






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= (IPNDMScheduler,)
	lowerCAmelCase													= (('''num_inference_steps''', 5_0),)




	def 							_UpperCamelCase			(       self       ,			**a					)       ->    Any:
								snake_case_				=							{'num_train_timesteps': 10_00}
								config.update(**a					)
								return config




	def 							_UpperCamelCase			(       self       ,			a=0       ,			**a					)       ->    Tuple:
								snake_case_				=							dict(self.forward_default_kwargs					)
								snake_case_				=							kwargs.pop('num_inference_steps'       ,			a					)
								snake_case_				=							self.dummy_sample
								snake_case_				=							0.1 * sample
								snake_case_				=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
															snake_case_				=							self.get_scheduler_config(**a					)
															snake_case_				=							scheduler_class(**a					)
															scheduler.set_timesteps(a					)
															# copy over dummy past residuals
															snake_case_				=							dummy_past_residuals[:]

															if time_step is None:
																						snake_case_				=							scheduler.timesteps[len(scheduler.timesteps					) // 2]

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(a					)
																						snake_case_				=							scheduler_class.from_pretrained(a					)
																						new_scheduler.set_timesteps(a					)
																						# copy over dummy past residuals
																						snake_case_				=							dummy_past_residuals[:]

															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample
															snake_case_				=							new_scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample

															assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample
															snake_case_				=							new_scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample

															assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								pass




	def 							_UpperCamelCase			(       self       ,			a=0       ,			**a					)       ->    Dict:
								snake_case_				=							dict(self.forward_default_kwargs					)
								snake_case_				=							kwargs.pop('num_inference_steps'       ,			a					)
								snake_case_				=							self.dummy_sample
								snake_case_				=							0.1 * sample
								snake_case_				=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
															snake_case_				=							self.get_scheduler_config()
															snake_case_				=							scheduler_class(**a					)
															scheduler.set_timesteps(a					)

															# copy over dummy past residuals (must be after setting timesteps)
															snake_case_				=							dummy_past_residuals[:]

															if time_step is None:
																						snake_case_				=							scheduler.timesteps[len(scheduler.timesteps					) // 2]

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

																						# copy over dummy past residual (must be after setting timesteps)
																						snake_case_				=							dummy_past_residuals[:]

															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample
															snake_case_				=							new_scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample

															assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample
															snake_case_				=							new_scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample

															assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"




	def 							_UpperCamelCase			(       self       ,			**a					)       ->    List[str]:
								snake_case_				=							self.scheduler_classes[0]
								snake_case_				=							self.get_scheduler_config(**a					)
								snake_case_				=							scheduler_class(**a					)

								snake_case_				=							10
								snake_case_				=							self.dummy_model()
								snake_case_				=							self.dummy_sample_deter
								scheduler.set_timesteps(a					)

								for i, t in enumerate(scheduler.timesteps					):
															snake_case_				=							model(a       ,			a					)
															snake_case_				=							scheduler.step(a       ,			a       ,			a					).prev_sample

								for i, t in enumerate(scheduler.timesteps					):
															snake_case_				=							model(a       ,			a					)
															snake_case_				=							scheduler.step(a       ,			a       ,			a					).prev_sample

								return sample




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							dict(self.forward_default_kwargs					)

								snake_case_				=							kwargs.pop('num_inference_steps'       ,			a					)

								for scheduler_class in self.scheduler_classes:
															snake_case_				=							self.get_scheduler_config()
															snake_case_				=							scheduler_class(**a					)

															snake_case_				=							self.dummy_sample
															snake_case_				=							0.1 * sample

															if num_inference_steps is not None and hasattr(a       ,			'set_timesteps'					):
																						scheduler.set_timesteps(a					)
															elif num_inference_steps is not None and not hasattr(a       ,			'set_timesteps'					):
																						snake_case_				=							num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															snake_case_				=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
															snake_case_				=							dummy_past_residuals[:]

															snake_case_				=							scheduler.timesteps[5]
															snake_case_				=							scheduler.timesteps[6]

															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample
															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample

															self.assertEqual(output_a.shape       ,			sample.shape					)
															self.assertEqual(output_a.shape       ,			output_a.shape					)

															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample
															snake_case_				=							scheduler.step(a       ,			a       ,			a       ,			**a					).prev_sample

															self.assertEqual(output_a.shape       ,			sample.shape					)
															self.assertEqual(output_a.shape       ,			output_a.shape					)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								for timesteps in [1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=a       ,			time_step=a					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								for t, num_inference_steps in zip([1, 5, 10]       ,			[10, 50, 1_00]					):
															self.check_over_forward(num_inference_steps=a       ,			time_step=a					)






	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.full_loop()
								snake_case_				=							torch.mean(torch.abs(a					)					)

								assert abs(result_mean.item() - 2_54_05_29					) < 10













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowercase  							=					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowercase  							=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= SqueezeBertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=True       ,			a="[UNK]"       ,			a="[SEP]"       ,			a="[PAD]"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    Tuple:
								super().__init__(
								    a       ,			tokenizer_file=a       ,			do_lower_case=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			tokenize_chinese_chars=a       ,			strip_accents=a       ,			**a       ,			)

								snake_case_				=							json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
								if (
								    normalizer_state.get('lowercase'       ,			a					) != do_lower_case
								    or normalizer_state.get('strip_accents'       ,			a					) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'       ,			a					) != tokenize_chinese_chars
								):
															snake_case_				=							getattr(a       ,			normalizer_state.pop('type'					)					)
															snake_case_				=							do_lower_case
															snake_case_				=							strip_accents
															snake_case_				=							tokenize_chinese_chars
															snake_case_				=							normalizer_class(**a					)

								snake_case_				=							do_lower_case




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None					)       ->    Tuple:
								snake_case_				=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)





