




import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
     from tests.trainer.test_trainer import (  # noqa
         RegressionModelConfig,
         RegressionPreTrainedModel,
         get_regression_trainer,
     )


set_seed(42)

__magic_name__           =					get_tests_dir('''fixtures''')
__magic_name__           =					os.path.join(dirname(get_tests_dir()))
__magic_name__           =					dirname(os.path.abspath(__file__))

# default torch.distributed port
__magic_name__           =					'''10999'''

__magic_name__           =					'''t5-small'''

# *** Working Models ***
__magic_name__           =					'''hf-internal-testing/tiny-albert'''
__magic_name__           =					'''sshleifer/bart-tiny-random'''
__magic_name__           =					'''hf-internal-testing/tiny-bert'''
__magic_name__           =					'''hf-internal-testing/tiny-random-bigbird_pegasus'''
__magic_name__           =					'''hf-internal-testing/tiny-random-big_bird'''
__magic_name__           =					'''hf-internal-testing/tiny-random-blenderbot'''
__magic_name__           =					'''bigscience/bigscience-small-testing'''
__magic_name__           =					'''hf-internal-testing/tiny-random-deberta'''
__magic_name__           =					'''hf-internal-testing/tiny-random-deberta-v2'''
__magic_name__           =					'''sshleifer/tiny-distilbert-base-cased'''
__magic_name__           =					'''hf-internal-testing/tiny-electra'''
__magic_name__           =					'''hf-internal-testing/tiny-random-flaubert'''
__magic_name__           =					'''stas/tiny-wmt19-en-de'''
__magic_name__           =					'''hf-internal-testing/tiny-random-funnel'''
__magic_name__           =					'''sshleifer/tiny-gpt2'''
__magic_name__           =					'''hf-internal-testing/tiny-random-gptj'''
__magic_name__           =					'''hf-internal-testing/tiny-random-gpt_neo'''
__magic_name__           =					'''hf-internal-testing/tiny-layoutlm'''
__magic_name__           =					'''hf-internal-testing/tiny-random-led'''
__magic_name__           =					'''hf-internal-testing/tiny-random-longformer'''
__magic_name__           =					'''stas/tiny-m2m_100'''  # hf tiny model is unsuitable
__magic_name__           =					'''sshleifer/tiny-marian-en-de'''
__magic_name__           =					'''sshleifer/tiny-mbart'''
__magic_name__           =					'''hf-internal-testing/tiny-random-mobilebert'''
__magic_name__           =					'''hf-internal-testing/tiny-random-mpnet'''
__magic_name__           =					'''stas/pegasus-cnn_dailymail-tiny-random'''
__magic_name__           =					'''hf-internal-testing/tiny-random-prophetnet'''
__magic_name__           =					'''sshleifer/tiny-distilroberta-base'''
__magic_name__           =					'''hf-internal-testing/tiny-random-squeezebert'''
__magic_name__           =					'''patrickvonplaten/t5-tiny-random'''
__magic_name__           =					'''hf-internal-testing/tiny-random-t5-v1.1'''
__magic_name__           =					'''hf-internal-testing/tiny-random-vit'''
__magic_name__           =					'''hf-internal-testing/tiny-xlm-roberta'''
__magic_name__           =					'''sshleifer/tiny-xlnet-base-cased'''


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__magic_name__           =					'''hf-internal-testing/tiny-random-mt5'''
__magic_name__           =					'''hf-internal-testing/tiny-random-camembert'''
__magic_name__           =					'''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
__magic_name__           =					'''hf-internal-testing/tiny-random-convbert'''
__magic_name__           =					'''hf-internal-testing/tiny-random-layoutlmv2'''
__magic_name__           =					'''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
__magic_name__           =					'''hf-internal-testing/tiny-random-ctrl'''
__magic_name__           =					'''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
__magic_name__           =					'''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
__magic_name__           =					'''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
__magic_name__           =					'''hf-internal-testing/tiny-random-dpr'''
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__magic_name__           =					'''hf-internal-testing/tiny-random-rag'''
# - "rag" research_projects
__magic_name__           =					''''''
# - "luke" Entities classes - no plan to make such example
__magic_name__           =					'''hf-internal-testing/tiny-random-lxmert'''
# - "lxmert" doesn't work with run_qa.py
__magic_name__           =					'''hf-internal-testing/tiny-random-clip'''
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__magic_name__           =					'''hf-internal-testing/tiny-random-speech_to_text'''
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
__magic_name__           =					'''hf-internal-testing/tiny-random-tapas'''





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase=False							):
    snake_case__  =			min(2      ,						get_gpu_count()							) if distributed else 1
    snake_case__  =			os.environ.get("DS_TEST_PORT"      ,						_A							)
    return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()





def 		SCREAMING_SNAKE_CASE__      ( ):
    snake_case__  =			F"""{FIXTURE_DIRECTORY}/tests_samples"""
    snake_case__  =			F"""{data_dir_samples}/wmt_en_ro"""
    snake_case__  =			F"""{data_dir_samples}/xsum"""
    snake_case__  =			"\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".split()

    # try to cover as many models as possible once (it's enough to run on one task per model)
    # but need a tiny model for each
    #
    # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
    snake_case__  =			{
        "trans": [
            "bart",
            "fsmt",
            "m2m_100",
            "marian",
            "mbart",
            "t5",
            "t5_v1",
            # "mt5", missing model files
        ],
        "sum": [
            "pegasus",
        ],
        "clm": [
            "big_bird",
            "bigbird_pegasus",
            "blenderbot",
            "bloom",
            "gpt2",
            "gpt_neo",
            "gptj",
            "xlm-roberta",
            "prophetnet",
            # "camembert", missing model files
        ],
        "mlm": [
            "albert",
            "deberta",
            "deberta-v2",
            "distilbert",
            "electra",
            "flaubert",
            "funnel",
            "layoutlm",
            # "reformer", # multiple issues with either mlm/qa/clas
        ],
        "qa": [
            "led",
            "longformer",
            "mobilebert",
            "mpnet",
            "roberta",
            "squeezebert",
            # "convbert", # missing tokenizer files
            # "layoutlmv2", missing model files
        ],
        "clas": [
            "bert",
            "xlnet",
            # "hubert", # missing tokenizer files
            # "ibert", # multiple issues with either mlm/qa/clas
            # "transfo-xl", # tokenizer issues as ctrl
            # "ctrl", # tokenizer issues
            # "openai-gpt", missing model files
            # "tapas", multiple issues
        ],
        "img_clas": [
            "vit",
        ],
    }

    snake_case__  =			F"""{ROOT_DIRECTORY}/examples/pytorch"""

    snake_case__  =			{
        "trans": F"""
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        """,
        "sum": F"""
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        """,
        "clm": F"""
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        """,
        "mlm": F"""
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        """,
        "qa": F"""
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        """,
        "clas": F"""
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        """,
        "img_clas": F"""
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        """,
    }

    snake_case__  =			get_launcher(distributed=_A							)

    snake_case__  =			{}
    for task, args in tasks.items():
        snake_case__  =			args.split()
        for model in tasksamodels[task]:
            snake_case__  =			globals()[F"""{model.upper().replace("-"      ,						"_"							)}_TINY"""]
            snake_case__  =			F"""--model_name_or_path {model_name}""".split()
            snake_case__  =			launcher + args + args_model + args_main

            # # generation special case
            # if task == "gen":
            #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
            #     args_model += f"--model_type {model}".split()
            #     cmds[f"{task}_{model}"] = launcher + args + args_model
            # else:

    return cmds


__magic_name__           =					make_task_cmds()

__magic_name__           =					'''zero2'''
__magic_name__           =					'''zero3'''

__magic_name__           =					[ZEROa, ZEROa]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			parameterized.to_safe_name("_".join(str(_A							) for x in param.args							)							)
    return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__magic_name__           =					list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class    _SCREAMING_SNAKE_CASE					( lowerCamelCase__	):
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):






          if task not in task_cmds:
              raise ValueError(F"""don't know of task {task}, have {task_cmds.keys()}"""	)

          snake_case__  =			task_cmds[task]
          snake_case__  =			F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

          snake_case__  =			self.get_auto_remove_tmp_dir()
          snake_case__  =			F"""--output_dir {output_dir}""".split()

          cmd += args_ds + args_out

          return cmd, output_dir



      @parameterized.expand(lowercase_						,		name_func=lowercase_	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  ,   snake_case__  =			self.get_task_cmd(lowercase_						,		lowercase_	)

          # 1. generate the checkpoint
          cmd += "--save_steps 1".split()
          # keep for quick debug
          # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
          execute_subprocess_async(lowercase_						,		env=self.get_env()	)

          # 2. test that the fp32 weights get reconsolidated
          snake_case__  =			F"""{output_dir}/checkpoint-1"""
          snake_case__  =			F"""{chkpt_dir}/out.bin"""
          snake_case__  =			F"""{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"""
          # keep for quick debug
          # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
          subprocess.check_call(lowercase_						,		shell=lowercase_	)
          assert os.path.exists(lowercase_	), F"""{recovered_model_path} was not found"""

          # possibly could also test that the resulting saved model is usable but given that we use
          # random models we won't know if it's any good





from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    seed(1							)
    return [gauss(__lowerCAmelCase      ,						__lowerCAmelCase							) for _ in range(__lowerCAmelCase							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return [k for k in range(__lowerCAmelCase							) for _ in range(instance_count[k]							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return sum(__lowerCAmelCase							) / instance_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return instance_count / total_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(__lowerCAmelCase							)							):
        # for loop iterates over number of elements in inner layer of items
        for j in range(len(items[i]							)							):
            # appending squared differences to 'squared_diff' list
            squared_diff.append((items[i][j] - means[i]) ** 2							)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
    snake_case__  =			len(__lowerCAmelCase							)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]
    # for loop iterates over number of elements in list
    for i in range(len(__lowerCAmelCase							)							):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i]							)							):
            snake_case__  =			[]  # to store all discriminant values of each item as a list
            # for loop iterates over number of classes we have in our dataset
            for k in range(len(__lowerCAmelCase							)							):
                # appending values of discriminants for each class to 'temp' list
                temp.append(
                    x_items[i][j] * (means[k] / variance)
                    - (means[k] ** 2 / (2 * variance))
                    + log(probabilities[k]							)							)
            # appending discriminant values of each item to 'results' list
            results.append(__lowerCAmelCase							)

    return [result.index(max(__lowerCAmelCase							)							) for result in results]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			sum(1 for i, j in zip(__lowerCAmelCase      ,						__lowerCAmelCase							) if i == j							)
    # percentage of accuracy equals to number of correct predictions divided by number
    # of all data and multiplied by 100
    return (correct / len(__lowerCAmelCase							)) * 100


__magic_name__           =					TypeVar('''num''')





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = lambda __lowerCAmelCase							: True      ,						__lowerCAmelCase = None      ,						):
    while True:
        try:
            snake_case__  =			input_type(input(__lowerCAmelCase							).strip() or default							)
            if condition(__lowerCAmelCase							):
                return user_input
            else:
                print(F"""{user_input}: {err_msg}"""							)
                continue
        except ValueError:
            print(
                F"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""							)





def 		SCREAMING_SNAKE_CASE__      ( ):
    while True:
        print(" Linear Discriminant Analysis ".center(50      ,						"*"							)							)
        print("*" * 50      ,						"\n"							)
        print("First of all we should specify the number of classes that"							)
        print("we want to generate as training dataset"							)
        # Trying to get number of classes
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x > 0      ,						input_msg="Enter the number of classes (Data Groupings): "      ,						err_msg="Number of classes should be positive!"      ,						)

        print("-" * 100							)

        # Trying to get the value of standard deviation
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x >= 0      ,						input_msg=(
                "Enter the value of standard deviation"
                "(Default value is 1.0 for all classes): "
            )      ,						err_msg="Standard deviation should not be negative!"      ,						default="1.0"      ,						)

        print("-" * 100							)

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        snake_case__  =			[]  # An empty list to store instance counts of classes in dataset
        for i in range(__lowerCAmelCase							):
            snake_case__  =			valid_input(
                input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x > 0      ,						input_msg=(F"""Enter The number of instances for class_{i+1}: """)      ,						err_msg="Number of instances should be positive!"      ,						)
            counts.append(__lowerCAmelCase							)
        print("-" * 100							)

        # An empty list to store values of user-entered means of classes
        snake_case__  =			[]
        for a in range(__lowerCAmelCase							):
            snake_case__  =			valid_input(
                input_type=__lowerCAmelCase      ,						input_msg=(F"""Enter the value of mean for class_{a+1}: """)      ,						err_msg="This is an invalid value."      ,						)
            user_means.append(__lowerCAmelCase							)
        print("-" * 100							)

        print("Standard deviation: "      ,						__lowerCAmelCase							)
        # print out the number of instances in classes in separated line
        for i, count in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Number of instances in class_{i} is: {count}"""							)
        print("-" * 100							)

        # print out mean values of classes separated line
        for i, user_mean in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Mean of class_{i} is: {user_mean}"""							)
        print("-" * 100							)

        # Generating training dataset drawn from gaussian distribution
        snake_case__  =			[
            gaussian_distribution(user_means[j]      ,						__lowerCAmelCase      ,						counts[j]							)
            for j in range(__lowerCAmelCase							)
        ]
        print("Generated Normal Distribution: \n"      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Generating Ys to detecting corresponding classes
        snake_case__  =			y_generator(__lowerCAmelCase      ,						__lowerCAmelCase							)
        print("Generated Corresponding Ys: \n"      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Calculating the value of actual mean for each class
        snake_case__  =			[calculate_mean(counts[k]      ,						x[k]							) for k in range(__lowerCAmelCase							)]
        # for loop iterates over number of elements in 'actual_means' list and print
        # out them in separated line
        for i, actual_mean in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Actual(Real) mean of class_{i} is: {actual_mean}"""							)
        print("-" * 100							)

        # Calculating the value of probabilities for each class
        snake_case__  =			[
            calculate_probabilities(counts[i]      ,						sum(__lowerCAmelCase							)							) for i in range(__lowerCAmelCase							)
        ]

        # for loop iterates over number of elements in 'probabilities' list and print
        # out them in separated line
        for i, probability in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Probability of class_{i} is: {probability}"""							)
        print("-" * 100							)

        # Calculating the values of variance for each class
        snake_case__  =			calculate_variance(__lowerCAmelCase      ,						__lowerCAmelCase      ,						sum(__lowerCAmelCase							)							)
        print("Variance: "      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        snake_case__  =			predict_y_values(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Calculating Accuracy of the model
        print(F"""Accuracy: {accuracy(__lowerCAmelCase      ,						__lowerCAmelCase							)}"""							)
        print("-" * 100							)
        print(" DONE ".center(100      ,						"+"							)							)

        if input("Press any key to restart or 'q' for quit: "							).strip().lower() == "q":
            print("\n" + "GoodBye!".center(100      ,						"-"							) + "\n"							)
            break
        system("cls" if name == "nt" else "clear"							)  # noqa: S605


if __name__ == "__main__":
     main()



