


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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__lowerCAmelCase	:       List[Any]     =	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm_flax
		import run_flax_glue
		import run_flax_ner
		import run_mlm_flax
		import run_qa
		import run_summarization_flax
		import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase	:       int     =	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],						A	:      Union[str, Any]="eval"     ):






			'''simple docstring'''







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


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






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



				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  str:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_glue.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
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_flax_glue.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]      ,   0.75							)





				@slow
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_clm_flax.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertLess(result["eval_perplexity"]      ,   1_00							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Dict:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_summarization.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
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_summarization_flax.main()
										a					=    get_results(__lowerCamelCase      ,   split="test"							)
										self.assertGreaterEqual(result["test_rouge1"]      ,   10							)
										self.assertGreaterEqual(result["test_rouge2"]      ,   2							)
										self.assertGreaterEqual(result["test_rougeL"]      ,   7							)
										self.assertGreaterEqual(result["test_rougeLsum"]      ,   7							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Tuple:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_mlm.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}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_mlm_flax.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertLess(result["eval_perplexity"]      ,   42							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Optional[Any]:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_ta_mlm_flax.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]      ,   0.42							)





				@slow
				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[Any]:
							# 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"""
            run_flax_ner.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}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_flax_ner.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]      ,   0.75							)
										self.assertGreaterEqual(result["eval_f1"]      ,   0.3							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  str:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_qa.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}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_qa.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_f1"]      ,   30							)
										self.assertGreaterEqual(result["eval_exact"]      ,   30							)












from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase	:       List[str]     =	[8, 5, 9, 7]
__lowerCAmelCase	:       str     =	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase	:       Optional[Any]     =	[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  list[int]:
							return [
							    sum(p_item[i] for p_item in self.__allocated_resources_table							)
							    for i in range(len(self.__allocated_resources_table[0]							)							)
							]





				def 							__UpperCAmelCase							(   self       :  str							)      ->  list[int]:
							return np.array(self.__claim_vector							) - np.array(
							    self.__processes_resource_summation()							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  list[list[int]]:
							return [
							    list(np.array(self.__maximum_claim_table[i]							) - np.array(__lowerCamelCase							)							)
							    for i, allocated_resource in enumerate(self.__allocated_resources_table							)
							]





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  dict[int, list[int]]:
							return {self.__need().index(__lowerCamelCase							): i for i in self.__need()}





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   **__lowerCamelCase       :  Any							)      ->  None:
							a					=    self.__need()
							a					=    self.__allocated_resources_table
							a					=    self.__available_resources()
							a					=    self.__need_index_manager()
							for kw, val in kwargs.items():
										if kw and val is True:
													self.__pretty_data()
							print("_" * 50 + "\n"							)
							while need_list:
										a					=    False
										for each_need in need_list:
													a					=    True
													for index, need in enumerate(__lowerCamelCase							):
																if need > available_resources[index]:
																			a					=    False
																			break
													if execution:
																a					=    True
																# get the original index of the process from ind_ctrl db
																for original_need_index, need_clone in need_index_manager.items():
																			if each_need == need_clone:
																						a					=    original_need_index
																print(f"""Process {process_number + 1} is executing."""							)
																# remove the process run from stack
																need_list.remove(__lowerCamelCase							)
																# update available/freed resources stack
																a					=    np.array(__lowerCamelCase							) + np.array(
																    alloc_resources_table[process_number]							)
																print(
																    "Updated available resource stack for processes: "
																    + " ".join([str(__lowerCamelCase							) for x in available_resources]							)							)
																break
										if safe:
													print("The process is in a safe state.\n"							)
										else:
													print("System in unsafe state. Aborting...\n"							)
													break





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							print(" " * 9 + "Allocated Resource Table"							)
							for item in self.__allocated_resources_table:
										print(
										    f"""P{self.__allocated_resources_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(" " * 9 + "System Resource Table"							)
							for item in self.__maximum_claim_table:
										print(
										    f"""P{self.__maximum_claim_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(
							    "Current Usage by Active Processes: "
							    + " ".join(str(__lowerCamelCase							) for x in self.__claim_vector							)							)
							print(
							    "Initial Available Resources:       "
							    + " ".join(str(__lowerCamelCase							) for x in self.__available_resources()							)							)
							time.sleep(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






