
'''simple docstring'''







import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case				:   str     =				get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
						from transformers.models.mbart.modeling_mbart import shift_tokens_right

_snake_case				:   List[Any]     =				250_004
_snake_case				:   List[str]     =				250_020


@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					List[str]       =					MBartaaTokenizer
		a							:					Any       =					MBartaaTokenizerFast
		a							:					List[Any]       =					True
		a							:					List[str]       =					True




		def     lowercase       (				self      ):
								super().setUp()

								# We have a SentencePiece fixture for testing
								_SCREAMING_SNAKE_CASE				= MBartaaTokenizer(UpperCamelCase					,						src_lang="en_XX"					,						tgt_lang="ro_RO"					,						keep_accents=UpperCamelCase      )
								tokenizer.save_pretrained(self.tmpdirname      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "<s>"
								_SCREAMING_SNAKE_CASE				= 0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase      )					,						UpperCamelCase      )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase      )					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= list(self.get_tokenizer().get_vocab().keys()      )

								self.assertEqual(vocab_keys[0]					,						"<s>"      )
								self.assertEqual(vocab_keys[1]					,						"<pad>"      )
								self.assertEqual(vocab_keys[-1]					,						"<mask>"      )
								self.assertEqual(len(UpperCamelCase      )					,						1_054      )




		def     lowercase       (				self      ):
								self.assertEqual(self.get_tokenizer().vocab_size					,						1_054      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= MBartaaTokenizer(UpperCamelCase					,						src_lang="en_XX"					,						tgt_lang="ro_RO"					,						keep_accents=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize("This is a test"      )
								self.assertListEqual(UpperCamelCase					,						["▁This", "▁is", "▁a", "▁t", "est"]      )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCamelCase      )					,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,						)

								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize("I was born in 92000, and this is falsé."      )
								self.assertListEqual(
								    UpperCamelCase					,						[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]					,						)
								_SCREAMING_SNAKE_CASE				= tokenizer.convert_tokens_to_ids(UpperCamelCase      )
								self.assertListEqual(
								    UpperCamelCase					,						[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]					,						)

								_SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(UpperCamelCase      )
								self.assertListEqual(
								    UpperCamelCase					,						[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]					,						)




		@slow
		def     lowercase       (				self      ):
								# fmt: off
								_SCREAMING_SNAKE_CASE				= {"input_ids": [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCamelCase					,						model_name="facebook/mbart-large-50"					,						revision="d3913889c59cd5c9e456b269c376325eabad57e2"					,						)




		def     lowercase       (				self      ):
								if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

								_SCREAMING_SNAKE_CASE				= (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(UpperCamelCase					,						**UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= self.tokenizer_class.from_pretrained(UpperCamelCase					,						**UpperCamelCase      )

																				_SCREAMING_SNAKE_CASE				= tempfile.mkdtemp()

																				_SCREAMING_SNAKE_CASE				= tokenizer_r.save_pretrained(UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_p.save_pretrained(UpperCamelCase      )

																				# Checks it save with the same files + the tokenizer.json file for the fast one
																				self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )
																				_SCREAMING_SNAKE_CASE				= tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f      )
																				self.assertSequenceEqual(UpperCamelCase					,						UpperCamelCase      )

																				# Checks everything loads correctly in the same way
																				_SCREAMING_SNAKE_CASE				= tokenizer_r.from_pretrained(UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_p.from_pretrained(UpperCamelCase      )

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(UpperCamelCase					,						UpperCamelCase      )      )
																										# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																										# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																				shutil.rmtree(UpperCamelCase      )

																				# Save tokenizer rust, legacy_format=True
																				_SCREAMING_SNAKE_CASE				= tempfile.mkdtemp()

																				_SCREAMING_SNAKE_CASE				= tokenizer_r.save_pretrained(UpperCamelCase					,						legacy_format=UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_p.save_pretrained(UpperCamelCase      )

																				# Checks it save with the same files
																				self.assertSequenceEqual(UpperCamelCase					,						UpperCamelCase      )

																				# Checks everything loads correctly in the same way
																				_SCREAMING_SNAKE_CASE				= tokenizer_r.from_pretrained(UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_p.from_pretrained(UpperCamelCase      )

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(UpperCamelCase					,						UpperCamelCase      )      )

																				shutil.rmtree(UpperCamelCase      )

																				# Save tokenizer rust, legacy_format=False
																				_SCREAMING_SNAKE_CASE				= tempfile.mkdtemp()

																				_SCREAMING_SNAKE_CASE				= tokenizer_r.save_pretrained(UpperCamelCase					,						legacy_format=UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_p.save_pretrained(UpperCamelCase      )

																				# Checks it saved the tokenizer.json file
																				self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )

																				# Checks everything loads correctly in the same way
																				_SCREAMING_SNAKE_CASE				= tokenizer_r.from_pretrained(UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_p.from_pretrained(UpperCamelCase      )

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(UpperCamelCase					,						UpperCamelCase      )      )

																				shutil.rmtree(UpperCamelCase      )


@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase (				unittest.TestCase			):
		a							:					Optional[Any]       =					"""facebook/mbart-large-50-one-to-many-mmt"""
		a							:					Union[str, Any]       =					[
		    """ UN Chief Says There Is No Military Solution in Syria""",
		    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
		]
		a							:					Tuple       =					[
		    """Şeful ONU declară că nu există o soluţie militară în Siria""",
		    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
		    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
		    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
		]
		a							:					Optional[int]       =					[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]




		@classmethod
		def     lowercase       (				cls      ):
								_SCREAMING_SNAKE_CASE				= MBartaaTokenizer.from_pretrained(
								    cls.checkpoint_name					,						src_lang="en_XX"					,						tgt_lang="ro_RO"      )
								_SCREAMING_SNAKE_CASE				= 1
								return cls




		def     lowercase       (				self      ):
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]					,						250_001      )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]					,						250_004      )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]					,						250_020      )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]					,						250_038      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								self.assertIn(UpperCamelCase					,						self.tokenizer.all_special_ids      )
								_SCREAMING_SNAKE_CASE				= [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
								_SCREAMING_SNAKE_CASE				= self.tokenizer.decode(UpperCamelCase					,						skip_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.tokenizer.decode(generated_ids[1:]					,						skip_special_tokens=UpperCamelCase      )
								self.assertEqual(UpperCamelCase					,						UpperCamelCase      )
								self.assertNotIn(self.tokenizer.eos_token					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= ["this is gunna be a long sentence " * 20]
								assert isinstance(src_text[0]					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= 10
								_SCREAMING_SNAKE_CASE				= self.tokenizer(UpperCamelCase					,						max_length=UpperCamelCase					,						truncation=UpperCamelCase      ).input_ids[0]
								self.assertEqual(ids[0]					,						UpperCamelCase      )
								self.assertEqual(ids[-1]					,						2      )
								self.assertEqual(len(UpperCamelCase      )					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]      )					,						[250_053, 250_001]      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= tempfile.mkdtemp()
								_SCREAMING_SNAKE_CASE				= self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= MBartaaTokenizer.from_pretrained(UpperCamelCase      )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,						UpperCamelCase      )




		@require_torch
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer(self.src_text					,						text_target=self.tgt_text					,						padding=UpperCamelCase					,						return_tensors="pt"      )
								_SCREAMING_SNAKE_CASE				= shift_tokens_right(batch["labels"]					,						self.tokenizer.pad_token_id      )

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == RO_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]




		@require_torch
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer(
								    self.src_text					,						text_target=self.tgt_text					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=len(self.expected_src_tokens      )					,						return_tensors="pt"					,						)
								_SCREAMING_SNAKE_CASE				= shift_tokens_right(batch["labels"]					,						self.tokenizer.pad_token_id      )

								self.assertIsInstance(UpperCamelCase					,						UpperCamelCase      )

								self.assertEqual((2, 14)					,						batch.input_ids.shape      )
								self.assertEqual((2, 14)					,						batch.attention_mask.shape      )
								_SCREAMING_SNAKE_CASE				= batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens					,						UpperCamelCase      )
								self.assertEqual(2					,						batch.decoder_input_ids[0, 0]      )  # decoder_start_token_id
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens					,						[EN_CODE]      )
								self.assertEqual(self.tokenizer.suffix_tokens					,						[self.tokenizer.eos_token_id]      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer(self.src_text					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=3					,						return_tensors="pt"      )
								_SCREAMING_SNAKE_CASE				= self.tokenizer(
								    text_target=self.tgt_text					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=10					,						return_tensors="pt"      )
								_SCREAMING_SNAKE_CASE				= targets["input_ids"]
								_SCREAMING_SNAKE_CASE				= shift_tokens_right(UpperCamelCase					,						self.tokenizer.pad_token_id      )

								self.assertEqual(batch.input_ids.shape[1]					,						3      )
								self.assertEqual(batch.decoder_input_ids.shape[1]					,						10      )




		@require_torch
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer._build_translation_inputs(
								    "A test"					,						return_tensors="pt"					,						src_lang="en_XX"					,						tgt_lang="ar_AR"      )

								self.assertEqual(
								    nested_simplify(UpperCamelCase      )					,						{
								        # en_XX, A, test, EOS
								        "input_ids": [[250_004, 62, 3_034, 2]],
								        "attention_mask": [[1, 1, 1, 1]],
								        # ar_AR
								        "forced_bos_token_id": 250_001,
								    }					,						)

'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= process
								_SCREAMING_SNAKE_CASE				= params




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.dataset[i]
								_SCREAMING_SNAKE_CASE				= self.process(UpperCamelCase					,						**self.params      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								_SCREAMING_SNAKE_CASE				= loader
								_SCREAMING_SNAKE_CASE				= infer
								_SCREAMING_SNAKE_CASE				= params
								if loader_batch_size == 1:
														# Let's spare some time by deactivating altogether
														_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= loader_batch_size

								# Internal bookkeeping
								_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= None




		def __len__(				self      ):
								return len(self.loader      )




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								if isinstance(self._loader_batch_data					,						torch.Tensor      ):
														# Batch data is simple tensor, just fetch the slice
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data[self._loader_batch_index]
								else:
														# Batch data is assumed to be BaseModelOutput (or dict)
														_SCREAMING_SNAKE_CASE				= {}
														for k, element in self._loader_batch_data.items():
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										# Convert ModelOutput to tuple first
																										_SCREAMING_SNAKE_CASE				= element.to_tuple()
																										if isinstance(element[0]					,						torch.Tensor      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= tuple(np.expand_dims(el[self._loader_batch_index]					,						0      ) for el in element      )
																										continue
																				if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										# Those are stored as lists of tensors so need specific unbatching.
																										if isinstance(element[0]					,						torch.Tensor      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= tuple(np.expand_dims(el[self._loader_batch_index]					,						0      ) for el in element      )
																										continue
																				if element is None:
																										# This can happen for optional data that get passed around
																										_SCREAMING_SNAKE_CASE				= None
																				elif isinstance(element[self._loader_batch_index]					,						torch.Tensor      ):
																										# Take correct batch data, but make it looked like batch_size=1
																										# For compatibility with other methods within transformers

																										_SCREAMING_SNAKE_CASE				= element[self._loader_batch_index].unsqueeze(0      )
																				elif isinstance(element[self._loader_batch_index]					,						np.ndarray      ):
																										# Take correct batch data, but make it looked like batch_size=1
																										# For compatibility with other methods within transformers
																										_SCREAMING_SNAKE_CASE				= np.expand_dims(element[self._loader_batch_index]					,						0      )
																				else:
																										# This is typically a list, so no need to `unsqueeze`.
																										_SCREAMING_SNAKE_CASE				= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data.__class__(UpperCamelCase      )
								self._loader_batch_index += 1
								return result




		def     lowercase       (				self      ):
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
														# We are currently unrolling a batch so we just need to return
														# the current item within a batch
														return self.loader_batch_item()

								# We're out of items within a batch
								_SCREAMING_SNAKE_CASE				= next(self.iterator      )
								_SCREAMING_SNAKE_CASE				= self.infer(UpperCamelCase					,						**self.params      )
								# We now have a batch of "inferred things".
								if self.loader_batch_size is not None:
														# Try to infer the size of the batch
														if isinstance(UpperCamelCase					,						torch.Tensor      ):
																				_SCREAMING_SNAKE_CASE				= processed
														else:
																				_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																				_SCREAMING_SNAKE_CASE				= processed[key]
														if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
														else:
																				_SCREAMING_SNAKE_CASE				= first_tensor.shape[0]
														if 0 < observed_batch_size < self.loader_batch_size:
																				# could be last batch so we can't unroll as many
																				# elements.
																				_SCREAMING_SNAKE_CASE				= observed_batch_size
														# Setting internal index to unwrap the batch
														_SCREAMING_SNAKE_CASE				= processed
														_SCREAMING_SNAKE_CASE				= 0
														return self.loader_batch_item()
								else:
														# We're not unrolling batches
														return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								_SCREAMING_SNAKE_CASE				= None
								return self




		def     lowercase       (				self      ):
								if self.subiterator is None:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
								try:
														# Try to return next item
														_SCREAMING_SNAKE_CASE				= next(self.subiterator      )
								except StopIteration:
														# When a preprocess iterator ends, we can start lookig at the next item
														# ChunkIterator will keep feeding until ALL elements of iterator
														# all have created their subiterator and have been iterating against.
														#
														# Another way to look at it, is we're basically flattening lists of lists
														# into a single list, but with generators
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														_SCREAMING_SNAKE_CASE				= next(self.subiterator      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								# Extremely similar to PipelineIterator in its unpacking mechanism
								# BUT, we have an extra required item which is the presence of `is_last`
								# That is because everything is flattened by `PipelineChunkIterator` we
								# need to keep track of how to regroup here in the original `process`
								# boundaries so that `process` and `postprocess` see the same data.

								# This iterator accumulates items (possibly while unbatching) until it
								# its a `is_last` and then just passes it on to the caller.
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= []
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
														while self._loader_batch_index < self.loader_batch_size:
																				_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
																				if is_last:
																										return accumulator

								while not is_last:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														if self.loader_batch_size is not None:
																				if isinstance(UpperCamelCase					,						torch.Tensor      ):
																										_SCREAMING_SNAKE_CASE				= processed
																				else:
																										_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																										_SCREAMING_SNAKE_CASE				= processed[key]
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
																				else:
																										_SCREAMING_SNAKE_CASE				= first_tensor.shape[0]
																				if 0 < observed_batch_size < self.loader_batch_size:
																										# could be last batch so we can't unroll as many
																										# elements.
																										_SCREAMING_SNAKE_CASE				= observed_batch_size
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= 0
																				while self._loader_batch_index < self.loader_batch_size:
																										_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																										_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																										accumulator.append(UpperCamelCase      )
																										if is_last:
																																return accumulator
														else:
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
								return accumulator


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= key




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								return self.dataset[i][self.key]


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= keya
								_SCREAMING_SNAKE_CASE				= keya




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}