


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
			_snake_case  :	List[Any]			      =       "pt"
elif is_tf_available():
			_snake_case  :	Dict			      =       "tf"
else:
			_snake_case  :	Tuple			      =       "jax"

class        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[Any]  						=						PerceiverTokenizer
							__UpperCAmelCase					:							List[Any]  						=						False

							def 					__snake_case      (							self :						str       )			->					Dict:
														super().setUp()
														__snake_case	:    str											=     PerceiverTokenizer()
														tokenizer.save_pretrained(self.tmpdirname       )

							@cached_property
							def 					__snake_case      (							self :						Optional[Any]       )			->					List[str]:
														return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"       )

							def 					__snake_case      (							self :						Optional[Any]					,			**lowerCamelCase :						List[str]       )			->					PerceiverTokenizer:
														return self.tokenizer_class.from_pretrained(self.tmpdirname					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						List[Any]=False					,			lowerCamelCase :						Tuple=20					,			lowerCamelCase :						List[str]=5       )			->					Tuple[str, list]:
														# XXX The default common tokenizer tests assume that every ID is decodable on its own.
														# This assumption is invalid for Perceiver because single bytes might not be
														# valid utf-8 (byte 128 for instance).
														# Here we're overriding the smallest possible method to provide
														# a clean sequence without making the same assumption.

														__snake_case	:    List[str]											=     []
														for i in range(len(lowerCamelCase       )       ):
																					try:
																												__snake_case	:    str											=     tokenizer.decode([i]					,			clean_up_tokenization_spaces=lowerCamelCase       )
																					except UnicodeDecodeError:
																												pass
																					toks.append((i, tok)       )

														__snake_case	:    Optional[Any]											=     list(filter(lambda lowerCamelCase       : re.match(R"^[ a-zA-Z]+$"					,			t[1]       )					,			lowerCamelCase       )       )
														__snake_case	:    List[Any]											=     list(filter(lambda lowerCamelCase       : [t[0]] == tokenizer.encode(t[1]					,			add_special_tokens=lowerCamelCase       )					,			lowerCamelCase       )       )
														if max_length is not None and len(lowerCamelCase       ) > max_length:
																					__snake_case	:    int											=     toks[:max_length]
														if min_length is not None and len(lowerCamelCase       ) < min_length and len(lowerCamelCase       ) > 0:
																					while len(lowerCamelCase       ) < min_length:
																												__snake_case	:    Optional[Any]											=     toks + toks
        # toks_str = [t[1] for t in toks]
														__snake_case	:    str											=     [t[0] for t in toks]

														# Ensure consistency
														__snake_case	:    List[Any]											=     tokenizer.decode(lowerCamelCase					,			clean_up_tokenization_spaces=lowerCamelCase       )
														if " " not in output_txt and len(lowerCamelCase       ) > 1:
																					__snake_case	:    Optional[Any]											=     (
																					    tokenizer.decode([toks_ids[0]]					,			clean_up_tokenization_spaces=lowerCamelCase       )
																					    + " "
																					    + tokenizer.decode(toks_ids[1:]					,			clean_up_tokenization_spaces=lowerCamelCase       )
																					)
														if with_prefix_space:
																					__snake_case	:    str											=     " " + output_txt
														__snake_case	:    int											=     tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
														return output_txt, output_ids

							def 					__snake_case      (							self :						Any       )			->					List[Any]:
														__snake_case	:    List[str]											=     self.perceiver_tokenizer
														__snake_case	:    Any											=     "Unicode €."
														__snake_case	:    Any											=     tokenizer(lowerCamelCase       )
														__snake_case	:    Any											=     [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
														self.assertEqual(encoded["input_ids"]					,			lowerCamelCase       )

														# decoding
														__snake_case	:    int											=     tokenizer.decode(lowerCamelCase       )
														self.assertEqual(lowerCamelCase					,			"[CLS]Unicode €.[SEP]"       )

														__snake_case	:    List[str]											=     tokenizer("e è é ê ë"       )
														__snake_case	:    Union[str, Any]											=     [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
														self.assertEqual(encoded["input_ids"]					,			lowerCamelCase       )
														# decoding
														__snake_case	:    List[Any]											=     tokenizer.decode(lowerCamelCase       )
														self.assertEqual(lowerCamelCase					,			"[CLS]e è é ê ë[SEP]"       )

														# encode/decode, but with `encode` instead of `__call__`
														self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"       )       )					,			"[CLS]e è é ê ë[SEP]"       )

							def 					__snake_case      (							self :						Tuple       )			->					Dict:
														__snake_case	:    Tuple											=     self.perceiver_tokenizer
														__snake_case	:    Tuple											=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
														# fmt: off
														__snake_case	:    int											=     [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
														# fmt: on
														__snake_case	:    Optional[Any]											=     tokenizer(lowerCamelCase					,			padding=lowerCamelCase					,			return_tensors=lowerCamelCase       )
														self.assertIsInstance(lowerCamelCase					,			lowerCamelCase       )

														if FRAMEWORK != "jax":
																					__snake_case	:    Optional[Any]											=     list(batch.input_ids.numpy()[0]       )
														else:
																					__snake_case	:    Tuple											=     list(batch.input_ids.tolist()[0]       )

														self.assertListEqual(lowerCamelCase					,			lowerCamelCase       )

														self.assertEqual((2, 38)					,			batch.input_ids.shape       )
														self.assertEqual((2, 38)					,			batch.attention_mask.shape       )

							def 					__snake_case      (							self :						int       )			->					Union[str, Any]:
														__snake_case	:    Union[str, Any]											=     self.perceiver_tokenizer
														__snake_case	:    List[Any]											=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
														__snake_case	:    Any											=     tokenizer(lowerCamelCase					,			padding=lowerCamelCase					,			return_tensors=lowerCamelCase       )
														# check if input_ids are returned and no decoder_input_ids
														self.assertIn("input_ids"					,			lowerCamelCase       )
														self.assertIn("attention_mask"					,			lowerCamelCase       )
														self.assertNotIn("decoder_input_ids"					,			lowerCamelCase       )
														self.assertNotIn("decoder_attention_mask"					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														__snake_case	:    Optional[int]											=     self.perceiver_tokenizer
														__snake_case	:    Tuple											=     [
														    "Summary of the text.",
														    "Another summary.",
														]
														__snake_case	:    Tuple											=     tokenizer(
														    text_target=lowerCamelCase					,			max_length=32					,			padding="max_length"					,			truncation=lowerCamelCase					,			return_tensors=lowerCamelCase       )
														self.assertEqual(32					,			targets["input_ids"].shape[1]       )

							def 					__snake_case      (							self :						List[str]       )			->					List[Any]:
														# safety check on max_len default value so we are sure the test works
														__snake_case	:    Optional[int]											=     self.get_tokenizers()
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'       ):
																												self.assertNotEqual(tokenizer.model_max_length					,			42       )

        # Now let's start the test
														__snake_case	:    List[str]											=     self.get_tokenizers()
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'       ):
																												# Isolate this from the other tests because we save additional tokens/etc
																												__snake_case	:    int											=     tempfile.mkdtemp()

																												__snake_case	:    List[Any]											=     " He is very happy, UNwant\u00E9d,running"
																												__snake_case	:    List[str]											=     tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
																												tokenizer.save_pretrained(lowerCamelCase       )

																												__snake_case	:    Tuple											=     tokenizer.__class__.from_pretrained(lowerCamelCase       )
																												__snake_case	:    str											=     after_tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
																												self.assertListEqual(lowerCamelCase					,			lowerCamelCase       )

																												shutil.rmtree(lowerCamelCase       )

														__snake_case	:    Tuple											=     self.get_tokenizers(model_max_length=42       )
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'       ):
																												# Isolate this from the other tests because we save additional tokens/etc
																												__snake_case	:    Union[str, Any]											=     tempfile.mkdtemp()

																												__snake_case	:    Optional[Any]											=     " He is very happy, UNwant\u00E9d,running"
																												tokenizer.add_tokens(["bim", "bambam"]       )
																												__snake_case	:    Union[str, Any]											=     tokenizer.additional_special_tokens
																												additional_special_tokens.append("new_additional_special_token"       )
																												tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}       )
																												__snake_case	:    List[str]											=     tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
																												tokenizer.save_pretrained(lowerCamelCase       )

																												__snake_case	:    Optional[Any]											=     tokenizer.__class__.from_pretrained(lowerCamelCase       )
																												__snake_case	:    List[str]											=     after_tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
																												self.assertListEqual(lowerCamelCase					,			lowerCamelCase       )
																												self.assertIn("new_additional_special_token"					,			after_tokenizer.additional_special_tokens       )
																												self.assertEqual(after_tokenizer.model_max_length					,			42       )

																												__snake_case	:    str											=     tokenizer.__class__.from_pretrained(lowerCamelCase					,			model_max_length=43       )
																												self.assertEqual(tokenizer.model_max_length					,			43       )

																												shutil.rmtree(lowerCamelCase       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					int:
														__snake_case	:    Optional[Any]											=     []
														if self.test_slow_tokenizer:
																					tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

														if self.test_rust_tokenizer:
																					tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

														for tokenizer_class, tokenizer_utils in tokenizer_list:
																					with tempfile.TemporaryDirectory() as tmp_dir:
																												tokenizer_utils.save_pretrained(lowerCamelCase       )

																												with open(os.path.join(lowerCamelCase					,			"special_tokens_map.json"       )					,			encoding="utf-8"       ) as json_file:
																																			__snake_case	:    str											=     json.load(lowerCamelCase       )

																												with open(os.path.join(lowerCamelCase					,			"tokenizer_config.json"       )					,			encoding="utf-8"       ) as json_file:
																																			__snake_case	:    Tuple											=     json.load(lowerCamelCase       )

																												__snake_case	:    str											=     [F'<extra_id_{i}>' for i in range(125       )]

																												__snake_case	:    List[Any]											=     added_tokens_extra_ids + [
																												    "an_additional_special_token"
																												]
																												__snake_case	:    int											=     added_tokens_extra_ids + [
																												    "an_additional_special_token"
																												]

																												with open(os.path.join(lowerCamelCase					,			"special_tokens_map.json"       )					,			"w"					,			encoding="utf-8"       ) as outfile:
																																			json.dump(lowerCamelCase					,			lowerCamelCase       )
																												with open(os.path.join(lowerCamelCase					,			"tokenizer_config.json"       )					,			"w"					,			encoding="utf-8"       ) as outfile:
																																			json.dump(lowerCamelCase					,			lowerCamelCase       )

																												# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																												# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																												# "special_tokens_map.json" files
																												__snake_case	:    Optional[int]											=     tokenizer_class.from_pretrained(
																												    lowerCamelCase					,			)
																												self.assertIn(
																												    "an_additional_special_token"					,			tokenizer_without_change_in_init.additional_special_tokens       )
																												self.assertEqual(
																												    ["an_additional_special_token"]					,			tokenizer_without_change_in_init.convert_ids_to_tokens(
																												        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]       )       )					,			)

																												# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																												__snake_case	:    Tuple											=     added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"					,			lstrip=lowerCamelCase       )]
																												__snake_case	:    str											=     tokenizer_class.from_pretrained(
																												    lowerCamelCase					,			additional_special_tokens=lowerCamelCase					,			)

																												self.assertIn("a_new_additional_special_token"					,			tokenizer.additional_special_tokens       )
																												self.assertEqual(
																												    ["a_new_additional_special_token"]					,			tokenizer.convert_ids_to_tokens(
																												        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]       )       )					,			)

							def 					__snake_case      (							self :						Tuple       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.perceiver_tokenizer
														self.assertEqual(tokenizer.decode([178]       )					,			"�"       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Tuple:
														pass

							def 					__snake_case      (							self :						Optional[int]       )			->					Dict:
														pass

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

							def 					__snake_case      (							self :						int       )			->					int:
														pass






							def 					__snake_case      (							self :						int       )			->					Tuple:
														# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
														# strings and special added tokens as tokens
														__snake_case	:    Optional[Any]											=     self.get_tokenizers(fast=lowerCamelCase					,			do_lower_case=lowerCamelCase       )
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'       ):
																												__snake_case	:    Optional[Any]											=     ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																												__snake_case	:    Tuple											=     tokenizer.convert_tokens_to_string(lowerCamelCase       )

																												self.assertIsInstance(lowerCamelCase					,			lowerCamelCase       )









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


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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Union[str, Any]											=     "\n".join(__lowerCamelCase			)
							Path(__lowerCamelCase			).open("w"			).writelines(__lowerCamelCase			)







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

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]					,			)
							@slow
							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						int       )			->					Union[str, Any]:
														__snake_case	:    Optional[Any]											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    List[Any]											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
														__snake_case	:    Optional[int]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in ARTICLES       )
														__snake_case	:    Optional[int]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in SUMMARIES       )
														__snake_case	:    str											=     4
														__snake_case	:    Optional[int]											=     8
														assert max_len_target > max_src_len  # Will be truncated
														assert max_len_source > max_src_len  # Will be truncated
														__snake_case				,    __snake_case	:    Optional[int]											=     "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
														__snake_case	:    List[str]											=     SeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=lowerCamelCase					,			max_target_length=lowerCamelCase					,			src_lang=lowerCamelCase					,			tgt_lang=lowerCamelCase					,			)
														__snake_case	:    str											=     DataLoader(lowerCamelCase					,			batch_size=2					,			collate_fn=train_dataset.collate_fn       )
														for batch in dataloader:
																					assert isinstance(lowerCamelCase					,			lowerCamelCase       )
																					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
																					__snake_case	:    int											=     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 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						List[Any]       )			->					Optional[Any]:
														__snake_case	:    Any											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    List[str]											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
														__snake_case	:    Union[str, Any]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in ARTICLES       )
														__snake_case	:    Tuple											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in SUMMARIES       )
														__snake_case	:    List[str]											=     4
														__snake_case	:    List[str]											=     LegacySeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=20					,			max_target_length=lowerCamelCase					,			)
														__snake_case	:    int											=     DataLoader(lowerCamelCase					,			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 					__snake_case      (							self :						List[str]       )			->					int:
														__snake_case	:    str											=     AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

														__snake_case	:    int											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
														__snake_case	:    str											=     tmp_dir.joinpath("train.source"       ).open().readlines()
														__snake_case	:    Any											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
														pack_data_dir(lowerCamelCase					,			lowerCamelCase					,			128					,			lowerCamelCase       )
														__snake_case	:    str											=     {x.name for x in tmp_dir.iterdir()}
														__snake_case	:    Optional[int]											=     {x.name for x in save_dir.iterdir()}
														__snake_case	:    Optional[int]											=     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(lowerCamelCase       ) < len(lowerCamelCase       )
														assert len(lowerCamelCase       ) == 1
														assert len(packed_examples[0]       ) == sum(len(lowerCamelCase       ) for x in orig_examples       )
														assert orig_paths == new_paths

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

							def 					__snake_case      (							self :						int       )			->					Any:
														__snake_case				,    __snake_case				,    __snake_case	:    Union[str, Any]											=     self._get_dataset(max_len=512       )
														__snake_case	:    Union[str, Any]											=     2
														__snake_case	:    List[str]											=     ds.make_sortish_sampler(lowerCamelCase					,			shuffle=lowerCamelCase       )

														__snake_case	:    Dict											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2       )
														__snake_case	:    List[Any]											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2					,			sampler=lowerCamelCase       )

														__snake_case	:    List[Any]											=     tokenizer.pad_token_id

														def count_pad_tokens(lowerCamelCase :						List[Any]					,			lowerCamelCase :						Optional[int]="input_ids"       ):
																					return [batch[k].eq(lowerCamelCase       ).sum().item() for batch in data_loader]

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

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						List[Any]=1000					,			lowerCamelCase :						Union[str, Any]=128       )			->					Any:
														if os.getenv("USE_REAL_DATA"					,			lowerCamelCase       ):
																					__snake_case	:    int											=     "examples/seq2seq/wmt_en_ro"
																					__snake_case	:    Union[str, Any]											=     max_len * 2 * 64
																					if not Path(lowerCamelCase       ).joinpath("train.len"       ).exists():
																												save_len_file(lowerCamelCase					,			lowerCamelCase       )
														else:
																					__snake_case	:    List[str]											=     "examples/seq2seq/test_data/wmt_en_ro"
																					__snake_case	:    List[Any]											=     max_len * 4
																					save_len_file(lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    Dict											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    int											=     SeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=lowerCamelCase					,			max_target_length=lowerCamelCase					,			n_obs=lowerCamelCase					,			)
														return ds, max_tokens, tokenizer

							def 					__snake_case      (							self :						Tuple       )			->					Dict:
														__snake_case				,    __snake_case				,    __snake_case	:    Any											=     self._get_dataset()
														__snake_case	:    Optional[Any]											=     set(DistributedSortishSampler(lowerCamelCase					,			256					,			num_replicas=2					,			rank=0					,			add_extra_examples=lowerCamelCase       )       )
														__snake_case	:    int											=     set(DistributedSortishSampler(lowerCamelCase					,			256					,			num_replicas=2					,			rank=1					,			add_extra_examples=lowerCamelCase       )       )
														assert idsa.intersection(lowerCamelCase       ) == set()






							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]					,			)
							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Union[str, Any]       )			->					str:
														__snake_case	:    Tuple											=     AutoTokenizer.from_pretrained(lowerCamelCase					,			use_fast=lowerCamelCase       )
														if tok_name == MBART_TINY:
																					__snake_case	:    Dict											=     SeqaSeqDataset(
																					    lowerCamelCase					,			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"					,			)
																					__snake_case	:    Any											=     train_dataset.dataset_kwargs
																					assert "src_lang" in kwargs and "tgt_lang" in kwargs
														else:
																					__snake_case	:    Union[str, Any]											=     SeqaSeqDataset(
																					    lowerCamelCase					,			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					,			)
																					__snake_case	:    Optional[int]											=     train_dataset.dataset_kwargs
																					assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																					assert len(lowerCamelCase       ) == 1 if tok_name == BART_TINY else len(lowerCamelCase       ) == 0

