'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




				'''simple docstring'''







				snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






				def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
									    'num_train_timesteps': 1_000,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									    'variance_type': 'fixed_small',
									    'clip_sample': True,
									}

									config.update(**lowercase__ )
									return config






				def       _UpperCamelCase    (       self )							-> int:
									for timesteps in [1, 5, 100, 1_000]:
														self.check_over_configs(num_train_timesteps=lowercase__ )






				def       _UpperCamelCase    (       self )							-> Optional[Any]:
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
														self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






				def       _UpperCamelCase    (       self )							-> List[Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=lowercase__ )






				def       _UpperCamelCase    (       self )							-> str:
									for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=lowercase__ )






				def       _UpperCamelCase    (       self )							-> Optional[Any]:
									for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=lowercase__ )






				def       _UpperCamelCase    (       self )							-> List[str]:
									self.check_over_configs(thresholding=lowercase__ )
									for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






				def       _UpperCamelCase    (       self )							-> Dict:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(prediction_type=lowercase__ )






				def       _UpperCamelCase    (       self )							-> Optional[Any]:
									for t in [0, 500, 999]:
														self.check_over_forward(time_step=lowercase__ )






				def       _UpperCamelCase    (       self )							-> Any:
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

									assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






				def       _UpperCamelCase    (       self )							-> int:
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
									SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
									SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
									SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

									SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
									SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

									assert abs(result_sum.item() - 1_153.1_833 ) < 1E-2
									assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






				def       _UpperCamelCase    (       self )							-> Tuple:
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

									SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
									SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

									for t in reversed(range(lowercase__ ) ):
														# 1. predict noise residual
														SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

														# 2. predict previous mean of sample x_t-1
														SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

														SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
									SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

									assert abs(result_sum.item() - 258.9_606 ) < 1E-2
									assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






				def       _UpperCamelCase    (       self )							-> List[str]:
									SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

									SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

									SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
									SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
									SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

									for t in reversed(range(lowercase__ ) ):
														# 1. predict noise residual
														SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

														# 2. predict previous mean of sample x_t-1
														SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

														SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
									SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

									assert abs(result_sum.item() - 202.0_296 ) < 1E-2
									assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






				def       _UpperCamelCase    (       self )							-> List[str]:
									SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

									SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

									scheduler.set_timesteps(timesteps=lowercase__ )

									SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

									for i, timestep in enumerate(lowercase__ ):
														if i == len(lowercase__ ) - 1:
																			SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
														else:
																			SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

														SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
														SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

														self.assertEqual(lowercase__    ,							lowercase__ )






				def       _UpperCamelCase    (       self )							-> Optional[int]:
									SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

									SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

									with self.assertRaises(lowercase__    ,							msg='`custom_timesteps` must be in descending order.' ):
														scheduler.set_timesteps(timesteps=lowercase__ )






				def       _UpperCamelCase    (       self )							-> Union[str, Any]:
									SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

									SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
									SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

									with self.assertRaises(lowercase__    ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
														scheduler.set_timesteps(num_inference_steps=lowercase__    ,							timesteps=lowercase__ )






				def       _UpperCamelCase    (       self )							-> List[str]:
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

									SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowercase__    ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,							):
														scheduler.set_timesteps(timesteps=lowercase__ )




'''simple docstring'''





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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   int							=						CLIPTokenizer
    snake_case__							:   Union[str, Any]							=						CLIPTokenizerFast
    snake_case__							:   str							=						True
    snake_case__							:   Optional[int]							=						{}
    snake_case__							:   int							=						False






    def       _UpperCamelCase    (       self )							-> Tuple:
         super().setUp()

         # fmt: off
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['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
         SCREAMING_SNAKE_CASE					:			int    	=       dict(zip(lowercase__    ,							range(len(lowercase__ ) ) ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
         SCREAMING_SNAKE_CASE					:			List[str]    	=       {'unk_token': '<unk>'}

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['vocab_file'] )
         SCREAMING_SNAKE_CASE					:			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(lowercase__ ) + '\n' )
         with open(self.merges_file    ,							'w'    ,							encoding='utf-8' ) as fp:
              fp.write('\n'.join(lowercase__ ) )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Dict:
         kwargs.update(self.special_tokens_map )
         return CLIPTokenizer.from_pretrained(self.tmpdirname    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Union[str, Any]:
         kwargs.update(self.special_tokens_map )
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'lower newer'
         SCREAMING_SNAKE_CASE					:			Dict    	=       'lower newer'
         return input_text, output_text






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       CLIPTokenizer(self.vocab_file    ,							self.merges_file    ,							**self.special_tokens_map )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       'lower newer'
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tokenizer.tokenize(lowercase__ )
         self.assertListEqual(lowercase__    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       tokens + [tokenizer.unk_token]
         SCREAMING_SNAKE_CASE					:			int    	=       [10, 2, 16, 9, 3, 2, 16, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__ )    ,							lowercase__ )






    @require_ftfy
    def       _UpperCamelCase    (       self )							-> List[str]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                   SCREAMING_SNAKE_CASE					:			Dict    	=       self.tokenizer_class.from_pretrained(lowercase__    ,							**lowercase__ )
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.rust_tokenizer_class.from_pretrained(lowercase__    ,							**lowercase__ )

                   SCREAMING_SNAKE_CASE					:			Tuple    	=       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenizer_s.tokenize(lowercase__ )
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenizer_r.tokenize(lowercase__ )

                   self.assertListEqual(lowercase__    ,							lowercase__ )

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

                   self.assertListEqual(lowercase__    ,							lowercase__ )

                   # Test that the tokenization is identical on unicode of space type
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
                       '\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:
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer_s.tokenize(lowercase__ )
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tokenizer_r.tokenize(lowercase__ )

                        self.assertListEqual(lowercase__    ,							lowercase__ )

                   # Test that the tokenization is identical on unicode of line break type
                   SCREAMING_SNAKE_CASE					:			int    	=       [
                       '\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:
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       tokenizer_s.tokenize(lowercase__ )
                        SCREAMING_SNAKE_CASE					:			int    	=       tokenizer_r.tokenize(lowercase__ )

                        self.assertListEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         # 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})""" ):
                   SCREAMING_SNAKE_CASE					:			str    	=       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       F"""{text_of_1_token} {text_of_1_token}"""

                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							)
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer_r(lowercase__    ,							return_offsets_mapping=lowercase__    ,							add_special_tokens=lowercase__ )
                   self.assertEqual(encoding.offset_mapping[0]    ,							(0, len(lowercase__ )) )
                   self.assertEqual(
                       encoding.offset_mapping[1]    ,							(len(lowercase__ ) + 1, len(lowercase__ ) + 1 + len(lowercase__ ))    ,							)

                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       F""" {text}"""

                   SCREAMING_SNAKE_CASE					:			Any    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							)
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tokenizer_r(lowercase__    ,							return_offsets_mapping=lowercase__    ,							add_special_tokens=lowercase__ )
                   self.assertEqual(encoding.offset_mapping[0]    ,							(1, 1 + len(lowercase__ )) )
                   self.assertEqual(
                       encoding.offset_mapping[1]    ,							(1 + len(lowercase__ ) + 1, 1 + len(lowercase__ ) + 1 + len(lowercase__ ))    ,							)






    def       _UpperCamelCase    (       self )							-> int:
         # 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(lowercase__ ) 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       _UpperCamelCase    (       self )							-> Union[str, Any]:
         super().test_tokenization_python_rust_equals()






    def       _UpperCamelCase    (       self )							-> int:
         # CLIP always lower cases letters
         pass



