
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch


def 		UpperCAmelCase     (      a_		)						->      List[str]:  # picklable for multiprocessing







				"""simple docstring"""



				return x.sum()


def 		UpperCAmelCase     (      a_		)						->      Optional[Any]:  # picklable for multiprocessing







				"""simple docstring"""



				return i + 1





@dataclass
class 				UpperCAmelCase :




	'''simple docstring'''





	snake_case_          =	42
	snake_case_          =	42





class 				UpperCAmelCase (    UpperCAmelCase_    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					{}
					__A       =					[]
					__A       =					1
					__A       =					[1, 2]
					__A       =					{"a": 1, "b": 2}
					__A       =					{"a": [1, 2], "b": [3, 4]}
					__A       =					{"a": {"1": 1}, "b": 2}
					__A       =					{"a": 1, "b": 2, "c": 3, "d": 4}
					__A       =					{}
					__A       =					[]
					__A       =					2
					__A       =					[2, 3]
					__A       =					{"a": 2, "b": 3}
					__A       =					{"a": [2, 3], "b": [4, 5]}
					__A       =					{"a": {"1": 2}, "b": 3}
					__A       =					{"a": 2, "b": 3, "c": 4, "d": 5}
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case					)				,_snake_case					)

					__A       =					2
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,num_proc=_snake_case					)				,_snake_case					)

					__A       =					{"a": np.eye(2					), "b": np.zeros(3					), "c": np.ones(2					)}
					__A       =					{"a": 2, "b": 0, "c": 2}
					__A       =					{
					    "a": np.eye(2					).astype(_snake_case					),
					    "b": np.zeros(3					).astype(_snake_case					),
					    "c": np.ones(2					).astype(_snake_case					),
					}
					self.assertEqual(map_nested(_snake_case				,_snake_case				,map_numpy=_snake_case					)				,_snake_case					)
					self.assertEqual(
					    {k: v.tolist() for k, v in map_nested(_snake_case				,_snake_case				,map_numpy=_snake_case					).items()}				,{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,)
					self.assertEqual(map_nested(_snake_case				,_snake_case				,map_numpy=_snake_case				,num_proc=_snake_case					)				,_snake_case					)
					self.assertEqual(
					    {k: v.tolist() for k, v in map_nested(_snake_case				,_snake_case				,map_numpy=_snake_case				,num_proc=_snake_case					).items()}				,{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,)
					with self.assertRaises(_snake_case					):  # can't pickle a local lambda
									map_nested(lambda A					: x + 1				,_snake_case				,num_proc=_snake_case					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					{"a": 1, "b": 2}
					__A       =					{"a": 3, "b": 4}
					__A       =					{"a": 5, "b": 6}
					__A       =					sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]					)
					self.assertEqual(sorted(zip_dict(_snake_case				,_snake_case				,_snake_case					)					)				,_snake_case					)







	def 							UpperCamelCase_    (			self   :					Tuple					):





					class 				UpperCAmelCase :




						'''simple docstring'''





						snake_case_          =	"bar"





					__A       =					Foo()
					self.assertEqual(foo.my_attr				,"bar"					)
					with temporary_assignment(_snake_case				,"my_attr"				,"BAR"					):
									self.assertEqual(foo.my_attr				,"BAR"					)
					self.assertEqual(foo.my_attr				,"bar"					)





@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"						,  [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]						,  )
def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      List[str]:







				"""simple docstring"""



				with patch("datasets.utils.py_utils._single_map_nested"		) as mock_single_map_nested, patch(
				    "datasets.parallel.parallel.Pool"		) as mock_multiprocessing_pool:
								__A       =					{F'''{i}''': i for i in range(__UpperCamelCase		)}
								__A       =					map_nested(lambda a_		: x + 1_0						,  __UpperCamelCase						,  num_proc=__UpperCamelCase						,  parallel_min_length=1_6		)
								if expected_num_proc == 1:
												assert mock_single_map_nested.called
												assert not mock_multiprocessing_pool.called
								else:
												assert not mock_single_map_nested.called
												assert mock_multiprocessing_pool.called
												assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class 				UpperCAmelCase (    UpperCAmelCase_    ):




	'''simple docstring'''





	@require_tf
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					import tensorflow as tf
					from tensorflow.keras import layers

					__A       =					layers.Dense(2					)

					def gen_random_output():
									__A       =					tf.random.uniform((1, 3)					)
									return model(_snake_case					).numpy()

					with temp_seed(42				,set_tensorflow=_snake_case					):
									__A       =					gen_random_output()
					with temp_seed(42				,set_tensorflow=_snake_case					):
									__A       =					gen_random_output()
					__A       =					gen_random_output()

					np.testing.assert_equal(_snake_case				,_snake_case					)
					self.assertGreater(np.abs(outa - outa					).sum()				,0					)






	@require_torch
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					import torch

					def gen_random_output():
									__A       =					torch.nn.Linear(3				,2					)
									__A       =					torch.rand(1				,3					)
									return model(_snake_case					).detach().numpy()

					with temp_seed(42				,set_pytorch=_snake_case					):
									__A       =					gen_random_output()
					with temp_seed(42				,set_pytorch=_snake_case					):
									__A       =					gen_random_output()
					__A       =					gen_random_output()

					np.testing.assert_equal(_snake_case				,_snake_case					)
					self.assertGreater(np.abs(outa - outa					).sum()				,0					)







	def 							UpperCamelCase_    (			self   :					List[Any]					):


					def gen_random_output():
									return np.random.rand(1				,3					)

					with temp_seed(42					):
									__A       =					gen_random_output()
					with temp_seed(42					):
									__A       =					gen_random_output()
					__A       =					gen_random_output()

					np.testing.assert_equal(_snake_case				,_snake_case					)
					self.assertGreater(np.abs(outa - outa					).sum()				,0					)





@pytest.mark.parametrize("input_data"						,  [{}]		)
def 		UpperCAmelCase     (      a_		)						->      List[str]:







				"""simple docstring"""



				__A       =					NestedDataStructure(__UpperCamelCase		).data
				assert output_data == input_data


@pytest.mark.parametrize(
    "data, expected_output"						,  [
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]						,  )
def 		UpperCAmelCase     (      a_						,  a_		)						->      str:







				"""simple docstring"""



				__A       =					NestedDataStructure(__UpperCamelCase		).flatten()
				assert output == expected_output


def 		UpperCAmelCase     (      )						->      Tuple:







				"""simple docstring"""



				__A       =					A(x=1						,  y="foobar"		)
				__A       =					{"x": 1, "y": "foobar"}
				assert asdict(__UpperCamelCase		) == expected_output

				__A       =					{"a": {"b": A(x=1_0						,  y="foo"		)}, "c": [A(x=2_0						,  y="bar"		)]}
				__A       =					{"a": {"b": {"x": 1_0, "y": "foo"}}, "c": [{"x": 2_0, "y": "bar"}]}
				assert asdict(__UpperCamelCase		) == expected_output

				with pytest.raises(__UpperCamelCase		):
								asdict([1, A(x=1_0						,  y="foo"		)]		)


def 		UpperCAmelCase     (      a_		)						->      str:







				"""simple docstring"""



				return text.split()


def 		UpperCAmelCase     (      a_		)						->      Tuple:







				"""simple docstring"""



				yield (time.time(), content)
				time.sleep(2		)
				yield (time.time(), content)


def 		UpperCAmelCase     (      )						->      Any:







				"""simple docstring"""



				with Pool(2		) as pool:
								__A       =					list(iflatmap_unordered(__UpperCamelCase						,  _split_text						,  kwargs_iterable=[{"text": "hello there"}] * 1_0		)		)
								assert out.count("hello"		) == 1_0
								assert out.count("there"		) == 1_0
								assert len(__UpperCamelCase		) == 2_0

				# check multiprocess from pathos (uses dill for pickling)
				with multiprocess.Pool(2		) as pool:
								__A       =					list(iflatmap_unordered(__UpperCamelCase						,  _split_text						,  kwargs_iterable=[{"text": "hello there"}] * 1_0		)		)
								assert out.count("hello"		) == 1_0
								assert out.count("there"		) == 1_0
								assert len(__UpperCamelCase		) == 2_0

				# check that we get items as fast as possible
				with Pool(2		) as pool:
								__A       =					[]
								for yield_time, content in iflatmap_unordered(
								    __UpperCamelCase						,  _aseconds_generator_of_aitems_with_timing						,  kwargs_iterable=[{"content": "a"}, {"content": "b"}]		):
												assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
												out.append(__UpperCamelCase		)
								assert out.count("a"		) == 2
								assert out.count("b"		) == 2
								assert len(__UpperCamelCase		) == 4





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     filter(lambda __UpperCamelCase       : p.requires_grad			,						model.parameters()       )
			A__	  =     sum([np.prod(p.size()       ) for p in model_parameters]       )
			return params


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			if metric == "rouge2":
						A__	  =     '{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						A__	  =     '{val_avg_bleu:.4f}-{step_count}'
			elif metric == "em":
						A__	  =     '{val_avg_em:.4f}-{step_count}'
			elif metric == "loss":
						A__	  =     '{val_avg_loss:.4f}-{step_count}'
			else:
						raise NotImplementedError(
						    f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
						    ' function.'       )

			A__	  =     ModelCheckpoint(
			    dirpath=__UpperCamelCase			,						filename=__UpperCamelCase			,						monitor=f'''val_{metric}'''			,						mode='max'			,						save_top_k=1			,						every_n_epochs=1			,						)

			return checkpoint_callback


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''			,						mode='min' if 'loss' in metric else 'max'			,						patience=__UpperCamelCase			,						verbose=__UpperCamelCase			,						)






class   __lowerCAmelCase  (		pl.Callback   ):
					"""simple docstring"""






					def      _a				(  self       :							Dict					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							str       ):






								"""simple docstring"""


								A__	  =     {F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
								pl_module.logger.log_metrics(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule					,       _snake_case       :							str					,       _snake_case       :							Optional[Any]=True       ):






								"""simple docstring"""


								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
								A__	  =     trainer.callback_metrics
								trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}       )
								# Log results
								A__	  =     Path(pl_module.hparams.output_dir       )
								if type_path == "test":
											A__	  =     od / 'test_results.txt'
											A__	  =     od / 'test_generations.txt'
								else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											A__	  =     od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											A__	  =     od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=_snake_case       )
											generations_file.parent.mkdir(exist_ok=_snake_case       )
								with open(_snake_case					,       'a+'       ) as writer:
											for key in sorted(_snake_case       ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														A__	  =     metrics[key]
														if isinstance(_snake_case					,       torch.Tensor       ):
																	A__	  =     val.item()
														A__	  =     F'''{key}: {val:.6f}\n'''
														writer.write(_snake_case       )

								if not save_generations:
											return

								if "preds" in metrics:
											A__	  =     '\n'.join(metrics['preds']       )
											generations_file.open('w+'       ).write(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Dict					,       _snake_case       :							List[str]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								try:
											A__	  =     pl_module.model.model.num_parameters()
								except AttributeError:
											A__	  =     pl_module.model.num_parameters()

								A__	  =     count_trainable_parameters(_snake_case       )
								# mp stands for million parameters
								trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}       )



					@rank_zero_only
					def      _a				(  self       :							int					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								return self._write_logs(_snake_case					,       _snake_case					,       'test'       )



					@rank_zero_only
					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")




