
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


SCREAMING_SNAKE_CASE							:List[str]							 =				[
    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)

SCREAMING_SNAKE_CASE							:List[str]							 =				logging.getLogger()


def 						UpperCAmelCase			(	)		->	List[str]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_="eval"						)		->	Dict:







				"""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}'''						)


SCREAMING_SNAKE_CASE							:Any							 =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Any ):
					__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(A							,"argv"							,A ):
									run_flax_glue.main()
									__A   			= get_results(A )
									self.assertGreaterEqual(result["eval_accuracy"]							,0.75 )






	@slow
	def 				UpperCamelCase_  (       self					: List[str] ):
					__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(A							,"argv"							,A ):
									run_clm_flax.main()
									__A   			= get_results(A )
									self.assertLess(result["eval_perplexity"]							,1_00 )






	@slow
	def 				UpperCamelCase_  (       self					: int ):
					__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(A							,"argv"							,A ):
									run_summarization_flax.main()
									__A   			= get_results(A							,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					: int ):
					__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(A							,"argv"							,A ):
									run_mlm_flax.main()
									__A   			= get_results(A )
									self.assertLess(result["eval_perplexity"]							,42 )






	@slow
	def 				UpperCamelCase_  (       self					: 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(A							,"argv"							,A ):
									run_ta_mlm_flax.main()
									__A   			= get_results(A )
									self.assertGreaterEqual(result["eval_accuracy"]							,0.42 )






	@slow
	def 				UpperCamelCase_  (       self					: 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(A							,"argv"							,A ):
									run_flax_ner.main()
									__A   			= get_results(A )
									self.assertGreaterEqual(result["eval_accuracy"]							,0.75 )
									self.assertGreaterEqual(result["eval_f1"]							,0.3 )







	@slow
	def 				UpperCamelCase_  (       self					: int ):
					__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(A							,"argv"							,A ):
									run_qa.main()
									__A   			= get_results(A )
									self.assertGreaterEqual(result["eval_f1"]							,30 )
									self.assertGreaterEqual(result["eval_exact"]							,30 )




'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .multicontrolnet import MultiControlNetModel
		from .pipeline_controlnet import StableDiffusionControlNetPipeline
		from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
		from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
		from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline