



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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  '\n'.join(snake_case__					)
				Path(snake_case__					).open('w'					).writelines(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     List[str]:



				"""simple docstring"""





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


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]    ,)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  AutoTokenizer.from_pretrained(a_						)
						lowerCAmelCase__		  =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
						lowerCAmelCase__		  =  max(len(tokenizer.encode(a_						)						) for a in ARTICLES						)
						lowerCAmelCase__		  =  max(len(tokenizer.encode(a_						)						) for a in SUMMARIES						)
						lowerCAmelCase__		  =  4
						lowerCAmelCase__		  =  8
						assert max_len_target > max_src_len  # Will be truncated
						assert max_len_source > max_src_len  # Will be truncated
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
						lowerCAmelCase__		  =  SeqaSeqDataset(
						    a_    ,data_dir=a_    ,type_path='train'    ,max_source_length=a_    ,max_target_length=a_    ,src_lang=a_    ,tgt_lang=a_    ,)
						lowerCAmelCase__		  =  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
										lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  AutoTokenizer.from_pretrained(a_						)
						lowerCAmelCase__		  =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
						lowerCAmelCase__		  =  max(len(tokenizer.encode(a_						)						) for a in ARTICLES						)
						lowerCAmelCase__		  =  max(len(tokenizer.encode(a_						)						) for a in SUMMARIES						)
						lowerCAmelCase__		  =  4
						lowerCAmelCase__		  =  LegacySeqaSeqDataset(
						    a_    ,data_dir=a_    ,type_path='train'    ,max_source_length=20    ,max_target_length=a_    ,)
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







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

						lowerCAmelCase__		  =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
						lowerCAmelCase__		  =  tmp_dir.joinpath('train.source'						).open().readlines()
						lowerCAmelCase__		  =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
						pack_data_dir(a_    ,a_    ,128    ,a_						)
						lowerCAmelCase__		  =  {x.name for x in tmp_dir.iterdir()}
						lowerCAmelCase__		  =  {x.name for x in save_dir.iterdir()}
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if not FAIRSEQ_AVAILABLE:
										return
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._get_dataset(max_len=64						)
						lowerCAmelCase__		  =  64
						lowerCAmelCase__		  =  ds.make_dynamic_sampler(a_    ,required_batch_size_multiple=a_						)
						lowerCAmelCase__		  =  [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
						lowerCAmelCase__		  =  DataLoader(a_    ,batch_sampler=a_    ,collate_fn=ds.collate_fn    ,num_workers=2						)
						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  []
						for batch in data_loader:
										lowerCAmelCase__		  =  batch['input_ids'].shape
										lowerCAmelCase__		  =  src_shape[0]
										assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
										lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._get_dataset(max_len=512						)
						lowerCAmelCase__		  =  2
						lowerCAmelCase__		  =  ds.make_sortish_sampler(a_    ,shuffle=a_						)

						lowerCAmelCase__		  =  DataLoader(a_    ,batch_size=a_    ,collate_fn=ds.collate_fn    ,num_workers=2						)
						lowerCAmelCase__		  =  DataLoader(a_    ,batch_size=a_    ,collate_fn=ds.collate_fn    ,num_workers=2    ,sampler=a_						)

						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_=1000    ,a_=128						):







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

						lowerCAmelCase__		  =  AutoTokenizer.from_pretrained(a_						)
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._get_dataset()
						lowerCAmelCase__		  =  set(DistributedSortishSampler(a_    ,256    ,num_replicas=2    ,rank=0    ,add_extra_examples=a_						)						)
						lowerCAmelCase__		  =  set(DistributedSortishSampler(a_    ,256    ,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 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  AutoTokenizer.from_pretrained(a_    ,use_fast=a_						)
						if tok_name == MBART_TINY:
										lowerCAmelCase__		  =  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'    ,)
										lowerCAmelCase__		  =  train_dataset.dataset_kwargs
										assert "src_lang" in kwargs and "tgt_lang" in kwargs
						else:
										lowerCAmelCase__		  =  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    ,)
										lowerCAmelCase__		  =  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





import numpy as np







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__ = 1E-12    ,			snake_case__ = 100    ,			)	->     tuple[float, np.ndarray]:



				"""simple docstring"""





				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[1]
				# Ensure proper dimensionality.
				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(snake_case__					) == np.iscomplexobj(snake_case__					)
				lowerCAmelCase__		  =  np.iscomplexobj(snake_case__					)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(snake_case__    ,			input_matrix.conj().T					)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				lowerCAmelCase__		  =  False
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  1E12

				while not convergence:
								# Multiple matrix by the vector.
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			snake_case__					)
								# Normalize the resulting output vector.
								lowerCAmelCase__		  =  w / np.linalg.norm(snake_case__					)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								lowerCAmelCase__		  =  vector.conj().T if is_complex else vector.T
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			np.dot(snake_case__    ,			snake_case__					)					)

								# Check convergence.
								lowerCAmelCase__		  =  np.abs(lambda_ - lambda_previous					) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												lowerCAmelCase__		  =  True

								lowerCAmelCase__		  =  lambda_

				if is_complex:
								lowerCAmelCase__		  =  np.real(lambda_					)

				return lambda_, vector







def     UpperCAmelCase_		(					)	->     None:



				"""simple docstring"""





				lowerCAmelCase__		  =  np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
				lowerCAmelCase__		  =  np.array([41, 4, 20]					)
				lowerCAmelCase__		  =  real_input_matrix.astype(np.complexaaa					)
				lowerCAmelCase__		  =  np.triu(1j * complex_input_matrix    ,			1					)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				lowerCAmelCase__		  =  np.array([41, 4, 20]					).astype(np.complexaaa					)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												lowerCAmelCase__		  =  real_input_matrix
												lowerCAmelCase__		  =  real_vector
								elif problem_type == "complex":
												lowerCAmelCase__		  =  complex_input_matrix
												lowerCAmelCase__		  =  complex_vector

								# Our implementation.
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  power_iteration(snake_case__    ,			snake_case__					)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  np.linalg.eigh(snake_case__					)
								# Last eigenvalue is the maximum one.
								lowerCAmelCase__		  =  eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								lowerCAmelCase__		  =  eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(snake_case__					) - np.abs(snake_case__					)					) <= 1E-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_power_iteration()
