

import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
   import textwrap

   from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
   import torch

   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   _a							=							False


_a							=							logging.get_logger(__name__)
_a							=							'ybelkada/fonts'




def   _A  (  )     ->						str:
   '''simple docstring'''


   if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
      raise ImportError(
          F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
          "Pix2StructImageProcessor. Please upgrade torch.")




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	Optional[Any])     ->						Optional[int]:
   '''simple docstring'''


   requires_backends(UpperCamelCase_,      ["torch"])
   _check_torch_version()

   __lowercase								=						image_tensor.unsqueeze(0)
   __lowercase								=						torch.nn.functional.unfold(UpperCamelCase_,      (patch_height, patch_width),      stride=(patch_height, patch_width))
   __lowercase								=						patches.reshape(image_tensor.size(0),      image_tensor.size(1),      UpperCamelCase_,      UpperCamelCase_,      -1)
   __lowercase								=						patches.permute(0,      4,      2,      3,      1).reshape(
       image_tensor.size(2) // patch_height,      image_tensor.size(3) // patch_width,      image_tensor.size(1) * patch_height * patch_width,      )
   return patches.unsqueeze(0)




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	int = 36,      UpperCamelCase_					:	str = "black",      UpperCamelCase_					:	str = "white",      UpperCamelCase_					:	int = 5,      UpperCamelCase_					:	int = 5,      UpperCamelCase_					:	int = 5,      UpperCamelCase_					:	int = 5,      UpperCamelCase_					:	Optional[bytes] = None,      UpperCamelCase_					:	Optional[str] = None,      )     ->						Image.Image:
   '''simple docstring'''


   requires_backends(UpperCamelCase_,      "vision")
   # Add new lines so that each line is no more than 80 characters.

   __lowercase								=						textwrap.TextWrapper(width=80)
   __lowercase								=						wrapper.wrap(text=UpperCamelCase_)
   __lowercase								=						"\n".join(UpperCamelCase_)

   if font_bytes is not None and font_path is None:
      __lowercase								=						io.BytesIO(UpperCamelCase_)
   elif font_path is not None:
      __lowercase								=						font_path
   else:
      __lowercase								=						hf_hub_download(UpperCamelCase_,      "Arial.TTF")
   __lowercase								=						ImageFont.truetype(UpperCamelCase_,      encoding="UTF-8",      size=UpperCamelCase_)

   # Use a temporary canvas to determine the width and height in pixels when
   # rendering the text.
   __lowercase								=						ImageDraw.Draw(Image.new("RGB",      (1, 1),      UpperCamelCase_))
   __lowercase		,__lowercase		,__lowercase		,__lowercase								=						temp_draw.textbbox((0, 0),      UpperCamelCase_,      UpperCamelCase_)

   # Create the actual image with a bit of padding around the text.
   __lowercase								=						text_width + left_padding + right_padding
   __lowercase								=						text_height + top_padding + bottom_padding
   __lowercase								=						Image.new("RGB",      (image_width, image_height),      UpperCamelCase_)
   __lowercase								=						ImageDraw.Draw(UpperCamelCase_)
   draw.text(xy=(left_padding, top_padding),      text=UpperCamelCase_,      fill=UpperCamelCase_,      font=UpperCamelCase_)
   return image




def   _A  (  UpperCamelCase_					:	np.ndarray,      UpperCamelCase_					:	str,      **UpperCamelCase_					:	Dict)     ->						Tuple:
   '''simple docstring'''


   requires_backends(UpperCamelCase_,      "vision")

   # Convert to PIL image if necessary
   __lowercase								=						to_pil_image(UpperCamelCase_)

   __lowercase								=						render_text(UpperCamelCase_,      **UpperCamelCase_)
   __lowercase								=						max(header_image.width,      image.width)

   __lowercase								=						int(image.height * (new_width / image.width))
   __lowercase								=						int(header_image.height * (new_width / header_image.width))

   __lowercase								=						Image.new("RGB",      (new_width, new_height + new_header_height),      "white")
   new_image.paste(header_image.resize((new_width, new_header_height)),      (0, 0))
   new_image.paste(image.resize((new_width, new_height)),      (0, new_header_height))

   # Convert back to the original framework if necessary
   __lowercase								=						to_numpy_array(UpperCamelCase_)

   if infer_channel_dimension_format(UpperCamelCase_) == ChannelDimension.LAST:
      __lowercase								=						to_channel_dimension_format(UpperCamelCase_,      ChannelDimension.LAST)

   return new_image




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Any       		=     ["flattened_patches"]


 def __init__(      self							:      Dict, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      int = 2_0_4_8, UpperCAmelCase__							:      bool = False, **UpperCAmelCase__							:      Dict, ):
    super().__init__(**UpperCAmelCase__     )
    __lowercase								=						patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
    __lowercase								=						do_normalize
    __lowercase								=						do_convert_rgb
    __lowercase								=						max_patches
    __lowercase								=						is_vqa


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      int, UpperCAmelCase__							:      dict, **UpperCAmelCase__							:      Optional[Any]     ):
    requires_backends(self.extract_flattened_patches, "torch"     )
    _check_torch_version()

    # convert to torch
    __lowercase								=						to_channel_dimension_format(UpperCAmelCase__, ChannelDimension.FIRST     )
    __lowercase								=						torch.from_numpy(UpperCAmelCase__     )

    __lowercase		,__lowercase								=						patch_size["height"], patch_size["width"]
    __lowercase		,__lowercase								=						get_image_size(UpperCAmelCase__     )

    # maximize scale s.t.
    __lowercase								=						math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)     )
    __lowercase								=						max(min(math.floor(scale * image_height / patch_height     ), UpperCAmelCase__     ), 1     )
    __lowercase								=						max(min(math.floor(scale * image_width / patch_width     ), UpperCAmelCase__     ), 1     )
    __lowercase								=						max(num_feasible_rows * patch_height, 1     )
    __lowercase								=						max(num_feasible_cols * patch_width, 1     )

    __lowercase								=						torch.nn.functional.interpolate(
        image.unsqueeze(0     ), size=(resized_height, resized_width), mode="bilinear", align_corners=UpperCAmelCase__, antialias=UpperCAmelCase__, ).squeeze(0     )

    # [1, rows, columns, patch_height * patch_width * image_channels]
    __lowercase								=						torch_extract_patches(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						patches.shape
    __lowercase								=						patches_shape[1]
    __lowercase								=						patches_shape[2]
    __lowercase								=						patches_shape[3]

    # [rows * columns, patch_height * patch_width * image_channels]
    __lowercase								=						patches.reshape([rows * columns, depth]     )

    # [rows * columns, 1]
    __lowercase								=						torch.arange(UpperCAmelCase__     ).reshape([rows, 1]     ).repeat(1, UpperCAmelCase__     ).reshape([rows * columns, 1]     )
    __lowercase								=						torch.arange(UpperCAmelCase__     ).reshape([1, columns]     ).repeat(UpperCAmelCase__, 1     ).reshape([rows * columns, 1]     )

    # Offset by 1 so the ids do not contain zeros, which represent padding.
    row_ids += 1
    col_ids += 1

    # Prepare additional patch features.
    # [rows * columns, 1]
    __lowercase								=						row_ids.to(torch.floataa     )
    __lowercase								=						col_ids.to(torch.floataa     )

    # [rows * columns, 2 + patch_height * patch_width * image_channels]
    __lowercase								=						torch.cat([row_ids, col_ids, patches], -1     )

    # [max_patches, 2 + patch_height * patch_width * image_channels]
    __lowercase								=						torch.nn.functional.pad(UpperCAmelCase__, [0, 0, 0, max_patches - (rows * columns)]     ).float()

    __lowercase								=						to_numpy_array(UpperCAmelCase__     )

    return result


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      List[Any]     ):
    if image.dtype == np.uinta:
       __lowercase								=						image.astype(np.floataa     )

    # take mean across the whole `image`
    __lowercase								=						np.mean(UpperCAmelCase__     )
    __lowercase								=						np.std(UpperCAmelCase__     )
    __lowercase								=						max(UpperCAmelCase__, 1.0 / math.sqrt(np.prod(image.shape     )     )     )

    return normalize(UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__, **UpperCAmelCase__     )



 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      ImageInput, UpperCAmelCase__							:      Optional[str] = None, UpperCAmelCase__							:      bool = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[int] = None, UpperCAmelCase__							:      Optional[Dict[str, int]] = None, UpperCAmelCase__							:      Optional[Union[str, TensorType]] = None, UpperCAmelCase__							:      ChannelDimension = ChannelDimension.FIRST, **UpperCAmelCase__							:      Optional[int], ):
    __lowercase								=						do_normalize if do_normalize is not None else self.do_normalize
    __lowercase								=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
    __lowercase								=						patch_size if patch_size is not None else self.patch_size
    __lowercase								=						max_patches if max_patches is not None else self.max_patches
    __lowercase								=						self.is_vqa

    if kwargs.get("data_format", UpperCAmelCase__     ) is not None:
       raise ValueError("data_format is not an accepted input as the outputs are "     )

    __lowercase								=						make_list_of_images(UpperCAmelCase__     )

    if not valid_images(UpperCAmelCase__     ):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."     )

    # PIL RGBA images are converted to RGB
    if do_convert_rgb:
       __lowercase								=						[convert_to_rgb(UpperCAmelCase__     ) for image in images]

    # All transformations expect numpy arrays.
    __lowercase								=						[to_numpy_array(UpperCAmelCase__     ) for image in images]

    if is_vqa:
       if header_text is None:
          raise ValueError("A header text must be provided for VQA models."     )
       __lowercase								=						kwargs.pop("font_bytes", UpperCAmelCase__     )
       __lowercase								=						kwargs.pop("font_path", UpperCAmelCase__     )

       if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
          __lowercase								=						[header_text] * len(UpperCAmelCase__     )

       __lowercase								=						[
           render_header(UpperCAmelCase__, header_text[i], font_bytes=UpperCAmelCase__, font_path=UpperCAmelCase__     )
           for i, image in enumerate(UpperCAmelCase__     )
       ]

    if do_normalize:
       __lowercase								=						[self.normalize(image=UpperCAmelCase__     ) for image in images]

    # convert to torch tensor and permute
    __lowercase								=						[
        self.extract_flattened_patches(image=UpperCAmelCase__, max_patches=UpperCAmelCase__, patch_size=UpperCAmelCase__     )
        for image in images
    ]

    # create attention mask in numpy
    __lowercase								=						[(image.sum(axis=-1     ) != 0).astype(np.floataa     ) for image in images]

    __lowercase								=						BatchFeature(
        data={"flattened_patches": images, "attention_mask": attention_masks}, tensor_type=UpperCAmelCase__     )

    return encoded_outputs







"""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   _A  (  UpperCamelCase_					:	Path,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						"\n".join(UpperCamelCase_)
   Path(UpperCamelCase_).open("w").writelines(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''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  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __lowercase		,__lowercase								=						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, src_lang=UpperCAmelCase__, tgt_lang=UpperCAmelCase__, )
    __lowercase								=						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
       __lowercase								=						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							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						LegacySeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=2_0, max_target_length=UpperCAmelCase__, )
    __lowercase								=						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 2_0 >= 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							:      str     ):
    __lowercase								=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"     )

    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    __lowercase								=						tmp_dir.joinpath("train.source"     ).open().readlines()
    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    pack_data_dir(UpperCAmelCase__, UpperCAmelCase__, 1_2_8, UpperCAmelCase__     )
    __lowercase								=						{x.name for x in tmp_dir.iterdir()}
    __lowercase								=						{x.name for x in save_dir.iterdir()}
    __lowercase								=						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  _lowercase			(      self							:      List[str]     ):
    if not FAIRSEQ_AVAILABLE:
       return
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=6_4     )
    __lowercase								=						6_4
    __lowercase								=						ds.make_dynamic_sampler(UpperCAmelCase__, required_batch_size_multiple=UpperCAmelCase__     )
    __lowercase								=						[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
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_sampler=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						[]
    __lowercase								=						[]
    for batch in data_loader:
       __lowercase								=						batch["input_ids"].shape
       __lowercase								=						src_shape[0]
       assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
       __lowercase								=						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  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=5_1_2     )
    __lowercase								=						2
    __lowercase								=						ds.make_sortish_sampler(UpperCAmelCase__, shuffle=UpperCAmelCase__     )

    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2, sampler=UpperCAmelCase__     )

    __lowercase								=						tokenizer.pad_token_id

    def count_pad_tokens(UpperCAmelCase__							:      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  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int=1_0_0_0, UpperCAmelCase__							:      str=1_2_8     ):
    if os.getenv("USE_REAL_DATA", UpperCAmelCase__     ):
       __lowercase								=						"examples/seq2seq/wmt_en_ro"
       __lowercase								=						max_len * 2 * 6_4
       if not Path(UpperCAmelCase__     ).joinpath("train.len"     ).exists():
          save_len_file(UpperCAmelCase__, UpperCAmelCase__     )
    else:
       __lowercase								=						"examples/seq2seq/test_data/wmt_en_ro"
       __lowercase								=						max_len * 4
       save_len_file(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						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  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset()
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=0, add_extra_examples=UpperCAmelCase__     )     )
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, 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  _lowercase			(      self							:      int, UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__, use_fast=UpperCAmelCase__     )
    if tok_name == MBART_TINY:
       __lowercase								=						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", )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
       __lowercase								=						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, )
       __lowercase								=						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




