




'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 			_a  (	__a ):
       __a				:     Any   				=					(PNDMScheduler,)
       __a				:     Any   				=					(("""num_inference_steps""", 50),)
       def     A      (	self						:	Dict   ,				**lowercase						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**lowercase				)
           return config
       def     A      (	self						:	Dict   ,				lowercase						:	Dict=0   ,				**lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)
           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)
           UpperCAmelCase			       =		self.dummy_sample
           UpperCAmelCase			       =		0.1 * sample
           UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.get_scheduler_config(**lowercase				)
               UpperCAmelCase			       =		scheduler_class(**lowercase				)
               scheduler.set_timesteps(lowercase				)
               # copy over dummy past residuals
               UpperCAmelCase			       =		dummy_past_residuals[:]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase				)
                   UpperCAmelCase			       =		scheduler_class.from_pretrained(lowercase				)
                   new_scheduler.set_timesteps(lowercase				)
                   # copy over dummy past residuals
                   UpperCAmelCase			       =		dummy_past_residuals[:]

               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any   ,				lowercase						:	Optional[Any]=0   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)
           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)
           UpperCAmelCase			       =		self.dummy_sample
           UpperCAmelCase			       =		0.1 * sample
           UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)
               scheduler.set_timesteps(lowercase				)

               # copy over dummy past residuals (must be after setting timesteps)
               UpperCAmelCase			       =		dummy_past_residuals[:]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase				)
                   UpperCAmelCase			       =		scheduler_class.from_pretrained(lowercase				)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(lowercase				)

                   # copy over dummy past residual (must be after setting timesteps)
                   UpperCAmelCase			       =		dummy_past_residuals[:]

               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
       def     A      (	self						:	Optional[Any]   ,				**lowercase						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config(**lowercase				)
           UpperCAmelCase			       =		scheduler_class(**lowercase				)

           UpperCAmelCase			       =		10
           UpperCAmelCase			       =		self.dummy_model()
           UpperCAmelCase			       =		self.dummy_sample_deter
           scheduler.set_timesteps(lowercase				)

           for i, t in enumerate(scheduler.prk_timesteps				):
               UpperCAmelCase			       =		model(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase				).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps				):
               UpperCAmelCase			       =		model(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase				).prev_sample

           return sample
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)

           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)

               UpperCAmelCase			       =		self.dummy_sample
               UpperCAmelCase			       =		0.1 * sample

               if num_inference_steps is not None and hasattr(lowercase   ,				'''set_timesteps'''				):
                   scheduler.set_timesteps(lowercase				)
               elif num_inference_steps is not None and not hasattr(lowercase   ,				'''set_timesteps'''				):
                   UpperCAmelCase			       =		num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               UpperCAmelCase			       =		dummy_past_residuals[:]

               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				0   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				1   ,				lowercase   ,				**lowercase				).prev_sample

               self.assertEqual(output_a.shape   ,				sample.shape				)
               self.assertEqual(output_a.shape   ,				output_a.shape				)

               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				0   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				1   ,				lowercase   ,				**lowercase				).prev_sample

               self.assertEqual(output_a.shape   ,				sample.shape				)
               self.assertEqual(output_a.shape   ,				output_a.shape				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowercase				)

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config(steps_offset=1				)
           UpperCAmelCase			       =		scheduler_class(**lowercase				)
           scheduler.set_timesteps(10				)
           assert torch.equal(
               scheduler.timesteps   ,				torch.LongTensor(
                   [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)   ,				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           for beta_start, beta_end in zip([0.0001, 0.001]   ,				[0.002, 0.02]				):
               self.check_over_configs(beta_start=lowercase   ,				beta_end=lowercase				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowercase				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           for t in [1, 5, 10]:
               self.check_over_forward(time_step=lowercase				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           for t, num_inference_steps in zip([1, 5, 10]   ,				[10, 50, 100]				):
               self.check_over_forward(num_inference_steps=lowercase				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		27

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.dummy_sample
               UpperCAmelCase			       =		0.1 * sample

               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)

               scheduler.set_timesteps(lowercase				)

               # before power of 3 fix, would error on first step, so we only need to do two
               for i, t in enumerate(scheduler.prk_timesteps[:2]				):
                   UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase				).prev_sample
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           with self.assertRaises(lowercase				):
               UpperCAmelCase			       =		self.scheduler_classes[0]
               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)

               scheduler.step_plms(self.dummy_sample   ,				1   ,				self.dummy_sample				).prev_sample
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop()
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_sum.item() - 198.1318				) < 1E-2
           assert abs(result_mean.item() - 0.2580				) < 1E-3
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(prediction_type='''v_prediction'''				)
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_sum.item() - 67.3986				) < 1E-2
           assert abs(result_mean.item() - 0.0878				) < 1E-3
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(set_alpha_to_one=lowercase   ,				beta_start=0.01				)
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_sum.item() - 230.0399				) < 1E-2
           assert abs(result_mean.item() - 0.2995				) < 1E-3







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(set_alpha_to_one=lowercase   ,				beta_start=0.01				)
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_sum.item() - 186.9482				) < 1E-2
           assert abs(result_mean.item() - 0.2434				) < 1E-3










'''simple docstring'''







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


A     =[
    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)

A     =logging.getLogger()





def 						snake_case_							():
    UpperCAmelCase			       =		argparse.ArgumentParser()
    parser.add_argument('''-f'''     )
    UpperCAmelCase			       =		parser.parse_args()
    return args.f





def 						snake_case_							(_a	:							List[str]    ,   _a	:							Union[str, Any]="eval"     ):
    UpperCAmelCase			       =		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}"     )


A     =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 			_a  (	__a ):
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_flax_glue.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.75				)
       @slow
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_clm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				100				)
       @slow
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_summarization_flax.main()
               UpperCAmelCase			       =		get_results(lowercase   ,				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     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_ta_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		7 if get_gpu_count() > 1 else 2

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_flax_ner.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.75				)
               self.assertGreaterEqual(result['''eval_f1''']   ,				0.3				)







       @slow
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_qa.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_f1''']   ,				30				)
               self.assertGreaterEqual(result['''eval_exact''']   ,				30				)




