/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            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\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        ".split()

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_translation.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertGreaterEqual(result['''bleu''']       ,		30     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_auto_remove_tmp_dir()
													__UpperCamelCase       					=     F"\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            ".split()

													with patch.object(lowerCAmelCase__       ,		'''argv'''       ,		lowerCAmelCase__     ):
																			run_image_classification.main()
																			__UpperCamelCase       					=     get_results(lowerCAmelCase__     )
																			self.assertGreaterEqual(result['''accuracy''']       ,		0.7     )






from typing import Any

import numpy as np




def 		A_	(							snake_case							:				np.ndarray	) ->      bool:



						'''simple docstring'''

						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[1]

						# If matrix not symmetric, exit right away.
						if np.allclose(snake_case	, matrix.T	) is False:
												return False

						# Get eigenvalues and eignevectors for a symmetric matrix.
						__UpperCamelCase					,							__UpperCamelCase       					=     np.linalg.eigh(snake_case	)

						# Check sign of all eigenvalues.
						# np.all returns a value of type np.bool_
						return bool(np.all(eigen_values > 0	)	)




def 		A_	(							snake_case							:				int	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     np.random.randn(snake_case	, snake_case	)
						__UpperCamelCase       					=     np.dot(snake_case	, random_matrix.T	)
						assert _is_matrix_spd(snake_case	)
						return spd_matrix




def 		A_	(							snake_case							:				np.ndarray	, snake_case							:				np.ndarray	, snake_case							:				int = 1000	, snake_case							:				float = 1e-8	, ) ->      Any:



						'''simple docstring'''

						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[1]
						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[0]
						assert _is_matrix_spd(snake_case	)

						# Initialize solution guess, residual, search direction.
						__UpperCamelCase       					=     np.zeros((np.shape(snake_case	)[0], 1)	)
						__UpperCamelCase       					=     np.copy(snake_case	)
						__UpperCamelCase       					=     np.copy(snake_case	)

						# Set initial errors in solution guess and residual.
						__UpperCamelCase       					=     1e9
						__UpperCamelCase       					=     1e9
						__UpperCamelCase       					=     1e9

						# Set iteration counter to threshold number of iterations.
						__UpperCamelCase       					=     0

						while error > tol:
												# Save this value so we only calculate the matrix-vector product once.
												__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

												# The main algorithm.

												# Update search direction magnitude.
												__UpperCamelCase       					=     np.dot(ra.T	, snake_case	) / np.dot(pa.T	, snake_case	)
												# Update solution guess.
												__UpperCamelCase       					=     xa + alpha * pa
												# Calculate new residual.
												__UpperCamelCase       					=     ra - alpha * w
												# Calculate new Krylov subspace scale.
												__UpperCamelCase       					=     np.dot(r.T	, snake_case	) / np.dot(ra.T	, snake_case	)
												# Calculate new A conjuage search direction.
												__UpperCamelCase       					=     r + beta * pa

												# Calculate errors.
												__UpperCamelCase       					=     np.linalg.norm(r - ra	)
												__UpperCamelCase       					=     np.linalg.norm(x - xa	)
												__UpperCamelCase       					=     np.maximum(snake_case	, snake_case	)

												# Update variables.
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)

												# Update number of iterations.
												iterations += 1
												if iterations > max_iterations:
																		break

						return x




def 		A_	(							) ->      None:



						'''simple docstring'''

						__UpperCamelCase       					=     3
						__UpperCamelCase       					=     _create_spd_matrix(snake_case	)
						__UpperCamelCase       					=     np.random.randn(snake_case	, 1	)
						__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

						# Numpy solution.
						__UpperCamelCase       					=     np.linalg.solve(snake_case	, snake_case	)

						# Our implementation.
						__UpperCamelCase       					=     conjugate_gradient(snake_case	, snake_case	)

						# Ensure both solutions are close to x_true (and therefore one another).
						assert np.linalg.norm(x_numpy - x_true	) <= 1e-6
						assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1e-6


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				test_conjugate_gradient()




