
"""simple docstring"""







import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowerCamelCase__   					=		"""bert-base-cased"""
lowerCamelCase__   					=		"""google/pegasus-xsum"""
lowerCamelCase__   					=		[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCamelCase__   					=		["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCamelCase__   					=		"""patrickvonplaten/t5-tiny-random"""
lowerCamelCase__   					=		"""sshleifer/bart-tiny-random"""
lowerCamelCase__   					=		"""sshleifer/tiny-mbart"""
lowerCamelCase__   					=		"""sshleifer/tiny-marian-en-de"""
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			:						List[str]									=      '\n'.join(lowerCAmelCase__		)
	Path(lowerCAmelCase__		).open('w'		).writelines(lowerCAmelCase__		)
def     __lowerCAmelCase				(_UpperCamelCase		):


	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(lowerCAmelCase__       ,  F"{split}.source"		)       ,  lowerCAmelCase__		)
		_dump_articles(os.path.join(lowerCAmelCase__       ,  F"{split}.target"		)       ,  lowerCAmelCase__		)
	return tmp_dir




class  A__       (	_lowerCamelCase):

		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]       ,		)
		@slow
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						List[str]									=      AutoTokenizer.from_pretrained(__lowerCAmelCase )
			__lowerCAmelCase			:						Dict									=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
			__lowerCAmelCase			:						Dict									=      max(len(tokenizer.encode(__lowerCAmelCase ) ) for a in ARTICLES )
			__lowerCAmelCase			:						Any									=      max(len(tokenizer.encode(__lowerCAmelCase ) ) for a in SUMMARIES )
			__lowerCAmelCase			:						List[Any]									=      4
			__lowerCAmelCase			:						str									=      8
			assert max_len_target > max_src_len  # Will be truncated
			assert max_len_source > max_src_len  # Will be truncated
			__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
			__lowerCAmelCase			:						Tuple									=      SeqaSeqDataset(
			    __lowerCAmelCase       ,		data_dir=__lowerCAmelCase       ,		type_path='train'       ,		max_source_length=__lowerCAmelCase       ,		max_target_length=__lowerCAmelCase       ,		src_lang=__lowerCAmelCase       ,		tgt_lang=__lowerCAmelCase       ,		)
			__lowerCAmelCase			:						Tuple									=      DataLoader(__lowerCAmelCase       ,		batch_size=2       ,		collate_fn=train_dataset.collate_fn )
			for batch in dataloader:
				assert isinstance(__lowerCAmelCase       ,		__lowerCAmelCase )
				assert batch["attention_mask"].shape == batch["input_ids"].shape
				# show that articles were trimmed.
				assert batch["input_ids"].shape[1] == max_src_len
				# show that targets are the same len
				assert batch["labels"].shape[1] == max_tgt_len
				if tok_name != MBART_TINY:
					continue
				# check language codes in correct place
				__lowerCAmelCase			:						List[Any]									=      shift_tokens_right(batch['labels']       ,		tokenizer.pad_token_id )
				assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
				assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
				assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
				assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

				break  # No need to test every batch

		@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[int]									=      AutoTokenizer.from_pretrained(__lowerCAmelCase )
			__lowerCAmelCase			:						str									=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
			__lowerCAmelCase			:						Optional[Any]									=      max(len(tokenizer.encode(__lowerCAmelCase ) ) for a in ARTICLES )
			__lowerCAmelCase			:						List[Any]									=      max(len(tokenizer.encode(__lowerCAmelCase ) ) for a in SUMMARIES )
			__lowerCAmelCase			:						Dict									=      4
			__lowerCAmelCase			:						Dict									=      LegacySeqaSeqDataset(
			    __lowerCAmelCase       ,		data_dir=__lowerCAmelCase       ,		type_path='train'       ,		max_source_length=20       ,		max_target_length=__lowerCAmelCase       ,		)
			__lowerCAmelCase			:						Optional[Any]									=      DataLoader(__lowerCAmelCase       ,		batch_size=2       ,		collate_fn=train_dataset.collate_fn )
			for batch in dataloader:
				assert batch["attention_mask"].shape == batch["input_ids"].shape
				# show that articles were trimmed.
				assert batch["input_ids"].shape[1] == max_len_source
				assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
				# show that targets were truncated
				assert batch["labels"].shape[1] == trunc_target  # Truncated
				assert max_len_target > trunc_target  # Truncated
				break  # No need to test every batch

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      AutoTokenizer.from_pretrained('facebook/mbart-large-cc25' )

			__lowerCAmelCase			:						Optional[int]									=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
			__lowerCAmelCase			:						Optional[int]									=      tmp_dir.joinpath('train.source' ).open().readlines()
			__lowerCAmelCase			:						Optional[int]									=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
			pack_data_dir(__lowerCAmelCase       ,		__lowerCAmelCase       ,		1_28       ,		__lowerCAmelCase )
			__lowerCAmelCase			:						Optional[Any]									=      {x.name for x in tmp_dir.iterdir()}
			__lowerCAmelCase			:						int									=      {x.name for x in save_dir.iterdir()}
			__lowerCAmelCase			:						Dict									=      save_dir.joinpath('train.source' ).open().readlines()
			# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
			# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
			assert len(__lowerCAmelCase ) < len(__lowerCAmelCase )
			assert len(__lowerCAmelCase ) == 1
			assert len(packed_examples[0] ) == sum(len(__lowerCAmelCase ) for x in orig_examples )
			assert orig_paths == new_paths

		@pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,		reason='This test requires fairseq' )
		def 							__lowerCamelCase							(      self ):



			if not FAIRSEQ_AVAILABLE:
				return
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						List[Any]									=      self._get_dataset(max_len=64 )
			__lowerCAmelCase			:						Optional[Any]									=      64
			__lowerCAmelCase			:						Optional[int]									=      ds.make_dynamic_sampler(__lowerCAmelCase       ,		required_batch_size_multiple=__lowerCAmelCase )
			__lowerCAmelCase			:						Optional[Any]									=      [len(__lowerCAmelCase ) for x in batch_sampler]
			assert len(set(__lowerCAmelCase ) ) > 1  # it's not dynamic batch size if every batch is the same length
			assert sum(__lowerCAmelCase ) == len(__lowerCAmelCase )  # no dropped or added examples
			__lowerCAmelCase			:						Optional[int]									=      DataLoader(__lowerCAmelCase       ,		batch_sampler=__lowerCAmelCase       ,		collate_fn=ds.collate_fn       ,		num_workers=2 )
			__lowerCAmelCase			:						Union[str, Any]									=      []
			__lowerCAmelCase			:						List[Any]									=      []
			for batch in data_loader:
				__lowerCAmelCase			:						Optional[int]									=      batch['input_ids'].shape
				__lowerCAmelCase			:						List[Any]									=      src_shape[0]
				assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
				__lowerCAmelCase			:						Any									=      np.product(batch['input_ids'].shape )
				num_src_per_batch.append(__lowerCAmelCase )
				if num_src_tokens > (max_tokens * 1.1):
					failures.append(__lowerCAmelCase )
			assert num_src_per_batch[0] == max(__lowerCAmelCase )
			if failures:
				raise AssertionError(f"too many tokens in {len(__lowerCAmelCase )} batches" )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      self._get_dataset(max_len=5_12 )
			__lowerCAmelCase			:						Any									=      2
			__lowerCAmelCase			:						Dict									=      ds.make_sortish_sampler(__lowerCAmelCase       ,		shuffle=__lowerCAmelCase )

			__lowerCAmelCase			:						List[Any]									=      DataLoader(__lowerCAmelCase       ,		batch_size=__lowerCAmelCase       ,		collate_fn=ds.collate_fn       ,		num_workers=2 )
			__lowerCAmelCase			:						List[Any]									=      DataLoader(__lowerCAmelCase       ,		batch_size=__lowerCAmelCase       ,		collate_fn=ds.collate_fn       ,		num_workers=2       ,		sampler=__lowerCAmelCase )

			__lowerCAmelCase			:						List[Any]									=      tokenizer.pad_token_id

			def count_pad_tokens(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE="input_ids" ):
				return [batch[k].eq(__lowerCAmelCase ).sum().item() for batch in data_loader]

			assert sum(count_pad_tokens(__lowerCAmelCase       ,		k='labels' ) ) < sum(count_pad_tokens(__lowerCAmelCase       ,		k='labels' ) )
			assert sum(count_pad_tokens(__lowerCAmelCase ) ) < sum(count_pad_tokens(__lowerCAmelCase ) )
			assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=10_00       ,		_SCREAMING_SNAKE_CASE=1_28 ):



			if os.getenv('USE_REAL_DATA'       ,		__lowerCAmelCase ):
				__lowerCAmelCase			:						Dict									=      'examples/seq2seq/wmt_en_ro'
				__lowerCAmelCase			:						Union[str, Any]									=      max_len * 2 * 64
				if not Path(__lowerCAmelCase ).joinpath('train.len' ).exists():
					save_len_file(__lowerCAmelCase       ,		__lowerCAmelCase )
			else:
				__lowerCAmelCase			:						Tuple									=      'examples/seq2seq/test_data/wmt_en_ro'
				__lowerCAmelCase			:						List[str]									=      max_len * 4
				save_len_file(__lowerCAmelCase       ,		__lowerCAmelCase )

			__lowerCAmelCase			:						str									=      AutoTokenizer.from_pretrained(__lowerCAmelCase )
			__lowerCAmelCase			:						Optional[int]									=      SeqaSeqDataset(
			    __lowerCAmelCase       ,		data_dir=__lowerCAmelCase       ,		type_path='train'       ,		max_source_length=__lowerCAmelCase       ,		max_target_length=__lowerCAmelCase       ,		n_obs=__lowerCAmelCase       ,		)
			return ds, max_tokens, tokenizer

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      self._get_dataset()
			__lowerCAmelCase			:						Optional[int]									=      set(DistributedSortishSampler(__lowerCAmelCase       ,		2_56       ,		num_replicas=2       ,		rank=0       ,		add_extra_examples=__lowerCAmelCase ) )
			__lowerCAmelCase			:						List[Any]									=      set(DistributedSortishSampler(__lowerCAmelCase       ,		2_56       ,		num_replicas=2       ,		rank=1       ,		add_extra_examples=__lowerCAmelCase ) )
			assert idsa.intersection(__lowerCAmelCase ) == set()







		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]       ,		)
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[Any]									=      AutoTokenizer.from_pretrained(__lowerCAmelCase       ,		use_fast=__lowerCAmelCase )
			if tok_name == MBART_TINY:
				__lowerCAmelCase			:						Union[str, Any]									=      SeqaSeqDataset(
				    __lowerCAmelCase       ,		data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )       ,		type_path='train'       ,		max_source_length=4       ,		max_target_length=8       ,		src_lang='EN'       ,		tgt_lang='FR'       ,		)
				__lowerCAmelCase			:						int									=      train_dataset.dataset_kwargs
				assert "src_lang" in kwargs and "tgt_lang" in kwargs
			else:
				__lowerCAmelCase			:						str									=      SeqaSeqDataset(
				    __lowerCAmelCase       ,		data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )       ,		type_path='train'       ,		max_source_length=4       ,		max_target_length=8       ,		)
				__lowerCAmelCase			:						str									=      train_dataset.dataset_kwargs
				assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
				assert len(__lowerCAmelCase ) == 1 if tok_name == BART_TINY else len(__lowerCAmelCase ) == 0
"""simple docstring"""






from __future__ import annotations

from scipy.special import comb  # type: ignore





class  _A						:

      def __init__(						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	list_of_points
            # Degree determines the flexibility of the curve.
            # Degree = 1 will produce a straight line.
            lowercase														=	len(__lowerCAmelCase						) - 1

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            assert 0 <= t <= 1, "Time t must be between 0 and 1."
            lowercase														=	[]
            for i in range(len(self.list_of_points						)						):
                  # basis function for each i
                  output_values.append(
                      comb(self.degree		,       __lowerCAmelCase						) * ((1 - t) ** (self.degree - i)) * (t**i)						)
            # the basis must sum up to 1 for it to produce a valid Bezier curve.
            assert round(sum(__lowerCAmelCase						)		,       5						) == 1
            return output_values

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            assert 0 <= t <= 1, "Time t must be between 0 and 1."

            lowercase														=	self.basis_function(__lowerCAmelCase						)
            lowercase														=	0.0
            lowercase														=	0.0
            for i in range(len(self.list_of_points						)						):
                  # For all points, sum up the product of i-th basis function and i-th point.
                  x += basis_function[i] * self.list_of_points[i][0]
                  y += basis_function[i] * self.list_of_points[i][1]
            return (x, y)







      def 				A__       (						self		,       __lowerCAmelCase = 0.0_1						):







            """simple docstring"""



            from matplotlib import pyplot as plt  # type: ignore

            lowercase														=	[]  # x coordinates of points to plot
            lowercase														=	[]  # y coordinates of points to plot

            lowercase														=	0.0
            while t <= 1:
                  lowercase														=	self.bezier_curve_function(__lowerCAmelCase						)
                  to_plot_x.append(value[0]						)
                  to_plot_y.append(value[1]						)
                  t += step_size

            lowercase														=	[i[0] for i in self.list_of_points]
            lowercase														=	[i[1] for i in self.list_of_points]

            plt.plot(
                __lowerCAmelCase		,       __lowerCAmelCase		,       color="""blue"""		,       label="""Curve of Degree """ + str(self.degree						)		,       )
            plt.scatter(__lowerCAmelCase		,       __lowerCAmelCase		,       color="""red"""		,       label="""Control Points"""						)
            plt.legend()
            plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
       BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
       BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



