




from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


_lowerCamelCase      :  Tuple      						=						[
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
_lowerCamelCase      :  Optional[int]      						=						"UperNetConfig"
class        __snake_case  (nn.Module		):



				def __init__(      self      :		Dict		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Union[int, Tuple[int, int]]		,	_UpperCAmelCase      :		Union[int, Tuple[int, int], str] = 0		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		Union[int, Tuple[int, int]] = 1		,	)  ->    None:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    str										=      nn.Convad(
								    in_channels=_UpperCAmelCase		,	out_channels=_UpperCAmelCase		,	kernel_size=_UpperCAmelCase		,	padding=_UpperCAmelCase		,	bias=_UpperCAmelCase		,	dilation=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      nn.BatchNormad(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      nn.ReLU()



				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		torch.Tensor      )  ->    torch.Tensor:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.conv(_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      self.batch_norm(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.activation(_UpperCAmelCase      )

								return output






class        __snake_case  (nn.Module		):



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int      )  ->    None:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    List[Any]										=      [
								    nn.AdaptiveAvgPoolad(_UpperCAmelCase      ),
								    UperNetConvModule(_UpperCAmelCase		,	_UpperCAmelCase		,	kernel_size=1      ),
								]
								for i, layer in enumerate(self.layers      ):
												self.add_module(str(_UpperCAmelCase      )		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		torch.Tensor      )  ->    torch.Tensor:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      input
								for layer in self.layers:
												_lowerCAmelCase    :    Optional[Any]										=      layer(_UpperCAmelCase      )
								return hidden_state






class        __snake_case  (nn.Module		):



				def __init__(      self      :		List[Any]		,	_UpperCAmelCase      :		Tuple[int, ...]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		bool      )  ->    None:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Any										=      pool_scales
								_lowerCAmelCase    :    int										=      align_corners
								_lowerCAmelCase    :    Optional[int]										=      in_channels
								_lowerCAmelCase    :    Any										=      channels
								_lowerCAmelCase    :    Optional[Any]										=      []
								for i, pool_scale in enumerate(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Dict										=      UperNetPyramidPoolingBlock(pool_scale=_UpperCAmelCase		,	in_channels=_UpperCAmelCase		,	channels=_UpperCAmelCase      )
												self.blocks.append(_UpperCAmelCase      )
												self.add_module(str(_UpperCAmelCase      )		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		torch.Tensor      )  ->    List[torch.Tensor]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      []
								for ppm in self.blocks:
												_lowerCAmelCase    :    Any										=      ppm(_UpperCAmelCase      )
												_lowerCAmelCase    :    Any										=      nn.functional.interpolate(
												    _UpperCAmelCase		,	size=x.size()[2:]		,	mode="""bilinear"""		,	align_corners=self.align_corners      )
												ppm_outs.append(_UpperCAmelCase      )
								return ppm_outs






class        __snake_case  (nn.Module		):



				def __init__(      self      :		Dict		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		List[str]      )  ->    Any:





								'''simple docstring'''




								super().__init__()

								_lowerCAmelCase    :    Tuple										=      config
								_lowerCAmelCase    :    str										=      config.pool_scales  # e.g. (1, 2, 3, 6)
								_lowerCAmelCase    :    Optional[int]										=      in_channels
								_lowerCAmelCase    :    List[Any]										=      config.hidden_size
								_lowerCAmelCase    :    str										=      False
								_lowerCAmelCase    :    Any										=      nn.Convad(self.channels		,	config.num_labels		,	kernel_size=1      )

								# PSP Module
								_lowerCAmelCase    :    List[Any]										=      UperNetPyramidPoolingModule(
								    self.pool_scales		,	self.in_channels[-1]		,	self.channels		,	align_corners=self.align_corners		,	)
								_lowerCAmelCase    :    Tuple										=      UperNetConvModule(
								    self.in_channels[-1] + len(self.pool_scales      ) * self.channels		,	self.channels		,	kernel_size=3		,	padding=1		,	)
								# FPN Module
								_lowerCAmelCase    :    int										=      nn.ModuleList()
								_lowerCAmelCase    :    Tuple										=      nn.ModuleList()
								for in_channels in self.in_channels[:-1]:  # skip the top layer
												_lowerCAmelCase    :    Any										=      UperNetConvModule(_UpperCAmelCase		,	self.channels		,	kernel_size=1      )
												_lowerCAmelCase    :    str										=      UperNetConvModule(self.channels		,	self.channels		,	kernel_size=3		,	padding=1      )
												self.lateral_convs.append(_UpperCAmelCase      )
												self.fpn_convs.append(_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      UperNetConvModule(
								    len(self.in_channels      ) * self.channels		,	self.channels		,	kernel_size=3		,	padding=1		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								self.apply(self._init_weights      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		List[str]      )  ->    Any:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,	nn.Convad      ):
												module.weight.data.normal_(mean=0.0		,	std=self.config.initializer_range      )
												if module.bias is not None:
																module.bias.data.zero_()



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      inputs[-1]
								_lowerCAmelCase    :    Optional[int]										=      [x]
								psp_outs.extend(self.psp_modules(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    int										=      torch.cat(_UpperCAmelCase		,	dim=1      )
								_lowerCAmelCase    :    Optional[Any]										=      self.bottleneck(_UpperCAmelCase      )

								return output



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		torch.Tensor      )  ->    torch.Tensor:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      [lateral_conv(encoder_hidden_states[i]      ) for i, lateral_conv in enumerate(self.lateral_convs      )]

								laterals.append(self.psp_forward(_UpperCAmelCase      )      )

								# build top-down path
								_lowerCAmelCase    :    str										=      len(_UpperCAmelCase      )
								for i in range(used_backbone_levels - 1		,	0		,	-1      ):
												_lowerCAmelCase    :    Optional[Any]										=      laterals[i - 1].shape[2:]
												_lowerCAmelCase    :    Any										=      laterals[i - 1] + nn.functional.interpolate(
												    laterals[i]		,	size=_UpperCAmelCase		,	mode="""bilinear"""		,	align_corners=self.align_corners      )

								# build outputs
								_lowerCAmelCase    :    List[str]										=      [self.fpn_convs[i](laterals[i]      ) for i in range(used_backbone_levels - 1      )]
								# append psp feature
								fpn_outs.append(laterals[-1]      )

								for i in range(used_backbone_levels - 1		,	0		,	-1      ):
												_lowerCAmelCase    :    Dict										=      nn.functional.interpolate(
												    fpn_outs[i]		,	size=fpn_outs[0].shape[2:]		,	mode="""bilinear"""		,	align_corners=self.align_corners      )
								_lowerCAmelCase    :    Any										=      torch.cat(_UpperCAmelCase		,	dim=1      )
								_lowerCAmelCase    :    Dict										=      self.fpn_bottleneck(_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      self.classifier(_UpperCAmelCase      )

								return output






class        __snake_case  (nn.Module		):



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 2		,	_UpperCAmelCase      :		int = 3		,	_UpperCAmelCase      :		Union[int, Tuple[int, int]] = 1      )  ->    None:





								'''simple docstring'''




								super().__init__()

								_lowerCAmelCase    :    Optional[int]										=      config
								_lowerCAmelCase    :    Dict										=      config.auxiliary_in_channels
								_lowerCAmelCase    :    int										=      config.auxiliary_channels
								_lowerCAmelCase    :    str										=      config.auxiliary_num_convs
								_lowerCAmelCase    :    List[str]										=      config.auxiliary_concat_input
								_lowerCAmelCase    :    str										=      in_index

								_lowerCAmelCase    :    int										=      (kernel_size // 2) * dilation
								_lowerCAmelCase    :    List[str]										=      []
								convs.append(
								    UperNetConvModule(
								        self.in_channels		,	self.channels		,	kernel_size=_UpperCAmelCase		,	padding=_UpperCAmelCase		,	dilation=_UpperCAmelCase      )      )
								for i in range(self.num_convs - 1      ):
												convs.append(
												    UperNetConvModule(
												        self.channels		,	self.channels		,	kernel_size=_UpperCAmelCase		,	padding=_UpperCAmelCase		,	dilation=_UpperCAmelCase      )      )
								if self.num_convs == 0:
												_lowerCAmelCase    :    Optional[int]										=      nn.Identity()
								else:
												_lowerCAmelCase    :    str										=      nn.Sequential(*_UpperCAmelCase      )
								if self.concat_input:
												_lowerCAmelCase    :    Union[str, Any]										=      UperNetConvModule(
												    self.in_channels + self.channels		,	self.channels		,	kernel_size=_UpperCAmelCase		,	padding=kernel_size // 2      )

								_lowerCAmelCase    :    int										=      nn.Convad(self.channels		,	config.num_labels		,	kernel_size=1      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								self.apply(self._init_weights      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Dict      )  ->    str:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,	nn.Convad      ):
												module.weight.data.normal_(mean=0.0		,	std=self.config.initializer_range      )
												if module.bias is not None:
																module.bias.data.zero_()



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		torch.Tensor      )  ->    torch.Tensor:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      encoder_hidden_states[self.in_index]
								_lowerCAmelCase    :    Any										=      self.convs(_UpperCAmelCase      )
								if self.concat_input:
												_lowerCAmelCase    :    Dict										=      self.conv_cat(torch.cat([hidden_states, output]		,	dim=1      )      )
								_lowerCAmelCase    :    Dict										=      self.classifier(_UpperCAmelCase      )
								return output






class        __snake_case  (_a		):
				lowerCAmelCase__				=     UperNetConfig
				lowerCAmelCase__				=     "pixel_values"
				lowerCAmelCase__				=     True



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    int:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
												module.backbone.init_weights()
												module.decode_head.init_weights()
												module.auxiliary_head.init_weights()



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





								'''simple docstring'''




								self.backbone.init_weights()
								self.decode_head.init_weights()
								self.auxiliary_head.init_weights()



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Tuple=False      )  ->    Tuple:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      value






_lowerCamelCase      :  int      						=						R"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_lowerCamelCase      :  int      						=						R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
    "UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes."						,	_a						,	)
class        __snake_case  (_a		):



				def __init__(      self      :		Optional[Any]		,	_UpperCAmelCase      :		Tuple      )  ->    Optional[Any]:





								'''simple docstring'''




								super().__init__(_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      AutoBackbone.from_config(config.backbone_config      )

								# Semantic segmentation head(s)
								_lowerCAmelCase    :    Any										=      UperNetHead(_UpperCAmelCase		,	in_channels=self.backbone.channels      )
								_lowerCAmelCase    :    Any										=      UperNetFCNHead(_UpperCAmelCase      ) if config.use_auxiliary_head else None

								# Initialize weights and apply final processing
								self.post_init()



				@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("""batch_size, sequence_length"""      )      )
				@replace_return_docstrings(output_type=_UpperCAmelCase		,	config_class=_CONFIG_FOR_DOC      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Optional[torch.Tensor] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[torch.Tensor] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	)  ->    Union[tuple, SemanticSegmenterOutput]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      return_dict if return_dict is not None else self.config.use_return_dict
								_lowerCAmelCase    :    Union[str, Any]										=      (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								_lowerCAmelCase    :    Optional[Any]										=      output_attentions if output_attentions is not None else self.config.output_attentions

								_lowerCAmelCase    :    Union[str, Any]										=      self.backbone.forward_with_filtered_kwargs(
								    _UpperCAmelCase		,	output_hidden_states=_UpperCAmelCase		,	output_attentions=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      outputs.feature_maps

								_lowerCAmelCase    :    Any										=      self.decode_head(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      nn.functional.interpolate(_UpperCAmelCase		,	size=pixel_values.shape[2:]		,	mode="""bilinear"""		,	align_corners=_UpperCAmelCase      )

								_lowerCAmelCase    :    int										=      None
								if self.auxiliary_head is not None:
												_lowerCAmelCase    :    Optional[int]										=      self.auxiliary_head(_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      nn.functional.interpolate(
												    _UpperCAmelCase		,	size=pixel_values.shape[2:]		,	mode="""bilinear"""		,	align_corners=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      None
								if labels is not None:
												if self.config.num_labels == 1:
																raise ValueError("""The number of labels should be greater than one"""      )
												else:
																# compute weighted loss
																_lowerCAmelCase    :    List[Any]										=      CrossEntropyLoss(ignore_index=self.config.loss_ignore_index      )
																_lowerCAmelCase    :    Union[str, Any]										=      loss_fct(_UpperCAmelCase		,	_UpperCAmelCase      )
																_lowerCAmelCase    :    Union[str, Any]										=      loss_fct(_UpperCAmelCase		,	_UpperCAmelCase      )
																_lowerCAmelCase    :    List[str]										=      main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

								if not return_dict:
												if output_hidden_states:
																_lowerCAmelCase    :    Dict										=      (logits,) + outputs[1:]
												else:
																_lowerCAmelCase    :    Dict										=      (logits,) + outputs[2:]
												return ((loss,) + output) if loss is not None else output

								return SemanticSegmenterOutput(
								    loss=_UpperCAmelCase		,	logits=_UpperCAmelCase		,	hidden_states=outputs.hidden_states		,	attentions=outputs.attentions		,	)







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




def 							_UpperCAmelCase  (UpperCamelCase_ : Path      ,    UpperCamelCase_ : list       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      """\n""".join(UpperCamelCase_       )
				Path(UpperCamelCase_       ).open("""w"""       ).writelines(UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.source"       )      ,    UpperCamelCase_       )
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.target"       )      ,    UpperCamelCase_       )
				return tmp_dir





class        __snake_case  (_a		):



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Any										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    Dict										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    List[str]										=      4
								_lowerCAmelCase    :    List[Any]										=      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(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	src_lang=_UpperCAmelCase		,	tgt_lang=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    int										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )
												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    :    Dict										=      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      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Optional[int]										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    str										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    Union[str, Any]										=      4
								_lowerCAmelCase    :    Dict										=      LegacySeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=20		,	max_target_length=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      DataLoader(_UpperCAmelCase		,	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      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""      )

								_lowerCAmelCase    :    List[str]										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								_lowerCAmelCase    :    List[Any]										=      tmp_dir.joinpath("""train.source"""      ).open().readlines()
								_lowerCAmelCase    :    Tuple										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								pack_data_dir(_UpperCAmelCase		,	_UpperCAmelCase		,	128		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      {x.name for x in tmp_dir.iterdir()}
								_lowerCAmelCase    :    List[Any]										=      {x.name for x in save_dir.iterdir()}
								_lowerCAmelCase    :    Optional[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(_UpperCAmelCase      ) < len(_UpperCAmelCase      )
								assert len(_UpperCAmelCase      ) == 1
								assert len(packed_examples[0]      ) == sum(len(_UpperCAmelCase      ) 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      :		Any      )  ->    str:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Union[str, Any]										=      self._get_dataset(max_len=512      )
								_lowerCAmelCase    :    List[Any]										=      2
								_lowerCAmelCase    :    Tuple										=      ds.make_sortish_sampler(_UpperCAmelCase		,	shuffle=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2      )
								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2		,	sampler=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      tokenizer.pad_token_id

								def count_pad_tokens(_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]="input_ids"      ):
												return [batch[k].eq(_UpperCAmelCase      ).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      ) < sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      )
								assert sum(count_pad_tokens(_UpperCAmelCase      )      ) < sum(count_pad_tokens(_UpperCAmelCase      )      )
								assert len(_UpperCAmelCase      ) == len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str=1000		,	_UpperCAmelCase      :		Dict=128      )  ->    Dict:





								'''simple docstring'''




								if os.getenv("""USE_REAL_DATA"""		,	_UpperCAmelCase      ):
												_lowerCAmelCase    :    Optional[int]										=      """examples/seq2seq/wmt_en_ro"""
												_lowerCAmelCase    :    Optional[int]										=      max_len * 2 * 64
												if not Path(_UpperCAmelCase      ).joinpath("""train.len"""      ).exists():
																save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    int										=      """examples/seq2seq/test_data/wmt_en_ro"""
												_lowerCAmelCase    :    int										=      max_len * 4
												save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	n_obs=_UpperCAmelCase		,	)
								return ds, max_tokens, tokenizer



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





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      self._get_dataset()
								_lowerCAmelCase    :    Optional[Any]										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=0		,	add_extra_examples=_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Any										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=1		,	add_extra_examples=_UpperCAmelCase      )      )
								assert idsa.intersection(_UpperCAmelCase      ) == set()



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase		,	use_fast=_UpperCAmelCase      )
								if tok_name == MBART_TINY:
												_lowerCAmelCase    :    Optional[Any]										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	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    :    Tuple										=      train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	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    :    Optional[Any]										=      train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(_UpperCAmelCase      ) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase      ) == 0

