


"""simple docstring"""





import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


snake_case__						:   Optional[Any]			 = '''sshleifer/mar_enro_6_3_student'''



class 						snake_case_(				a__	):






     def    lowerCamelCase__   (		self     :    List[Any] ):
           super().setUp()

           lowerCAmelCase    :		Optional[Any]				=      cached_path(
               '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''    , extract_compressed_file=UpperCamelCase_    , )
           lowerCAmelCase    :		List[Any]				=      F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






     @slow
     @require_torch_gpu
     def    lowerCamelCase__   (		self     :    Any ):
           MarianMTModel.from_pretrained(UpperCamelCase_ )






     @slow
     @require_torch_gpu
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		List[str]				=      {
               '''$MAX_LEN''': 6_4,
               '''$BS''': 6_4,
               '''$GAS''': 1,
               '''$ENRO_DIR''': self.data_dir,
               '''facebook/mbart-large-cc25''': MARIAN_MODEL,
               # "val_check_interval=0.25": "val_check_interval=1.0",
               '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
               '''--num_train_epochs 6''': '''--num_train_epochs 1''',
           }

           # Clean up bash script
           lowerCAmelCase    :		Tuple				=      (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''' )[1].strip()
           lowerCAmelCase    :		Any				=      bash_script.replace('''\\\n'''    , '''''' ).strip().replace('''"$@"'''    , '''''' )
           for k, v in env_vars_to_replace.items():
                 lowerCAmelCase    :		Any				=      bash_script.replace(UpperCamelCase_    , str(UpperCamelCase_ ) )
           lowerCAmelCase    :		Optional[Any]				=      self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           lowerCAmelCase    :		List[Any]				=      F'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
           # XXX: args.gpus > 1 : handle multi_gpu in the future

           lowerCAmelCase    :		Union[str, Any]				=      ['''finetune.py'''] + bash_script.split() + args
           with patch.object(UpperCamelCase_    , '''argv'''    , UpperCamelCase_ ):
                 lowerCAmelCase    :		Dict				=      argparse.ArgumentParser()
                 lowerCAmelCase    :		Dict				=      pl.Trainer.add_argparse_args(UpperCamelCase_ )
                 lowerCAmelCase    :		Optional[int]				=      SummarizationModule.add_model_specific_args(UpperCamelCase_    , os.getcwd() )
                 lowerCAmelCase    :		List[Any]				=      parser.parse_args()
                 lowerCAmelCase    :		Union[str, Any]				=      main(UpperCamelCase_ )

           # Check metrics
           lowerCAmelCase    :		str				=      load_json(model.metrics_save_path )
           lowerCAmelCase    :		Any				=      metrics['''val'''][0]
           lowerCAmelCase    :		Optional[Any]				=      metrics['''val'''][-1]
           self.assertEqual(len(metrics['''val'''] )    , (args.max_epochs / args.val_check_interval) )
           assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']    , UpperCamelCase_ )

           self.assertGreater(last_step_stats['''val_avg_gen_time''']    , 0.01 )
           # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
           self.assertLessEqual(last_step_stats['''val_avg_gen_time''']    , 1.0 )

           # test learning requirements:

           # 1. BLEU improves over the course of training by more than 2 pts
           self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']    , 2 )

           # 2. BLEU finishes above 17
           self.assertGreater(last_step_stats['''val_avg_bleu''']    , 1_7 )

           # 3. test BLEU and val BLEU within ~1.1 pt.
           self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu'''] )    , 1.1 )

           # check lightning ckpt can be loaded and has a reasonable statedict
           lowerCAmelCase    :		List[Any]				=      os.listdir(UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      [x for x in contents if x.endswith('''.ckpt''' )][0]
           lowerCAmelCase    :		Optional[int]				=      os.path.join(args.output_dir    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      torch.load(UpperCamelCase_    , map_location='''cpu''' )
           lowerCAmelCase    :		List[str]				=      '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
           assert expected_key in ckpt["state_dict"]
           assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

           # TODO: turn on args.do_predict when PL bug fixed.
           if args.do_predict:
                 lowerCAmelCase    :		str				=      {os.path.basename(UpperCamelCase_ ) for p in contents}
                 assert "test_generations.txt" in contents
                 assert "test_results.txt" in contents
                 # assert len(metrics["val"]) ==  desired_n_evals
                 assert len(metrics['''test'''] ) == 1



class 						snake_case_(				a__	):






     @timeout_decorator.timeout(6_0_0 )
     @slow
     @require_torch_gpu
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
           lowerCAmelCase    :		Any				=      {
               '''--fp16_opt_level=O1''': '''''',
               '''$MAX_LEN''': 1_2_8,
               '''$BS''': 1_6,
               '''$GAS''': 1,
               '''$ENRO_DIR''': data_dir,
               '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
               '''val_check_interval=0.25''': '''val_check_interval=1.0''',
           }

           # Clean up bash script
           lowerCAmelCase    :		Tuple				=      (
               (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''' )[1].strip()
           )
           lowerCAmelCase    :		Union[str, Any]				=      bash_script.replace('''\\\n'''    , '''''' ).strip().replace('''"$@"'''    , '''''' )
           lowerCAmelCase    :		Dict				=      bash_script.replace('''--fp16 '''    , ''' ''' )

           for k, v in env_vars_to_replace.items():
                 lowerCAmelCase    :		List[Any]				=      bash_script.replace(UpperCamelCase_    , str(UpperCamelCase_ ) )
           lowerCAmelCase    :		Optional[int]				=      self.get_auto_remove_tmp_dir()
           lowerCAmelCase    :		str				=      bash_script.replace('''--fp16'''    , '''''' )
           lowerCAmelCase    :		Optional[Any]				=      6
           lowerCAmelCase    :		Union[str, Any]				=      (
               ['''distillation.py''']
               + bash_script.split()
               + [
                   F'''--output_dir={output_dir}''',
                   '''--gpus=1''',
                   '''--learning_rate=1e-3''',
                   F'''--num_train_epochs={epochs}''',
                   '''--warmup_steps=10''',
                   '''--val_check_interval=1.0''',
                   '''--do_predict''',
               ]
           )
           with patch.object(UpperCamelCase_    , '''argv'''    , UpperCamelCase_ ):
                 lowerCAmelCase    :		Union[str, Any]				=      argparse.ArgumentParser()
                 lowerCAmelCase    :		List[Any]				=      pl.Trainer.add_argparse_args(UpperCamelCase_ )
                 lowerCAmelCase    :		int				=      SummarizationDistiller.add_model_specific_args(UpperCamelCase_    , os.getcwd() )
                 lowerCAmelCase    :		int				=      parser.parse_args()
                 # assert args.gpus == gpus THIS BREAKS for multi_gpu

                 lowerCAmelCase    :		int				=      distill_main(UpperCamelCase_ )

           # Check metrics
           lowerCAmelCase    :		Dict				=      load_json(model.metrics_save_path )
           lowerCAmelCase    :		Optional[int]				=      metrics['''val'''][0]
           lowerCAmelCase    :		str				=      metrics['''val'''][-1]
           assert len(metrics['''val'''] ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

           assert last_step_stats["val_avg_gen_time"] >= 0.01

           assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
           assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
           assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']    , UpperCamelCase_ )

           # check lightning ckpt can be loaded and has a reasonable statedict
           lowerCAmelCase    :		Dict				=      os.listdir(UpperCamelCase_ )
           lowerCAmelCase    :		List[str]				=      [x for x in contents if x.endswith('''.ckpt''' )][0]
           lowerCAmelCase    :		Dict				=      os.path.join(args.output_dir    , UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      torch.load(UpperCamelCase_    , map_location='''cpu''' )
           lowerCAmelCase    :		Dict				=      '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
           assert expected_key in ckpt["state_dict"]
           assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

           # TODO: turn on args.do_predict when PL bug fixed.
           if args.do_predict:
                 lowerCAmelCase    :		Tuple				=      {os.path.basename(UpperCamelCase_ ) for p in contents}
                 assert "test_generations.txt" in contents
                 assert "test_results.txt" in contents
                 # assert len(metrics["val"]) ==  desired_n_evals
                 assert len(metrics['''test'''] ) == 1








from math import pi, sqrt, tan







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if side_length < 0:
								raise ValueError('surface_area_cube() only accepts non-negative values'      )
				return 6 * side_length**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if length < 0 or breadth < 0 or height < 0:
								raise ValueError('surface_area_cuboid() only accepts non-negative values'      )
				return 2 * ((length * breadth) + (breadth * height) + (length * height))







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0:
								raise ValueError('surface_area_sphere() only accepts non-negative values'      )
				return 4 * pi * radius**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0:
								raise ValueError('surface_area_hemisphere() only accepts non-negative values'      )
				return 3 * pi * radius**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0 or height < 0:
								raise ValueError('surface_area_cone() only accepts non-negative values'      )
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    'surface_area_conical_frustum() only accepts non-negative values'      )
				__lowerCamelCase		       =    (height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0 or height < 0:
								raise ValueError('surface_area_cylinder() only accepts non-negative values'      )
				return 2 * pi * radius * (height + radius)







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if torus_radius < 0 or tube_radius < 0:
								raise ValueError('surface_area_torus() only accepts non-negative values'      )
				if torus_radius < tube_radius:
								raise ValueError(
								    'surface_area_torus() does not support spindle or self intersecting tori'      )
				return 4 * pow(UpperCamelCase__ ,     2      ) * torus_radius * tube_radius







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if length < 0 or width < 0:
								raise ValueError('area_rectangle() only accepts non-negative values'      )
				return length * width







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if side_length < 0:
								raise ValueError('area_square() only accepts non-negative values'      )
				return side_length**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if base < 0 or height < 0:
								raise ValueError('area_triangle() only accepts non-negative values'      )
				return (base * height) / 2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError('area_triangle_three_sides() only accepts non-negative values'      )
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError('Given three sides do not form a triangle'      )
				__lowerCamelCase		       =    (sidea + sidea + sidea) / 2
				__lowerCamelCase		       =    sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)      )
				return area







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if base < 0 or height < 0:
								raise ValueError('area_parallelogram() only accepts non-negative values'      )
				return base * height







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if basea < 0 or basea < 0 or height < 0:
								raise ValueError('area_trapezium() only accepts non-negative values'      )
				return 1 / 2 * (basea + basea) * height







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0:
								raise ValueError('area_circle() only accepts non-negative values'      )
				return pi * radius**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius_x < 0 or radius_y < 0:
								raise ValueError('area_ellipse() only accepts non-negative values'      )
				return pi * radius_x * radius_y







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError('area_rhombus() only accepts non-negative values'      )
				return 1 / 2 * diagonal_a * diagonal_a







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if not isinstance(UpperCamelCase__ ,     UpperCamelCase__      ) or sides < 3:
								raise ValueError(
								    'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'      )
				elif length < 0:
								raise ValueError(
								    'area_reg_polygon() only accepts non-negative values as \
length of a side'      )
				return (sides * length**2) / (4 * tan(pi / sides      ))
				return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

					print("[DEMO] Areas of various geometric shapes: \n")
					print(f'''Rectangle: {area_rectangle(10, 20) = }''')
					print(f'''Square: {area_square(10) = }''')
					print(f'''Triangle: {area_triangle(10, 10) = }''')
					print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
					print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
					print(f'''Rhombus: {area_rhombus(10, 20) = }''')
					print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
					print(f'''Circle: {area_circle(20) = }''')
					print(f'''Ellipse: {area_ellipse(10, 20) = }''')
					print("\nSurface Areas of various geometric shapes: \n")
					print(f'''Cube: {surface_area_cube(20) = }''')
					print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
					print(f'''Sphere: {surface_area_sphere(20) = }''')
					print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
					print(f'''Cone: {surface_area_cone(10, 20) = }''')
					print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
					print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
					print(f'''Torus: {surface_area_torus(20, 10) = }''')
					print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
					print(f'''Square: {area_reg_polygon(4, 10) = }''')
					print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')


