
"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()







class     lowercase		( unittest.TestCase		):
							lowercase_	:	List[Any]	 =StableDiffusionLDMaDPipeline
							lowercase_	:	Dict	 =TEXT_TO_IMAGE_PARAMS
							lowercase_	:	Dict	 =TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase_	:	List[str]	 =TEXT_TO_IMAGE_IMAGE_PARAMS




							def 			A__				(    self):
												torch.manual_seed(0)
												lowercase						     =						UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4)       ,layers_per_block=2       ,sample_size=3_2       ,in_channels=4       ,out_channels=4       ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,cross_attention_dim=3_2       ,)
												lowercase						     =						DDIMScheduler(
												    beta_start=0.00085       ,beta_end=0.012       ,beta_schedule='''scaled_linear'''       ,clip_sample=A__       ,set_alpha_to_one=A__       ,)
												torch.manual_seed(0)
												lowercase						     =						AutoencoderKL(
												    block_out_channels=[3_2, 6_4]       ,in_channels=6       ,out_channels=6       ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,latent_channels=4       ,)
												torch.manual_seed(0)
												lowercase						     =						CLIPTextConfig(
												    bos_token_id=0       ,eos_token_id=2       ,hidden_size=3_2       ,intermediate_size=3_7       ,layer_norm_eps=1E-05       ,num_attention_heads=4       ,num_hidden_layers=5       ,pad_token_id=1       ,vocab_size=1_0_0_0       ,)
												lowercase						     =						CLIPTextModel(A__)
												lowercase						     =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

												lowercase						     =						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}
												return components




							def 			A__				(    self       ,A__       ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase						     =						torch.manual_seed(A__)
												else:
																	lowercase						     =						torch.Generator(device=A__).manual_seed(A__)
												lowercase						     =						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 6.0,
												    '''output_type''': '''numpy''',
												}
												return inputs




							def 			A__				(    self):
												lowercase						     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

												lowercase						     =						self.get_dummy_components()
												lowercase						     =						StableDiffusionLDMaDPipeline(**A__)
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth

												lowercase						     =						rgb[0, -3:, -3:, -1]
												lowercase						     =						depth[0, -3:, -1]

												assert rgb.shape == (1, 6_4, 6_4, 3)
												assert depth.shape == (1, 6_4, 6_4)

												lowercase						     =						np.array(
												    [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262])
												lowercase						     =						np.array([103.46727, 85.812004, 87.849236])

												assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1E-2
												assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1E-2




							def 			A__				(    self):
												lowercase						     =						self.get_dummy_components()
												lowercase						     =						StableDiffusionLDMaDPipeline(**A__)
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						3 * [inputs['''prompt''']]

												# forward
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb_slice_a[0, -3:, -3:, -1]
												lowercase						     =						depth_slice_a[0, -3:, -1]

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						3 * [inputs.pop('''prompt''')]

												lowercase						     =						ldmad_pipe.tokenizer(
												    A__       ,padding='''max_length'''       ,max_length=ldmad_pipe.tokenizer.model_max_length       ,truncation=A__       ,return_tensors='''pt'''       ,)
												lowercase						     =						text_inputs['''input_ids'''].to(A__)

												lowercase						     =						ldmad_pipe.text_encoder(A__)[0]

												lowercase						     =						prompt_embeds

												# forward
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb_slice_a[0, -3:, -3:, -1]
												lowercase						     =						depth_slice_a[0, -3:, -1]

												assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()).max() < 1E-4
												assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()).max() < 1E-4





							def 			A__				(    self):
												lowercase						     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												lowercase						     =						self.get_dummy_components()
												lowercase						     =						PNDMScheduler(skip_prk_steps=A__)
												lowercase						     =						StableDiffusionLDMaDPipeline(**A__)
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						'''french fries'''
												lowercase						     =						ldmad_pipe(**A__       ,negative_prompt=A__)

												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb[0, -3:, -3:, -1]
												lowercase						     =						depth[0, -3:, -1]

												assert rgb.shape == (1, 6_4, 6_4, 3)
												assert depth.shape == (1, 6_4, 6_4)

												lowercase						     =						np.array(
												    [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217])
												lowercase						     =						np.array([107.84738, 84.62802, 89.962135])
												assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1E-2
												assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1E-2







@slow
@require_torch_gpu
class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 			A__				(    self       ,A__       ,A__="cpu"       ,A__=torch.floataa       ,A__=0):
												lowercase						     =						torch.Generator(device=A__).manual_seed(A__)
												lowercase						     =						np.random.RandomState(A__).standard_normal((1, 4, 6_4, 6_4))
												lowercase						     =						torch.from_numpy(A__).to(device=A__       ,dtype=A__)
												lowercase						     =						{
												    '''prompt''': '''a photograph of an astronaut riding a horse''',
												    '''latents''': latents,
												    '''generator''': generator,
												    '''num_inference_steps''': 3,
												    '''guidance_scale''': 7.5,
												    '''output_type''': '''numpy''',
												}
												return inputs





							def 			A__				(    self):
												lowercase						     =						StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''')
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb[0, -3:, -3:, -1].flatten()
												lowercase						     =						rgb[0, -3:, -1].flatten()

												assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
												assert depth.shape == (1, 5_1_2, 5_1_2)

												lowercase						     =						np.array(
												    [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706])
												lowercase						     =						np.array(
												    [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706])
												assert np.abs(rgb_slice - expected_slice_rgb).max() < 3E-3
												assert np.abs(depth_slice - expected_slice_depth).max() < 3E-3







@nightly
@require_torch_gpu
class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 			A__				(    self       ,A__       ,A__="cpu"       ,A__=torch.floataa       ,A__=0):
												lowercase						     =						torch.Generator(device=A__).manual_seed(A__)
												lowercase						     =						np.random.RandomState(A__).standard_normal((1, 4, 6_4, 6_4))
												lowercase						     =						torch.from_numpy(A__).to(device=A__       ,dtype=A__)
												lowercase						     =						{
												    '''prompt''': '''a photograph of an astronaut riding a horse''',
												    '''latents''': latents,
												    '''generator''': generator,
												    '''num_inference_steps''': 5_0,
												    '''guidance_scale''': 7.5,
												    '''output_type''': '''numpy''',
												}
												return inputs




							def 			A__				(    self):
												lowercase						     =						StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''').to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth

												lowercase						     =						0.495586
												lowercase						     =						0.33795515
												lowercase						     =						112.48518
												lowercase						     =						98.489746
												assert np.abs(expected_rgb_mean - rgb.mean()) < 1E-3
												assert np.abs(expected_rgb_std - rgb.std()) < 1E-3
												assert np.abs(expected_depth_mean - depth.mean()) < 1E-3
												assert np.abs(expected_depth_std - depth.std()) < 1E-3





							def 			A__				(    self):
												lowercase						     =						StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''').to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth

												lowercase						     =						0.4194127
												lowercase						     =						0.35375586
												lowercase						     =						0.5638502
												lowercase						     =						0.34686103

												assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
												assert depth.shape == (1, 5_1_2, 5_1_2, 1)
												assert np.abs(expected_rgb_mean - rgb.mean()) < 1E-3
												assert np.abs(expected_rgb_std - rgb.std()) < 1E-3
												assert np.abs(expected_depth_mean - depth.mean()) < 1E-3
												assert np.abs(expected_depth_std - depth.std()) < 1E-3






import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__		:Dict			  =   logging.get_logger(__name__)

lowercase__		:Optional[Any]			  =   "▁"

lowercase__		:str			  =   {"vocab_file": "prophetnet.tokenizer"}

lowercase__		:List[str]			  =   {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowercase__		:Optional[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowercase__		:List[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						collections.OrderedDict()
					with open(lowerCAmelCase__			,      '''r'''			,      encoding='''utf-8'''		) as reader:
										lowercase						     =						reader.readlines()
					for index, token in enumerate(lowerCAmelCase__		):
										lowercase						     =						token.rstrip('''\n'''		)
										lowercase						     =						index
					return vocab







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	Any	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Any	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




							def __init__(    self       ,A__       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[UNK]"       ,A__="[PAD]"       ,A__="[CLS]"       ,A__="[MASK]"       ,A__ = None       ,**A__       ,):
												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,sep_token=A__       ,unk_token=A__       ,pad_token=A__       ,cls_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))
												lowercase						     =						vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												lowercase						     =						{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

												for i in range(1_0):
																	lowercase						     =						f'[unused{i}]'
																	lowercase						     =						5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												lowercase						     =						1_2
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(A__)




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												# for backward compatibility
												if not hasattr(self       ,'''sp_model_kwargs'''):
																	lowercase						     =						{}

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

												if token_ids_a is None:
																	return ([0] * len(A__)) + [1]
												return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]

												if token_ids_a is None:
																	return len(token_ids_a + sep) * [0]
												return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]




							@property
							def 			A__				(    self):
												return len(self.sp_model) + self.fairseq_offset




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset)




							def 			A__				(    self       ,A__):
												lowercase						     =						''''''.join(A__).replace(A__       ,''' ''').strip()
												return out_string




							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)





							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




