from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowerCamelCase_					(       a__			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase)	->     Dict:
        super().__init__()

        # make sure scheduler can always be converted to DDIM
        __UpperCamelCase					:Dict        =		DDIMScheduler.from_config(scheduler.config)

        self.register_modules(unet=__lowercase			,			scheduler=__lowercase)







    @torch.no_grad()
    def __call__(     self			,			__lowercase = 1			,			__lowercase = None			,			__lowercase = 0.0			,			__lowercase = 50			,			__lowercase = None			,			__lowercase = "pil"			,			__lowercase = True			,			)	->     Tuple:

        # Sample gaussian noise to begin loop
        if isinstance(self.unet.config.sample_size			,			__lowercase):
            __UpperCamelCase					:Any        =		(
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size,
                self.unet.config.sample_size,
            )
        else:
            __UpperCamelCase					:Dict        =		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

        if isinstance(__lowercase			,			__lowercase) and len(__lowercase) != batch_size:
            raise ValueError(
                f"""You have passed a list of generators of length {len(__lowercase)}, but requested an effective batch"""
                f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

        __UpperCamelCase					:str        =		randn_tensor(__lowercase			,			generator=__lowercase			,			device=self.device			,			dtype=self.unet.dtype)

        # set step values
        self.scheduler.set_timesteps(__lowercase)

        for t in self.progress_bar(self.scheduler.timesteps):
            # 1. predict noise model_output
            __UpperCamelCase					:Any        =		self.unet(__lowercase			,			__lowercase).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            __UpperCamelCase					:Union[str, Any]        =		self.scheduler.step(
                __lowercase			,			__lowercase			,			__lowercase			,			eta=__lowercase			,			use_clipped_model_output=__lowercase			,			generator=__lowercase).prev_sample

        __UpperCamelCase					:Any        =		(image / 2 + 0.5).clamp(0			,			1)
        __UpperCamelCase					:Optional[Any]        =		image.cpu().permute(0			,			2			,			3			,			1).numpy()
        if output_type == "pil":
            __UpperCamelCase					:str        =		self.numpy_to_pil(__lowercase)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=__lowercase)






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						List[Any]      =				PhobertTokenizer
    a__     :						Union[str, Any]      =				False







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __UpperCamelCase					:Union[str, Any]        =		['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
        __UpperCamelCase					:int        =		dict(zip(__lowercase			,			range(len(__lowercase))))
        __UpperCamelCase					:Dict        =		['''#version: 0.2''', '''l à</w>''']
        __UpperCamelCase					:Any        =		{'''unk_token''': '''<unk>'''}

        __UpperCamelCase					:int        =		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file'''])
        __UpperCamelCase					:Optional[int]        =		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file'''])

        with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8''') as fp:
            for token in vocab_tokens:
                fp.write(f"""{token} {vocab_tokens[token]}\n""")
        with open(self.merges_file			,			'''w'''			,			encoding='''utf-8''') as fp:
            fp.write('''\n'''.join(__lowercase))







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Optional[Any]:
        kwargs.update(self.special_tokens_map)
        return PhobertTokenizer.from_pretrained(self.tmpdirname			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        __UpperCamelCase					:List[Any]        =		'''Tôi là VinAI Research'''
        __UpperCamelCase					:List[str]        =		'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
        return input_text, output_text







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Dict        =		PhobertTokenizer(self.vocab_file			,			self.merges_file			,			**self.special_tokens_map)
        __UpperCamelCase					:List[Any]        =		'''Tôi là VinAI Research'''
        __UpperCamelCase					:List[str]        =		'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
        __UpperCamelCase					:int        =		tokenizer.tokenize(__lowercase)
        print(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:Dict        =		tokens + [tokenizer.unk_token]

        __UpperCamelCase					:Any        =		[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)			,			__lowercase)





