


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class   __a      (   __UpperCamelCase				):
 __lowercase			:							int       					=			(UniPCMultistepScheduler,)
 __lowercase			:							List[str]       					=			(('num_inference_steps', 25),)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        lowercase__:       List[str]        =       {
            'num_train_timesteps': 1_000,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'solver_type': 'bh2',
        }

        config.update(**lowerCAmelCase__			)
        return config
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=0    ,			**lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       dict(self.forward_default_kwargs			)
        lowercase__:       List[Any]        =       kwargs.pop('num_inference_steps'    ,			lowerCAmelCase__			)
        lowercase__:       Tuple        =       self.dummy_sample
        lowercase__:       List[str]        =       0.1 * sample
        lowercase__:       List[Any]        =       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
               lowercase__:       Dict        =       self.get_scheduler_config(**lowerCAmelCase__			)
               lowercase__:       Any        =       scheduler_class(**lowerCAmelCase__			)
               scheduler.set_timesteps(lowerCAmelCase__			)
               # copy over dummy past residuals
               lowercase__:       List[Any]        =       dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__			)
                      lowercase__:       List[str]        =       scheduler_class.from_pretrained(lowerCAmelCase__			)
                      new_scheduler.set_timesteps(lowerCAmelCase__			)
                      # copy over dummy past residuals
                      lowercase__:       str        =       dummy_past_residuals[: new_scheduler.config.solver_order]

               lowercase__      ,						lowercase__:       List[str]        =       sample, sample
               for t in range(lowerCAmelCase__    ,			time_step + scheduler.config.solver_order + 1			):
                      lowercase__:       Optional[Any]        =       scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
                      lowercase__:       Optional[int]        =       new_scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

                      assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=0    ,			**lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Any        =       dict(self.forward_default_kwargs			)
        lowercase__:       Dict        =       kwargs.pop('num_inference_steps'    ,			lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       self.dummy_sample
        lowercase__:       List[Any]        =       0.1 * sample
        lowercase__:       List[str]        =       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
               lowercase__:       Dict        =       self.get_scheduler_config()
               lowercase__:       Dict        =       scheduler_class(**lowerCAmelCase__			)
               scheduler.set_timesteps(lowerCAmelCase__			)

               # copy over dummy past residuals (must be after setting timesteps)
               lowercase__:       List[Any]        =       dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__			)
                      lowercase__:       str        =       scheduler_class.from_pretrained(lowerCAmelCase__			)
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(lowerCAmelCase__			)

                      # copy over dummy past residual (must be after setting timesteps)
                      lowercase__:       Union[str, Any]        =       dummy_past_residuals[: new_scheduler.config.solver_order]

               lowercase__:       List[Any]        =       scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       Optional[int]        =       new_scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

               assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=None    ,			**lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        if scheduler is None:
               lowercase__:       Optional[int]        =       self.scheduler_classes[0]
               lowercase__:       List[Any]        =       self.get_scheduler_config(**lowerCAmelCase__			)
               lowercase__:       Optional[int]        =       scheduler_class(**lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       self.scheduler_classes[0]
        lowercase__:       Dict        =       self.get_scheduler_config(**lowerCAmelCase__			)
        lowercase__:       str        =       scheduler_class(**lowerCAmelCase__			)

        lowercase__:       int        =       10
        lowercase__:       str        =       self.dummy_model()
        lowercase__:       Union[str, Any]        =       self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase__			)

        for i, t in enumerate(scheduler.timesteps			):
               lowercase__:       Optional[Any]        =       model(lowerCAmelCase__    ,			lowerCAmelCase__			)
               lowercase__:       Dict        =       scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			).prev_sample

        return sample
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       int        =       dict(self.forward_default_kwargs			)

        lowercase__:       Optional[int]        =       kwargs.pop('num_inference_steps'    ,			lowerCAmelCase__			)

        for scheduler_class in self.scheduler_classes:
               lowercase__:       Any        =       self.get_scheduler_config()
               lowercase__:       Union[str, Any]        =       scheduler_class(**lowerCAmelCase__			)

               lowercase__:       Dict        =       self.dummy_sample
               lowercase__:       Optional[Any]        =       0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCAmelCase__    ,			'set_timesteps'			):
                      scheduler.set_timesteps(lowerCAmelCase__			)
               elif num_inference_steps is not None and not hasattr(lowerCAmelCase__    ,			'set_timesteps'			):
                      lowercase__:       Dict        =       num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               lowercase__:       str        =       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
               lowercase__:       Any        =       dummy_past_residuals[: scheduler.config.solver_order]

               lowercase__:       Optional[int]        =       scheduler.timesteps[5]
               lowercase__:       str        =       scheduler.timesteps[6]

               lowercase__:       Dict        =       scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       Dict        =       scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

               self.assertEqual(output_a.shape    ,			sample.shape			)
               self.assertEqual(output_a.shape    ,			output_a.shape			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        lowercase__:       str        =       UniPCMultistepScheduler(**self.get_scheduler_config()			)
        lowercase__:       Optional[Any]        =       self.full_loop(scheduler=lowerCAmelCase__			)
        lowercase__:       str        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_mean.item() - 0.2_4_6_4			) < 1E-3

        lowercase__:       Union[str, Any]        =       DPMSolverSinglestepScheduler.from_config(scheduler.config			)
        lowercase__:       str        =       DEISMultistepScheduler.from_config(scheduler.config			)
        lowercase__:       Optional[Any]        =       DPMSolverMultistepScheduler.from_config(scheduler.config			)
        lowercase__:       List[Any]        =       UniPCMultistepScheduler.from_config(scheduler.config			)

        lowercase__:       List[str]        =       self.full_loop(scheduler=lowerCAmelCase__			)
        lowercase__:       int        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_mean.item() - 0.2_4_6_4			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        for timesteps in [25, 50, 100, 999, 1_000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        self.check_over_configs(thresholding=lowerCAmelCase__			)
        for order in [1, 2, 3]:
               for solver_type in ["bh1", "bh2"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=lowerCAmelCase__    ,			prediction_type=lowerCAmelCase__    ,			sample_max_value=lowerCAmelCase__    ,			solver_order=lowerCAmelCase__    ,			solver_type=lowerCAmelCase__    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        for solver_type in ["bh1", "bh2"]:
               for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 solver_order=lowerCAmelCase__    ,			solver_type=lowerCAmelCase__    ,			prediction_type=lowerCAmelCase__    ,			)
                             lowercase__:       List[Any]        =       self.full_loop(
                                 solver_order=lowerCAmelCase__    ,			solver_type=lowerCAmelCase__    ,			prediction_type=lowerCAmelCase__    ,			)
                             assert not torch.isnan(lowerCAmelCase__			).any(), "Samples have nan numbers"
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        self.check_over_configs(lower_order_final=lowerCAmelCase__			)
        self.check_over_configs(lower_order_final=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
               self.check_over_forward(num_inference_steps=lowerCAmelCase__    ,			time_step=0			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.full_loop()
        lowercase__:       Tuple        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_mean.item() - 0.2_4_6_4			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       str        =       self.full_loop(prediction_type='v_prediction'			)
        lowercase__:       Dict        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_mean.item() - 0.1_0_1_4			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       self.scheduler_classes[0]
        lowercase__:       List[Any]        =       self.get_scheduler_config(thresholding=lowerCAmelCase__    ,			dynamic_thresholding_ratio=0			)
        lowercase__:       List[Any]        =       scheduler_class(**lowerCAmelCase__			)

        lowercase__:       str        =       10
        lowercase__:       Optional[Any]        =       self.dummy_model()
        lowercase__:       Union[str, Any]        =       self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCAmelCase__			)

        for i, t in enumerate(scheduler.timesteps			):
               lowercase__:       Optional[Any]        =       model(lowerCAmelCase__    ,			lowerCAmelCase__			)
               lowercase__:       List[Any]        =       scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			).prev_sample

        assert sample.dtype == torch.floataa
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        for scheduler_class in self.scheduler_classes:
               lowercase__:       List[Any]        =       self.get_scheduler_config(**lowerCAmelCase__			)
               lowercase__:       Union[str, Any]        =       scheduler_class(**lowerCAmelCase__			)

               scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
               assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps






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


