


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      (   __UpperCamelCase	,	__UpperCamelCase				):
 __lowercase			:							int       					=			1
 @register_to_config
 def __init__(       self    ,			lowerCAmelCase__ = 1_000    ,			lowerCAmelCase__ = None			) ->    Optional[int]:




        '''simple docstring'''
        # set `betas`, `alphas`, `timesteps`
        self.set_timesteps(lowerCAmelCase__			)

        # standard deviation of the initial noise distribution
        lowercase__:       Tuple        =       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__:       Tuple        =       4

        # running values
        lowercase__:       Tuple        =       []
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    str:




        '''simple docstring'''
        lowercase__:       Dict        =       num_inference_steps
        lowercase__:       Dict        =       torch.linspace(1    ,			0    ,			num_inference_steps + 1			)[:-1]
        lowercase__:       int        =       torch.cat([steps, torch.tensor([0.0]			)]			)

        if self.config.trained_betas is not None:
               lowercase__:       List[str]        =       torch.tensor(self.config.trained_betas    ,			dtype=torch.floataa			)
        else:
               lowercase__:       Tuple        =       torch.sin(steps * math.pi / 2			) ** 2

        lowercase__:       int        =       (1.0 - self.betas**2) ** 0.5

        lowercase__:       int        =       (torch.atana(self.betas    ,			self.alphas			) / math.pi * 2)[:-1]
        lowercase__:       Dict        =       timesteps.to(lowerCAmelCase__			)

        lowercase__:       int        =       []
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = True    ,			) ->    Union[SchedulerOutput, Tuple]:




        '''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__:       int        =       (self.timesteps == timestep).nonzero().item()
        lowercase__:       List[Any]        =       timestep_index + 1

        lowercase__:       Union[str, Any]        =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
        self.ets.append(lowerCAmelCase__			)

        if len(self.ets			) == 1:
               lowercase__:       Tuple        =       self.ets[-1]
        elif len(self.ets			) == 2:
               lowercase__:       List[str]        =       (3 * self.ets[-1] - self.ets[-2]) / 2
        elif len(self.ets			) == 3:
               lowercase__:       Union[str, Any]        =       (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
        else:
               lowercase__:       Optional[Any]        =       (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

        lowercase__:       int        =       self._get_prev_sample(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    torch.FloatTensor:




        '''simple docstring'''
        return sample
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       self.alphas[timestep_index]
        lowercase__:       Union[str, Any]        =       self.betas[timestep_index]

        lowercase__:       str        =       self.alphas[prev_timestep_index]
        lowercase__:       Union[str, Any]        =       self.betas[prev_timestep_index]

        lowercase__:       int        =       (sample - sigma * ets) / max(lowerCAmelCase__    ,			1E-8			)
        lowercase__:       List[str]        =       next_alpha * pred + ets * next_sigma

        return prev_sample
 def __len__(       self			) ->    List[str]:




        '''simple docstring'''
        return self.config.num_train_timesteps






import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Optional[int]       					=			CLIPTokenizer
 __lowercase			:							str       					=			CLIPTokenizerFast
 __lowercase			:							Tuple       					=			True
 __lowercase			:							str       					=			{}
 __lowercase			:							Dict       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        super().setUp()

        # fmt: off
        lowercase__:       str        =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
        # fmt: on
        lowercase__:       List[str]        =       dict(zip(lowerCAmelCase__    ,			range(len(lowerCAmelCase__			)			)			)			)
        lowercase__:       int        =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
        lowercase__:       Optional[int]        =       {'unk_token': '<unk>'}

        lowercase__:       int        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       Union[str, Any]        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['merges_file']			)
        with open(self.vocab_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write(json.dumps(lowerCAmelCase__			) + '\n'			)
        with open(self.merges_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write('\n'.join(lowerCAmelCase__			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizer.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Dict        =       'lower newer'
        return input_text, output_text
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       CLIPTokenizer(self.vocab_file    ,			self.merges_file    ,			**self.special_tokens_map			)
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Union[str, Any]        =       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
        lowercase__:       Any        =       tokenizer.tokenize(lowerCAmelCase__			)
        self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

        lowercase__:       Tuple        =       tokens + [tokenizer.unk_token]
        lowercase__:       Tuple        =       [10, 2, 16, 9, 3, 2, 16, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__			)    ,			lowerCAmelCase__			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       List[str]        =       self.tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)
                      lowercase__:       Optional[int]        =       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)

                      lowercase__:       Optional[int]        =       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
                      lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       Dict        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                      # with Tilde) encoded in 2 different ways
                      lowercase__:       Dict        =       'xa\u0303y' + ' ' + 'x\xe3y'
                      lowercase__:       Tuple        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       int        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of space type
                      lowercase__:       str        =       [
                          '\u0009',  # (horizontal tab, '\t')
                          '\u000B',  # (vertical tab)
                          '\u000C',  # (form feed)
                          '\u0020',  # (space, ' ')
                          '\u200E',  # (left-to-right mark):w
                          '\u200F',  # (right-to-left mark)
                      ]
                      for unicode_seq in spaces_unicodes:
                             lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Tuple        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of line break type
                      lowercase__:       str        =       [
                          '\u000A',  # (line feed, '\n')
                          '\r\n',  # (carriage return and line feed, '\r\n')
                          '\u000D',  # (carriage return, '\r')
                          '\r',  # (carriage return, '\r')
                          '\u000D',  # (carriage return, '\r')
                          '\u2028',  # (line separator)
                          '\u2029',  # (paragraph separator)
                          # "\u0085", # (next line)
                      ]

                      # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                      # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                      # space (and thus into an empty list).

                      for unicode_seq in line_break_unicodes:
                             lowercase__:       Optional[int]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Optional[int]        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       Optional[int]        =       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                      lowercase__:       Optional[int]        =       F'{text_of_1_token} {text_of_1_token}'

                      lowercase__:       int        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       Dict        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(len(lowerCAmelCase__			) + 1, len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)

                      lowercase__:       Any        =       F' {text}'

                      lowercase__:       Union[str, Any]        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       int        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(1, 1 + len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(1 + len(lowerCAmelCase__			) + 1, 1 + len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        # Test related to the breaking change introduced in transformers v4.17.0
        # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
        with self.assertRaises(lowerCAmelCase__			) as context:
               self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'			)

        self.assertTrue(
            context.exception.args[0].startswith(
                'The `backend_tokenizer` provided does not match the expected format.'			)			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        super().test_tokenization_python_rust_equals()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # CLIP always lower cases letters
        pass


