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


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







def     _a				(      lowercase__  :			Path     ,  lowercase__  :			list ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'\n'.join(lowercase__ )
     Path(lowercase__ ).open('w' ).writelines(lowercase__ )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


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







class       snake_case					(      UpperCamelCase_						):




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]     , )
  @slow
  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Optional[int]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		AutoTokenizer.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       SCREAMING_SNAKE_CASE__				:				int			=		max(len(tokenizer.encode(a_				)				) for a in ARTICLES				)
       SCREAMING_SNAKE_CASE__				:				str			=		max(len(tokenizer.encode(a_				)				) for a in SUMMARIES				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		4
       SCREAMING_SNAKE_CASE__				:				Dict			=		8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		SeqaSeqDataset(
           a_     , data_dir=a_     , type_path='train'     , max_source_length=a_     , max_target_length=a_     , src_lang=a_     , tgt_lang=a_     , )
       SCREAMING_SNAKE_CASE__				:				Any			=		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
            SCREAMING_SNAKE_CASE__				:				Tuple			=		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 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[str]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		AutoTokenizer.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       SCREAMING_SNAKE_CASE__				:				Any			=		max(len(tokenizer.encode(a_				)				) for a in ARTICLES				)
       SCREAMING_SNAKE_CASE__				:				Any			=		max(len(tokenizer.encode(a_				)				) for a in SUMMARIES				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		4
       SCREAMING_SNAKE_CASE__				:				str			=		LegacySeqaSeqDataset(
           a_     , data_dir=a_     , type_path='train'     , max_source_length=20     , max_target_length=a_     , )
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		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 	__lowercase(      self     :	Optional[int]				)->       List[Any]:




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

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       SCREAMING_SNAKE_CASE__				:				Any			=		tmp_dir.joinpath('train.source'				).open().readlines()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       pack_data_dir(a_     , a_     , 128     , a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{x.name for x in tmp_dir.iterdir()}
       SCREAMING_SNAKE_CASE__				:				Dict			=		{x.name for x in save_dir.iterdir()}
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		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 	__lowercase(      self     :	int				)->       Union[str, Any]:




       """simple docstring"""
       if not FAIRSEQ_AVAILABLE:
            return
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self._get_dataset(max_len=64				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		64
       SCREAMING_SNAKE_CASE__				:				int			=		ds.make_dynamic_sampler(a_     , required_batch_size_multiple=a_				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		[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
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		DataLoader(a_     , batch_sampler=a_     , collate_fn=ds.collate_fn     , num_workers=2				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
       for batch in data_loader:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		batch['input_ids'].shape
            SCREAMING_SNAKE_CASE__				:				int			=		src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            SCREAMING_SNAKE_CASE__				:				Any			=		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 	__lowercase(      self     :	Dict				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				str			=		self._get_dataset(max_len=512				)
       SCREAMING_SNAKE_CASE__				:				int			=		2
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ds.make_sortish_sampler(a_     , shuffle=a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		DataLoader(a_     , batch_size=a_     , collate_fn=ds.collate_fn     , num_workers=2				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		DataLoader(a_     , batch_size=a_     , collate_fn=ds.collate_fn     , num_workers=2     , sampler=a_				)

       SCREAMING_SNAKE_CASE__				:				str			=		tokenizer.pad_token_id

       def count_pad_tokens(a_     :	int     , a_     :	Dict="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 	__lowercase(      self     :	Optional[Any]     , a_     :	Union[str, Any]=1000     , a_     :	Tuple=128				)->       Optional[int]:




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

       SCREAMING_SNAKE_CASE__				:				str			=		AutoTokenizer.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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 	__lowercase(      self     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Any			=		self._get_dataset()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		set(DistributedSortishSampler(a_     , 256     , num_replicas=2     , rank=0     , add_extra_examples=a_				)				)
       SCREAMING_SNAKE_CASE__				:				Any			=		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 	__lowercase(      self     :	Union[str, Any]     , a_     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		AutoTokenizer.from_pretrained(a_     , use_fast=a_				)
       if tok_name == MBART_TINY:
            SCREAMING_SNAKE_CASE__				:				Any			=		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'     , )
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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     , )
            SCREAMING_SNAKE_CASE__				:				Tuple			=		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




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class       snake_case					:
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42




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




       """simple docstring"""
       assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
       assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
       assert len(self.x.shape				) == len(self.y.shape				) == len(self.z.shape				) == len(self.origin.shape				) == 2




  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       return torch.from_numpy(np.array([self.width, self.height]     , dtype=np.floataa				)				)




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




       """simple docstring"""
       return torch.from_numpy(np.array([self.x_fov, self.y_fov]     , dtype=np.floataa				)				)




  def 	__lowercase(      self     :	Tuple				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		torch.arange(self.height * self.width				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.stack(
           [
               pixel_indices % self.width,
               torch.div(a_     , self.width     , rounding_mode='trunc'				),
           ]     , axis=1     , )
       return coords




  @property
  def 	__lowercase(      self     :	Any				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.shape
       SCREAMING_SNAKE_CASE__				:				Tuple			=		int(np.prod(a_				)				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_image_coords()
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.broadcast_to(coords.unsqueeze(0				)     , [batch_size * inner_batch_size, *coords.shape]				)
       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_camera_rays(a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		rays.view(a_     , inner_batch_size * self.height * self.width     , 2     , 3				)

       return rays




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	torch.Tensor				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       SCREAMING_SNAKE_CASE__				:				str			=		coords.view(a_     , -1     , 2				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.resolution()
       SCREAMING_SNAKE_CASE__				:				str			=		self.fov()

       SCREAMING_SNAKE_CASE__				:				Any			=		(flat.float() / (res - 1)) * 2 - 1
       SCREAMING_SNAKE_CASE__				:				Any			=		fracs * torch.tan(fov / 2				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		fracs.view(a_     , -1     , 2				)
       SCREAMING_SNAKE_CASE__				:				str			=		(
           self.z.view(a_     , 1     , 3				)
           + self.x.view(a_     , 1     , 3				) * fracs[:, :, :1]
           + self.y.view(a_     , 1     , 3				) * fracs[:, :, 1:]
       )
       SCREAMING_SNAKE_CASE__				:				Tuple			=		directions / directions.norm(dim=-1     , keepdim=a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		torch.stack(
           [
               torch.broadcast_to(self.origin.view(a_     , 1     , 3				)     , [batch_size, directions.shape[1], 3]				),
               directions,
           ]     , dim=2     , )
       return rays.view(a_     , *a_     , 2     , 3				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	int				)->       "DifferentiableProjectiveCamera":




       """simple docstring"""
       assert width * self.height == height * self.width, "The aspect ratio should not change."
       return DifferentiableProjectiveCamera(
           origin=self.origin     , x=self.x     , y=self.y     , z=self.z     , width=a_     , height=a_     , x_fov=self.x_fov     , y_fov=self.y_fov     , )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				str			=		[]
     for theta in np.linspace(0     ,  2 * np.pi     ,  num=20 ):
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
          z /= np.sqrt(np.sum(z**2 ) )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		-z * 4
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		np.cross(lowercase__     ,  lowercase__ )
          origins.append(lowercase__ )
          xs.append(lowercase__ )
          ys.append(lowercase__ )
          zs.append(lowercase__ )
     return DifferentiableProjectiveCamera(
         origin=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  x=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  y=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  z=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  width=lowercase__     ,  height=lowercase__     ,  x_fov=0.7     ,  y_fov=0.7     ,  shape=(1, len(lowercase__ ))     ,  )



