
'''simple docstring'''







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case				:   List[Any]     =				get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")


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




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

								# We have a SentencePiece fixture for testing
								_SCREAMING_SNAKE_CASE				= GPTSwaTokenizer(_lowerCAmelCase					,						eos_token="<unk>"					,						bos_token="<unk>"					,						pad_token="<unk>"      )

								tokenizer.save_pretrained(self.tmpdirname      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= "This is a test"
								_SCREAMING_SNAKE_CASE				= "This is a test"
								return input_text, output_text




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "<s>"
								_SCREAMING_SNAKE_CASE				= 1

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




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

								self.assertEqual(vocab_keys[0]					,						"<unk>"      )
								self.assertEqual(vocab_keys[1]					,						"<s>"      )
								self.assertEqual(vocab_keys[-1]					,						"j"      )
								self.assertEqual(len(_lowerCAmelCase      )					,						2_000      )




		def     lowercase       (				self      ):
								self.assertEqual(self.get_tokenizer().vocab_size					,						2_000      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GPTSwaTokenizer(_lowerCAmelCase      )

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

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase      )					,						[465, 287, 265, 631, 842]      )

								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize("I was born in 92000, and this is falsé."      )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase					,						["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]					,						)
								# fmt: on

								_SCREAMING_SNAKE_CASE				= tokenizer.convert_tokens_to_ids(_lowerCAmelCase      )
								self.assertListEqual(
								    _lowerCAmelCase					,						[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,						)

								_SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(_lowerCAmelCase      )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase					,						["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]      )
								# fmt: on




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GPTSwaTokenizer(_lowerCAmelCase      )
								_SCREAMING_SNAKE_CASE				= ["This is a test", "I was born in 92000, and this is falsé."]
								_SCREAMING_SNAKE_CASE				= [
								    [465, 287, 265, 631, 842],
								    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
								]

								# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
								for text, expected_ids in zip(_lowerCAmelCase					,						_lowerCAmelCase      ):
														self.assertListEqual(tokenizer.encode_fast(_lowerCAmelCase      )					,						_lowerCAmelCase      )

								# Test that decode_fast returns the input text
								for text, token_ids in zip(_lowerCAmelCase					,						_lowerCAmelCase      ):
														self.assertEqual(tokenizer.decode_fast(_lowerCAmelCase      )					,						_lowerCAmelCase      )




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= [
								    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
								    "Hey there, how are you doing this fine day?",
								    "This is a text with a trailing spaces followed by a dot     .",
								    "Häj sväjs lillebrör! =)",
								    "Det är inget fel på Mr. Cool",
								]

								# fmt: off
								_SCREAMING_SNAKE_CASE				= {"input_ids": [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[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, 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]], "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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
								# fmt: on
								self.tokenizer_integration_test_util(
								    expected_encoding=_lowerCAmelCase					,						model_name="AI-Sweden/gpt-sw3-126m"					,						sequences=_lowerCAmelCase					,						)

'''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]}