




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


SCREAMING_SNAKE_CASE_    :       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)

SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							logging.getLogger()
def 							__lowerCAmelCase			():
    a__  			=							argparse.ArgumentParser()
    parser.add_argument("""-f"""		)
    a__  			=							parser.parse_args()
    return args.f
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							{}
    a__  			=							os.path.join(UpperCAmelCase_ ,     """all_results.json"""		)
    if os.path.exists(UpperCAmelCase_		):
        with open(UpperCAmelCase_ ,     """r"""		) as f:
            a__  			=							json.load(UpperCAmelCase_		)
    else:
        raise ValueError(F"""can't find {path}"""		)
    return results
def 							__lowerCAmelCase			():
    return bool(tf.config.list_physical_devices("""GPU"""		)		)


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







class 		__UpperCAmelCase(		__A			):





     """simple docstring"""



     @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""     )
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Tuple:






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

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

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      List[str]:






         """simple docstring"""
         a__  			=							self.get_auto_remove_tmp_dir()
         a__  			=							f"""
            run_clm.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 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.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(__lowercase    ,					"""argv"""    ,					__lowercase     ):
             run_clm.main()
             a__  			=							get_results(__lowercase     )
             self.assertLess(result["""eval_perplexity"""]    ,					1_00     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      Any:






         """simple docstring"""
         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
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

         with patch.object(__lowercase    ,					"""argv"""    ,					__lowercase     ):
             run_mlm.main()
             a__  			=							get_results(__lowercase     )
             self.assertLess(result["""eval_perplexity"""]    ,					42     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Dict:






         """simple docstring"""
         # 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_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
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

         with patch.object(__lowercase    ,					"""argv"""    ,					__lowercase     ):
             run_ner.main()
             a__  			=							get_results(__lowercase     )
             self.assertGreaterEqual(result["""accuracy"""]    ,					0.7_5     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      int:






         """simple docstring"""
         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
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

         with patch.object(__lowercase    ,					"""argv"""    ,					__lowercase     ):
             run_squad.main()
             a__  			=							get_results(__lowercase     )
             self.assertGreaterEqual(result["""f1"""]    ,					30     )
             self.assertGreaterEqual(result["""exact"""]    ,					30     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      str:






         """simple docstring"""
         a__  			=							self.get_auto_remove_tmp_dir()
         a__  			=							f"""
            run_swag.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}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

         with patch.object(__lowercase    ,					"""argv"""    ,					__lowercase     ):
             run_swag.main()
             a__  			=							get_results(__lowercase     )
             self.assertGreaterEqual(result["""val_accuracy"""]    ,					0.8     )

     @slow
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Union[str, Any]:






         """simple docstring"""
         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
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

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

     @slow
     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Any:






         """simple docstring"""
         a__  			=							self.get_auto_remove_tmp_dir()
         a__  			=							f"""
            run_translation.py
            --model_name_or_path Rocketknight1/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}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

         with patch.object(__lowercase    ,					"""argv"""    ,					__lowercase     ):
             run_translation.main()
             a__  			=							get_results(__lowercase     )
             self.assertGreaterEqual(result["""bleu"""]    ,					30     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Any:






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

         with patch.object(__lowercase    ,					"""argv"""    ,					__lowercase     ):
             run_image_classification.main()
             a__  			=							get_results(__lowercase     )
             self.assertGreaterEqual(result["""accuracy"""]    ,					0.7     )










'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

