






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCamelCase__			= False






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		List[Any]=32	):

											'''simple docstring'''
											set_seed(0	)
											__a       =		UNetaDModel(sample_size=UpperCamelCase__      ,				in_channels=3      ,				out_channels=3	)
											__a       =		torch.optim.SGD(model.parameters()      ,				lr=0.0001	)
											return model, optimizer







							@slow
							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
											__a       =		DDPMScheduler(
											    num_train_timesteps=1000      ,				beta_start=0.0001      ,				beta_end=0.02      ,				beta_schedule="""linear"""      ,				clip_sample=UpperCamelCase__      ,				)
											__a       =		DDIMScheduler(
											    num_train_timesteps=1000      ,				beta_start=0.0001      ,				beta_end=0.02      ,				beta_schedule="""linear"""      ,				clip_sample=UpperCamelCase__      ,				)

											assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

											# shared batches for DDPM and DDIM
											set_seed(0	)
											__a       =		[torch.randn((4, 3, 32, 32)	).clip(-1      ,				1	).to(UpperCamelCase__	) for _ in range(4	)]
											__a       =		[torch.randn((4, 3, 32, 32)	).to(UpperCamelCase__	) for _ in range(4	)]
											__a       =		[torch.randint(0      ,				1000      ,				(4,)	).long().to(UpperCamelCase__	) for _ in range(4	)]

											# train with a DDPM scheduler
											__a       =		self.get_model_optimizer(resolution=32	)
											model.train().to(UpperCamelCase__	)
											for i in range(4	):
															optimizer.zero_grad()
															__a       =		ddpm_scheduler.add_noise(clean_images[i]      ,				noise[i]      ,				timesteps[i]	)
															__a       =		model(UpperCamelCase__      ,				timesteps[i]	).sample
															__a       =		torch.nn.functional.mse_loss(UpperCamelCase__      ,				noise[i]	)
															loss.backward()
															optimizer.step()
											del model, optimizer

											# recreate the model and optimizer, and retry with DDIM
											__a       =		self.get_model_optimizer(resolution=32	)
											model.train().to(UpperCamelCase__	)
											for i in range(4	):
															optimizer.zero_grad()
															__a       =		ddim_scheduler.add_noise(clean_images[i]      ,				noise[i]      ,				timesteps[i]	)
															__a       =		model(UpperCamelCase__      ,				timesteps[i]	).sample
															__a       =		torch.nn.functional.mse_loss(UpperCamelCase__      ,				noise[i]	)
															loss.backward()
															optimizer.step()
											del model, optimizer

											self.assertTrue(torch.allclose(UpperCamelCase__      ,				UpperCamelCase__      ,				atol=1E-5	)	)
											self.assertTrue(torch.allclose(UpperCamelCase__      ,				UpperCamelCase__      ,				atol=1E-5	)	)





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =BlenderbotSmallTokenizer
							__lowerCamelCase	:	str    =False





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

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

											__a       =		["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
											__a       =		dict(zip(__lowercase      ,				range(len(__lowercase	)	)	)	)

											__a       =		["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
											__a       =		{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
											__a       =		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		:		Optional[Any]      ,				**__lowercase		:		Dict	):

											'''simple docstring'''
											kwargs.update(self.special_tokens_map	)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		"""adapt act apte"""
											__a       =		"""adapt act apte"""
											return input_text, output_text





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map	)
											__a       =		"""adapt act apte"""
											__a       =		["""adapt""", """act""", """ap@@""", """te"""]
											__a       =		tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											__a       =		[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase	)      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											assert tok("""sam"""	).input_ids == [1384]
											__a       =		"""I am a small frog."""
											__a       =		tok([src_text]      ,				padding=__lowercase      ,				truncation=__lowercase	)["""input_ids"""]
											__a       =		tok.batch_decode(__lowercase      ,				skip_special_tokens=__lowercase      ,				clean_up_tokenization_spaces=__lowercase	)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											__a       =		"""I am a small frog ."""
											__a       =		"""."""
											__a       =		tok(__lowercase	)["""input_ids"""]
											__a       =		tok(__lowercase	)["""input_ids"""]

											assert encoded[-1] == encoded_dot[0]
