from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = None			,			)	->     List[Any]:
        super().__init__()
        self.register_modules(transformer=__lowercase			,			vae=__lowercase			,			scheduler=__lowercase)

        # create a imagenet -> id dictionary for easier use
        __UpperCamelCase					:Tuple        =		{}
        if idalabel is not None:
            for key, value in idalabel.items():
                for label in value.split(''','''):
                    __UpperCamelCase					:List[Any]        =		int(__lowercase)
            __UpperCamelCase					:List[str]        =		dict(sorted(self.labels.items()))







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[int]:

        if not isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:Tuple        =		list(__lowercase)

        for l in label:
            if l not in self.labels:
                raise ValueError(
                    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""")

        return [self.labels[l] for l in label]







    @torch.no_grad()
    def __call__(     self			,			__lowercase			,			__lowercase = 4.0			,			__lowercase = None			,			__lowercase = 50			,			__lowercase = "pil"			,			__lowercase = True			,			)	->     Union[ImagePipelineOutput, Tuple]:
        __UpperCamelCase					:str        =		len(__lowercase)
        __UpperCamelCase					:List[Any]        =		self.transformer.config.sample_size
        __UpperCamelCase					:Dict        =		self.transformer.config.in_channels

        __UpperCamelCase					:List[Any]        =		randn_tensor(
            shape=(batch_size, latent_channels, latent_size, latent_size)			,			generator=__lowercase			,			device=self.device			,			dtype=self.transformer.dtype			,			)
        __UpperCamelCase					:List[Any]        =		torch.cat([latents] * 2) if guidance_scale > 1 else latents

        __UpperCamelCase					:List[Any]        =		torch.tensor(__lowercase			,			device=self.device).reshape(-1)
        __UpperCamelCase					:Optional[int]        =		torch.tensor([1_000] * batch_size			,			device=self.device)
        __UpperCamelCase					:Any        =		torch.cat([class_labels, class_null]			,			0) if guidance_scale > 1 else class_labels

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

        for t in self.progress_bar(self.scheduler.timesteps):
            if guidance_scale > 1:
                __UpperCamelCase					:Tuple        =		latent_model_input[: len(__lowercase) // 2]
                __UpperCamelCase					:int        =		torch.cat([half, half]			,			dim=0)
            __UpperCamelCase					:List[Any]        =		self.scheduler.scale_model_input(__lowercase			,			__lowercase)

            __UpperCamelCase					:Any        =		t
            if not torch.is_tensor(__lowercase):
                # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                # This would be a good case for the `match` statement (Python 3.10+)
                __UpperCamelCase					:int        =		latent_model_input.device.type == '''mps'''
                if isinstance(__lowercase			,			__lowercase):
                    __UpperCamelCase					:int        =		torch.floataa if is_mps else torch.floataa
                else:
                    __UpperCamelCase					:Optional[int]        =		torch.intaa if is_mps else torch.intaa
                __UpperCamelCase					:Tuple        =		torch.tensor([timesteps]			,			dtype=__lowercase			,			device=latent_model_input.device)
            elif len(timesteps.shape) == 0:
                __UpperCamelCase					:str        =		timesteps[None].to(latent_model_input.device)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            __UpperCamelCase					:Optional[Any]        =		timesteps.expand(latent_model_input.shape[0])
            # predict noise model_output
            __UpperCamelCase					:Tuple        =		self.transformer(
                __lowercase			,			timestep=__lowercase			,			class_labels=__lowercase).sample

            # perform guidance
            if guidance_scale > 1:
                __UpperCamelCase					:Dict        =		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                __UpperCamelCase					:List[Any]        =		torch.split(__lowercase			,			len(__lowercase) // 2			,			dim=0)

                __UpperCamelCase					:Union[str, Any]        =		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                __UpperCamelCase					:Any        =		torch.cat([half_eps, half_eps]			,			dim=0)

                __UpperCamelCase					:List[str]        =		torch.cat([eps, rest]			,			dim=1)

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                __UpperCamelCase					:List[Any]        =		torch.split(__lowercase			,			__lowercase			,			dim=1)
            else:
                __UpperCamelCase					:str        =		noise_pred

            # compute previous image: x_t -> x_t-1
            __UpperCamelCase					:List[Any]        =		self.scheduler.step(__lowercase			,			__lowercase			,			__lowercase).prev_sample

        if guidance_scale > 1:
            __UpperCamelCase					:Any        =		latent_model_input.chunk(2			,			dim=0)
        else:
            __UpperCamelCase					:Optional[int]        =		latent_model_input

        __UpperCamelCase					:List[str]        =		1 / self.vae.config.scaling_factor * latents
        __UpperCamelCase					:Dict        =		self.vae.decode(__lowercase).sample

        __UpperCamelCase					:Any        =		(samples / 2 + 0.5).clamp(0			,			1)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        __UpperCamelCase					:Any        =		samples.cpu().permute(0			,			2			,			3			,			1).float().numpy()

        if output_type == "pil":
            __UpperCamelCase					:Optional[Any]        =		self.numpy_to_pil(__lowercase)

        if not return_dict:
            return (samples,)

        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)





