"""simple docstring"""






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase    :       List[Any]					     =get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")





@require_sentencepiece
@require_tokenizers
class  _A						(       lowerCAmelCase  ,    unittest.TestCase	):
      snake_case__			:   Optional[Any]							  =	PegasusTokenizer
      snake_case__			:   List[str]							  =	PegasusTokenizerFast
      snake_case__			:   Union[str, Any]							  =	True
      snake_case__			:   int							  =	True

      def 				A__       (						self						):







            """simple docstring"""



            super().setUp()

            # We have a SentencePiece fixture for testing
            lowercase														=	PegasusTokenizer(__lowerCAmelCase						)
            tokenizer.save_pretrained(self.tmpdirname						)

      @cached_property
      def 				A__       (						self						):







            """simple docstring"""



            return PegasusTokenizer.from_pretrained("""google/pegasus-large"""						)

      def 				A__       (						self		,       **__lowerCAmelCase						):







            """simple docstring"""



            return PegasusTokenizer.from_pretrained(self.tmpdirname		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            return ("This is a test", "This is a test")

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	"""</s>"""
            lowercase														=	1

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

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	list(self.get_tokenizer().get_vocab().keys()						)

            self.assertEqual(vocab_keys[0]		,       """<pad>"""						)
            self.assertEqual(vocab_keys[1]		,       """</s>"""						)
            self.assertEqual(vocab_keys[-1]		,       """v"""						)
            self.assertEqual(len(__lowerCAmelCase						)		,       1103						)

      def 				A__       (						self						):







            """simple docstring"""



            self.assertEqual(self.get_tokenizer().vocab_size		,       1103						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
            lowercase														=	self.tokenizer_class.from_pretrained(self.tmpdirname						)
            lowercase														=	(
                """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
                """ </s> <pad> <pad> <pad>"""
            )
            lowercase														=	rust_tokenizer([raw_input_str]		,       return_tensors=__lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase						).input_ids[0]
            lowercase														=	py_tokenizer([raw_input_str]		,       return_tensors=__lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase						).input_ids[0]
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self._large_tokenizer
            # <mask_1> masks whole sentence while <mask_2> masks single word
            lowercase														=	"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
            lowercase														=	[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
            lowercase														=	tokenizer([raw_input_str]		,       return_tensors=__lowerCAmelCase						).input_ids[0]
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self._large_tokenizer
            # The tracebacks for the following asserts are **better** without messages or self.assertEqual
            assert tokenizer.vocab_size == 9_6103
            assert tokenizer.pad_token_id == 0
            assert tokenizer.eos_token_id == 1
            assert tokenizer.offset == 103
            assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
            assert tokenizer.unk_token == "<unk>"
            assert tokenizer.model_max_length == 1024
            lowercase														=	"""To ensure a smooth flow of bank resolutions."""
            lowercase														=	[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
            lowercase														=	tokenizer([raw_input_str]		,       return_tensors=__lowerCAmelCase						).input_ids[0]
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)
            assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]						) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

      @require_torch
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	["""This is going to be way too long.""" * 150, """short example"""]
            lowercase														=	["""not super long but more than 5 tokens""", """tiny"""]
            lowercase														=	self._large_tokenizer(__lowerCAmelCase		,       padding=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       return_tensors="""pt"""						)
            lowercase														=	self._large_tokenizer(
                text_target=__lowerCAmelCase		,       max_length=5		,       padding=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       return_tensors="""pt"""						)

            assert batch.input_ids.shape == (2, 1024)
            assert batch.attention_mask.shape == (2, 1024)
            assert targets["input_ids"].shape == (2, 5)
            assert len(__lowerCAmelCase						) == 2  # input_ids, attention_mask.







      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], """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, 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, 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=__lowerCAmelCase		,       model_name="""google/bigbird-pegasus-large-arxiv"""		,       revision="""ba85d0851d708441f91440d509690f1ab6353415"""		,       )






@require_sentencepiece
@require_tokenizers
class  _A						(       lowerCAmelCase  ,    unittest.TestCase	):
      snake_case__			:   str							  =	PegasusTokenizer
      snake_case__			:   str							  =	PegasusTokenizerFast
      snake_case__			:   Optional[int]							  =	True
      snake_case__			:   Union[str, Any]							  =	True

      def 				A__       (						self						):







            """simple docstring"""



            super().setUp()

            # We have a SentencePiece fixture for testing
            lowercase														=	PegasusTokenizer(__lowerCAmelCase		,       offset=0		,       mask_token_sent=__lowerCAmelCase		,       mask_token="""[MASK]"""						)
            tokenizer.save_pretrained(self.tmpdirname						)

      @cached_property
      def 				A__       (						self						):







            """simple docstring"""



            return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""						)

      def 				A__       (						self		,       **__lowerCAmelCase						):







            """simple docstring"""



            return PegasusTokenizer.from_pretrained(self.tmpdirname		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            return ("This is a test", "This is a test")

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
            lowercase														=	self.tokenizer_class.from_pretrained(self.tmpdirname						)
            lowercase														=	(
                """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
                """ <pad> <pad> <pad>"""
            )
            lowercase														=	rust_tokenizer([raw_input_str]		,       return_tensors=__lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase						).input_ids[0]
            lowercase														=	py_tokenizer([raw_input_str]		,       return_tensors=__lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase						).input_ids[0]
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)

      @require_torch
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	["""This is going to be way too long.""" * 1000, """short example"""]
            lowercase														=	["""not super long but more than 5 tokens""", """tiny"""]
            lowercase														=	self._large_tokenizer(__lowerCAmelCase		,       padding=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       return_tensors="""pt"""						)
            lowercase														=	self._large_tokenizer(
                text_target=__lowerCAmelCase		,       max_length=5		,       padding=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       return_tensors="""pt"""						)

            assert batch.input_ids.shape == (2, 4096)
            assert batch.attention_mask.shape == (2, 4096)
            assert targets["input_ids"].shape == (2, 5)
            assert len(__lowerCAmelCase						) == 2  # input_ids, attention_mask.







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	(
                """This is an example string that is used to test the original TF implementation against the HF"""
                """ implementation"""
            )

            lowercase														=	self._large_tokenizer(__lowerCAmelCase						).input_ids

            self.assertListEqual(
                __lowerCAmelCase		,       [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]		,       )




"""simple docstring"""






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Tuple							  =	1

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 1000		,       __lowerCAmelCase = None						):







            """simple docstring"""



            self.set_timesteps(__lowerCAmelCase						)

            # standard deviation of the initial noise distribution
            lowercase														=	1.0

            # For now we only support F-PNDM, i.e. the runge-kutta method
            # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
            # mainly at formula (9), (12), (13) and the Algorithm 2.
            lowercase														=	4

            # running values
            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	torch.linspace(1		,       0		,       num_inference_steps + 1						)[:-1]
            lowercase														=	torch.cat([steps, torch.tensor([0.0]						)]						)

            if self.config.trained_betas is not None:
                  lowercase														=	torch.tensor(self.config.trained_betas		,       dtype=torch.floataa						)
            else:
                  lowercase														=	torch.sin(steps * math.pi / 2						) ** 2

            lowercase														=	(1.0 - self.betas**2) ** 0.5

            lowercase														=	(torch.atana(self.betas		,       self.alphas						) / math.pi * 2)[:-1]
            lowercase														=	timesteps.to(__lowerCAmelCase						)

            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       ):







            """simple docstring"""



            if self.num_inference_steps is None:
                  raise ValueError(
                      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

            lowercase														=	(self.timesteps == timestep).nonzero().item()
            lowercase														=	timestep_index + 1

            lowercase														=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
            self.ets.append(__lowerCAmelCase						)

            if len(self.ets						) == 1:
                  lowercase														=	self.ets[-1]
            elif len(self.ets						) == 2:
                  lowercase														=	(3 * self.ets[-1] - self.ets[-2]) / 2
            elif len(self.ets						) == 3:
                  lowercase														=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
            else:
                  lowercase														=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

            lowercase														=	self._get_prev_sample(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.alphas[timestep_index]
            lowercase														=	self.betas[timestep_index]

            lowercase														=	self.alphas[prev_timestep_index]
            lowercase														=	self.betas[prev_timestep_index]

            lowercase														=	(sample - sigma * ets) / max(__lowerCAmelCase		,       1E-8						)
            lowercase														=	next_alpha * pred + ets * next_sigma

            return prev_sample







      def __len__(						self						):







            """simple docstring"""



            return self.config.num_train_timesteps



