
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 )


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
