



"""simple docstring"""






import gc
import unittest

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

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

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	LDMTextToImagePipeline
	SCREAMING_SNAKE_CASE          =	TEXT_TO_IMAGE_PARAMS - {
	    'negative_prompt',
	    'negative_prompt_embeds',
	    'cross_attention_kwargs',
	    'prompt_embeds',
	}
	SCREAMING_SNAKE_CASE          =	PipelineTesterMixin.required_optional_params - {
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	SCREAMING_SNAKE_CASE          =	TEXT_TO_IMAGE_BATCH_PARAMS
	SCREAMING_SNAKE_CASE          =	False

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       str:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase      :  Tuple												=       UNetaDConditionModel(
				    block_out_channels=(32, 64)       ,			layers_per_block=2       ,			sample_size=32       ,			in_channels=4       ,			out_channels=4       ,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,			cross_attention_dim=32       ,			)
				__lowerCAmelCase      :  Optional[int]												=       DDIMScheduler(
				    beta_start=0.0_0085       ,			beta_end=0.012       ,			beta_schedule="scaled_linear"       ,			clip_sample=_SCREAMING_SNAKE_CASE       ,			set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,			)
				torch.manual_seed(0)
				__lowerCAmelCase      :  Optional[int]												=       AutoencoderKL(
				    block_out_channels=(32, 64)       ,			in_channels=3       ,			out_channels=3       ,			down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")       ,			up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")       ,			latent_channels=4       ,			)
				torch.manual_seed(0)
				__lowerCAmelCase      :  List[str]												=       CLIPTextConfig(
				    bos_token_id=0       ,			eos_token_id=2       ,			hidden_size=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			pad_token_id=1       ,			vocab_size=1000       ,			)
				__lowerCAmelCase      :  Optional[int]												=       CLIPTextModel(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

				__lowerCAmelCase      :  Optional[Any]												=       {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vqvae": vae,
				    "bert": text_encoder,
				    "tokenizer": tokenizer,
				}
				return components

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0)				->       Any:

				"""simple docstring"""

				if str(_SCREAMING_SNAKE_CASE).startswith("mps"):
							__lowerCAmelCase      :  Union[str, Any]												=       torch.manual_seed(_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase      :  Optional[Any]												=       torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Any												=       {
				    "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 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       "cpu"  # ensure determinism for the device-dependent torch.Generator

				__lowerCAmelCase      :  Optional[int]												=       self.get_dummy_components()
				__lowerCAmelCase      :  List[Any]												=       LDMTextToImagePipeline(**_SCREAMING_SNAKE_CASE)
				pipe.to(_SCREAMING_SNAKE_CASE)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Any												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase      :  List[str]												=       image[0, -3:, -3:, -1]

				assert image.shape == (1, 16, 16, 3)
				__lowerCAmelCase      :  List[str]												=       np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

				assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3






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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       List[Any]:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Tuple=torch.floataa       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0)				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       torch.manual_seed(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       np.random.RandomState(_SCREAMING_SNAKE_CASE).standard_normal((1, 4, 32, 32))
				__lowerCAmelCase      :  Union[str, Any]												=       torch.from_numpy(_SCREAMING_SNAKE_CASE).to(device=_SCREAMING_SNAKE_CASE       ,			dtype=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       {
				    "prompt": "A painting of a squirrel eating a burger",
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs







	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(_SCREAMING_SNAKE_CASE)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Tuple												=       self.get_inputs(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase      :  Tuple												=       image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 256, 256, 3)
				__lowerCAmelCase      :  Any												=       np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878])
				__lowerCAmelCase      :  Union[str, Any]												=       np.abs(expected_slice - image_slice).max()
				assert max_diff < 1e-3






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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Optional[int]:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		List[str]       ,			_SCREAMING_SNAKE_CASE:		List[Any]=torch.floataa       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       torch.manual_seed(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       np.random.RandomState(_SCREAMING_SNAKE_CASE).standard_normal((1, 4, 32, 32))
				__lowerCAmelCase      :  List[Any]												=       torch.from_numpy(_SCREAMING_SNAKE_CASE).to(device=_SCREAMING_SNAKE_CASE       ,			dtype=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  int												=       {
				    "prompt": "A painting of a squirrel eating a burger",
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 50,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(_SCREAMING_SNAKE_CASE)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  int												=       self.get_inputs(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       pipe(**_SCREAMING_SNAKE_CASE).images[0]

				__lowerCAmelCase      :  int												=       load_numpy(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
				__lowerCAmelCase      :  Optional[int]												=       np.abs(expected_image - image).max()
				assert max_diff < 1e-3




"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
	from ...feature_extraction_utils import FeatureExtractionMixin
	from ...tokenization_utils_base import PreTrainedTokenizerBase
	from ...utils import TensorType

__snake_case							:   Optional[Any]      =    logging.get_logger(__name__)

__snake_case							:   Optional[Any]      =    {
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
__snake_case							:   Any      =    [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
    1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
    4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
    11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
    17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
    34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
__snake_case							:   str      =    [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1_350, 1_853, 1_982, 2_460, 2_627,
    3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
    7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
    14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
    22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
    42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'whisper'
	SCREAMING_SNAKE_CASE          =	['past_key_values']
	SCREAMING_SNAKE_CASE          =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

	def __init__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Any=5_1865       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=80       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=6       ,			_SCREAMING_SNAKE_CASE:		Any=4       ,			_SCREAMING_SNAKE_CASE:		Dict=6       ,			_SCREAMING_SNAKE_CASE:		Dict=4       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=1536       ,			_SCREAMING_SNAKE_CASE:		List[str]=1536       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.0       ,			_SCREAMING_SNAKE_CASE:		str=0.0       ,			_SCREAMING_SNAKE_CASE:		List[str]=5_0257       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		List[str]=True       ,			_SCREAMING_SNAKE_CASE:		Optional[int]="gelu"       ,			_SCREAMING_SNAKE_CASE:		Tuple=256       ,			_SCREAMING_SNAKE_CASE:		str=0.0       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.0       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.0       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1500       ,			_SCREAMING_SNAKE_CASE:		str=448       ,			_SCREAMING_SNAKE_CASE:		Any=5_0256       ,			_SCREAMING_SNAKE_CASE:		Any=5_0256       ,			_SCREAMING_SNAKE_CASE:		List[str]=5_0256       ,			_SCREAMING_SNAKE_CASE:		Dict=None       ,			_SCREAMING_SNAKE_CASE:		List[Any]=[220, 5_0256]       ,			_SCREAMING_SNAKE_CASE:		Dict=False       ,			_SCREAMING_SNAKE_CASE:		str=256       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.05       ,			_SCREAMING_SNAKE_CASE:		List[str]=10       ,			_SCREAMING_SNAKE_CASE:		str=2       ,			_SCREAMING_SNAKE_CASE:		Any=0.0       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=10       ,			_SCREAMING_SNAKE_CASE:		int=0       ,			_SCREAMING_SNAKE_CASE:		Any=7       ,			**_SCREAMING_SNAKE_CASE:		List[str]       ,			)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       vocab_size
				__lowerCAmelCase      :  Optional[Any]												=       num_mel_bins
				__lowerCAmelCase      :  int												=       d_model
				__lowerCAmelCase      :  List[Any]												=       encoder_layers
				__lowerCAmelCase      :  List[Any]												=       encoder_attention_heads
				__lowerCAmelCase      :  List[str]												=       decoder_layers
				__lowerCAmelCase      :  Tuple												=       decoder_attention_heads
				__lowerCAmelCase      :  Any												=       decoder_ffn_dim
				__lowerCAmelCase      :  Tuple												=       encoder_ffn_dim
				__lowerCAmelCase      :  List[str]												=       dropout
				__lowerCAmelCase      :  Union[str, Any]												=       attention_dropout
				__lowerCAmelCase      :  Union[str, Any]												=       activation_dropout
				__lowerCAmelCase      :  Dict												=       activation_function
				__lowerCAmelCase      :  Tuple												=       init_std
				__lowerCAmelCase      :  str												=       encoder_layerdrop
				__lowerCAmelCase      :  int												=       decoder_layerdrop
				__lowerCAmelCase      :  Optional[int]												=       use_cache
				__lowerCAmelCase      :  Union[str, Any]												=       encoder_layers
				__lowerCAmelCase      :  List[str]												=       scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase      :  int												=       max_source_positions
				__lowerCAmelCase      :  Any												=       max_target_positions

				# Audio Classification-specific parameters. Feel free to ignore for other classes.
				__lowerCAmelCase      :  Dict												=       classifier_proj_size
				__lowerCAmelCase      :  Dict												=       use_weighted_layer_sum

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				__lowerCAmelCase      :  int												=       apply_spec_augment
				__lowerCAmelCase      :  Union[str, Any]												=       mask_time_prob
				__lowerCAmelCase      :  str												=       mask_time_length
				__lowerCAmelCase      :  int												=       mask_time_min_masks
				__lowerCAmelCase      :  List[Any]												=       mask_feature_prob
				__lowerCAmelCase      :  Tuple												=       mask_feature_length
				__lowerCAmelCase      :  Any												=       mask_feature_min_masks

				__lowerCAmelCase      :  Union[str, Any]												=       median_filter_width

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE       ,			bos_token_id=_SCREAMING_SNAKE_CASE       ,			eos_token_id=_SCREAMING_SNAKE_CASE       ,			is_encoder_decoder=_SCREAMING_SNAKE_CASE       ,			decoder_start_token_id=_SCREAMING_SNAKE_CASE       ,			suppress_tokens=_SCREAMING_SNAKE_CASE       ,			begin_suppress_tokens=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)






class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       OrderedDict(
				    [
				        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
				    ])
				if self.use_past:
							__lowerCAmelCase      :  Tuple												=       {0: "batch"}
				else:
							__lowerCAmelCase      :  Union[str, Any]												=       {0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE       ,			direction="inputs")

				return common_inputs

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,			_SCREAMING_SNAKE_CASE:		int = -1       ,			_SCREAMING_SNAKE_CASE:		int = -1       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		Optional["TensorType"] = None       ,			_SCREAMING_SNAKE_CASE:		int = 2_2050       ,			_SCREAMING_SNAKE_CASE:		float = 5.0       ,			_SCREAMING_SNAKE_CASE:		int = 220       ,			)				->       Mapping[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       OrderedDict()
				__lowerCAmelCase      :  Optional[Any]												=       OnnxConfig.generate_dummy_inputs(
				    self       ,			preprocessor=preprocessor.feature_extractor       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			framework=_SCREAMING_SNAKE_CASE       ,			sampling_rate=_SCREAMING_SNAKE_CASE       ,			time_duration=_SCREAMING_SNAKE_CASE       ,			frequency=_SCREAMING_SNAKE_CASE       ,			)
				__lowerCAmelCase      :  List[str]												=       encoder_inputs["input_features"].shape[2]
				__lowerCAmelCase      :  List[str]												=       encoder_sequence_length // 2 if self.use_past else seq_length

				__lowerCAmelCase      :  List[Any]												=       super().generate_dummy_inputs(
				    preprocessor.tokenizer       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[str]												=       encoder_inputs.pop("input_features")
				__lowerCAmelCase      :  List[Any]												=       decoder_inputs.pop("decoder_input_ids")

				if "past_key_values" in decoder_inputs:
							__lowerCAmelCase      :  int												=       decoder_inputs.pop("past_key_values")

				return dummy_inputs







	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       float:

				"""simple docstring"""

				return 1e-3