
"""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


_a														=		"""bert-base-cased"""
_a														=		"""google/pegasus-xsum"""
_a														=		[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_a														=		["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_a														=		"""patrickvonplaten/t5-tiny-random"""
_a														=		"""sshleifer/bart-tiny-random"""
_a														=		"""sshleifer/tiny-mbart"""
_a														=		"""sshleifer/tiny-marian-en-de"""



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  '''\n'''.join(__a     )
					Path(__a     ).open('''w'''     ).writelines(__a     )



def 				lowerCamelCase__		(      __snake_case     )							->		Optional[int]:


					"""simple docstring"""





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






class    _UpperCAmelCase(   __lowercase     ):



		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]    ,		)
		@slow
		def 						UpperCAmelCase							( self    ,		__a)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  AutoTokenizer.from_pretrained(_a)
							_UpperCamelCase										=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
							_UpperCamelCase										=  max(len(tokenizer.encode(_a)) for a in ARTICLES)
							_UpperCamelCase										=  max(len(tokenizer.encode(_a)) for a in SUMMARIES)
							_UpperCamelCase										=  4
							_UpperCamelCase										=  8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							_UpperCamelCase			,  _UpperCamelCase										=  '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
							_UpperCamelCase										=  SeqaSeqDataset(
							    _a    ,		data_dir=_a    ,		type_path='''train'''    ,		max_source_length=_a    ,		max_target_length=_a    ,		src_lang=_a    ,		tgt_lang=_a    ,		)
							_UpperCamelCase										=  DataLoader(_a    ,		batch_size=2    ,		collate_fn=train_dataset.collate_fn)
							for batch in dataloader:
												assert isinstance(_a    ,		_a)
												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
												_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  AutoTokenizer.from_pretrained(_a)
							_UpperCamelCase										=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
							_UpperCamelCase										=  max(len(tokenizer.encode(_a)) for a in ARTICLES)
							_UpperCamelCase										=  max(len(tokenizer.encode(_a)) for a in SUMMARIES)
							_UpperCamelCase										=  4
							_UpperCamelCase										=  LegacySeqaSeqDataset(
							    _a    ,		data_dir=_a    ,		type_path='''train'''    ,		max_source_length=20    ,		max_target_length=_a    ,		)
							_UpperCamelCase										=  DataLoader(_a    ,		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 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')

							_UpperCamelCase										=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
							_UpperCamelCase										=  tmp_dir.joinpath('''train.source''').open().readlines()
							_UpperCamelCase										=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
							pack_data_dir(_a    ,		_a    ,		1_28    ,		_a)
							_UpperCamelCase										=  {x.name for x in tmp_dir.iterdir()}
							_UpperCamelCase										=  {x.name for x in save_dir.iterdir()}
							_UpperCamelCase										=  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(_a) < len(_a)
							assert len(_a) == 1
							assert len(packed_examples[0]) == sum(len(_a) for x in orig_examples)
							assert orig_paths == new_paths



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






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



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  self._get_dataset(max_len=5_12)
							_UpperCamelCase										=  2
							_UpperCamelCase										=  ds.make_sortish_sampler(_a    ,		shuffle=_a)

							_UpperCamelCase										=  DataLoader(_a    ,		batch_size=_a    ,		collate_fn=ds.collate_fn    ,		num_workers=2)
							_UpperCamelCase										=  DataLoader(_a    ,		batch_size=_a    ,		collate_fn=ds.collate_fn    ,		num_workers=2    ,		sampler=_a)

							_UpperCamelCase										=  tokenizer.pad_token_id

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

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



		def 						UpperCAmelCase							( self    ,		__a=10_00    ,		__a=1_28)	->	str:






							'''simple docstring'''
							if os.getenv('''USE_REAL_DATA'''    ,		_a):
												_UpperCamelCase										=  '''examples/seq2seq/wmt_en_ro'''
												_UpperCamelCase										=  max_len * 2 * 64
												if not Path(_a).joinpath('''train.len''').exists():
																	save_len_file(_a    ,		_a)
							else:
												_UpperCamelCase										=  '''examples/seq2seq/test_data/wmt_en_ro'''
												_UpperCamelCase										=  max_len * 4
												save_len_file(_a    ,		_a)

							_UpperCamelCase										=  AutoTokenizer.from_pretrained(_a)
							_UpperCamelCase										=  SeqaSeqDataset(
							    _a    ,		data_dir=_a    ,		type_path='''train'''    ,		max_source_length=_a    ,		max_target_length=_a    ,		n_obs=_a    ,		)
							return ds, max_tokens, tokenizer



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  self._get_dataset()
							_UpperCamelCase										=  set(DistributedSortishSampler(_a    ,		2_56    ,		num_replicas=2    ,		rank=0    ,		add_extra_examples=_a))
							_UpperCamelCase										=  set(DistributedSortishSampler(_a    ,		2_56    ,		num_replicas=2    ,		rank=1    ,		add_extra_examples=_a))
							assert idsa.intersection(_a) == set()




		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]    ,		)
		def 						UpperCAmelCase							( self    ,		__a)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  AutoTokenizer.from_pretrained(_a    ,		use_fast=_a)
							if tok_name == MBART_TINY:
												_UpperCamelCase										=  SeqaSeqDataset(
												    _a    ,		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'''    ,		)
												_UpperCamelCase										=  train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
												_UpperCamelCase										=  SeqaSeqDataset(
												    _a    ,		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    ,		)
												_UpperCamelCase										=  train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(_a) == 1 if tok_name == BART_TINY else len(_a) == 0








"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 				lowerCamelCase__		(      __snake_case     )							->		None:


					"""simple docstring"""





					_UpperCamelCase			,  _UpperCamelCase										=  analyze_text(__snake_case     )
					_UpperCamelCase										=  list(''' ''' + ascii_lowercase     )
					# what is our total sum of probabilities.
					_UpperCamelCase										=  sum(single_char_strings.values()     )

					# one length string
					_UpperCamelCase										=  0
					# for each alpha we go in our dict and if it is in it we calculate entropy
					for ch in my_alphas:
										if ch in single_char_strings:
															_UpperCamelCase										=  single_char_strings[ch]
															_UpperCamelCase										=  my_str / all_sum
															my_fir_sum += prob * math.loga(__snake_case     )  # entropy formula.

    # print entropy
					print(F'''{round(-1 * my_fir_sum     ):.1f}'''     )

					# two len string
					_UpperCamelCase										=  sum(two_char_strings.values()     )
					_UpperCamelCase										=  0
					# for each alpha (two in size) calculate entropy.
					for cha in my_alphas:
										for cha in my_alphas:
															_UpperCamelCase										=  cha + cha
															if sequence in two_char_strings:
																				_UpperCamelCase										=  two_char_strings[sequence]
																				_UpperCamelCase										=  int(__snake_case     ) / all_sum
																				my_sec_sum += prob * math.loga(__snake_case     )

    # print second entropy
					print(F'''{round(-1 * my_sec_sum     ):.1f}'''     )

					# print the difference between them
					print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}'''     )



def 				lowerCamelCase__		(      __snake_case     )							->		tuple[dict, dict]:


					"""simple docstring"""





					_UpperCamelCase										=  Counter()  # type: ignore
					_UpperCamelCase										=  Counter()  # type: ignore
					single_char_strings[text[-1]] += 1

					# first case when we have space at start.
					two_char_strings[" " + text[0]] += 1
					for i in range(0,					len(__snake_case     ) - 1     ):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings



def 				lowerCamelCase__		(      )							->		Dict:


					"""simple docstring"""





					import doctest

					doctest.testmod()
					# text = (
					#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
					#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
					#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
					#     "beloved evident through no service elderly is. Blind there if every no so "
					#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
					#     "of message cottage windows do besides against uncivil.  Delightful "
					#     "unreserved impossible few estimating men favourable see entreaties. She "
					#     "propriety immediate was improving. He or entrance humoured likewise "
					#     "moderate. Much nor game son say feel. Fat make met can must form into "
					#     "gate. Me we offending prevailed discovery. "
					# )

					# calculate_prob(text)


if __name__ == "__main__":
				main()






