




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		):
     _a							 =						StableDiffusionLDMaDPipeline
     _a							 =						TEXT_TO_IMAGE_PARAMS
     _a							 =						TEXT_TO_IMAGE_BATCH_PARAMS
     _a							 =						TEXT_TO_IMAGE_IMAGE_PARAMS

     def 					a__	(  self				)						->							List[str]:
           torch.manual_seed(0				)
           _A							:     Optional[Any]			=						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		,     )
           _A							:     Any			=						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				)
           _A							:     Union[str, Any]			=						AutoencoderKL(
               block_out_channels=[32, 64]		,     in_channels=6		,     out_channels=6		,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,     latent_channels=4		,     )
           torch.manual_seed(0				)
           _A							:     Tuple			=						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		,     )
           _A							:     Optional[int]			=						CLIPTextModel(_a				)
           _A							:     Any			=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

           _A							:     Optional[Any]			=						{
               """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				)						->							Tuple:
           if str(_a				).startswith("""mps"""				):
                 _A							:     Tuple			=						torch.manual_seed(_a				)
           else:
                 _A							:     List[str]			=						torch.Generator(device=_a				).manual_seed(_a				)
           _A							:     str			=						{
               """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				)						->							List[Any]:
           _A							:     Dict			=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

           _A							:     Optional[int]			=						self.get_dummy_components()
           _A							:     Union[str, Any]			=						StableDiffusionLDMaDPipeline(**_a				)
           _A							:     Optional[int]			=						ldmad_pipe.to(_a				)
           ldmad_pipe.set_progress_bar_config(disable=_a				)

           _A							:     Dict			=						self.get_dummy_inputs(_a				)
           _A							:     Optional[Any]			=						ldmad_pipe(**_a				)
           _A			,   _A							:     str			=						output.rgb, output.depth

           _A							:     Union[str, Any]			=						rgb[0, -3:, -3:, -1]
           _A							:     Tuple			=						depth[0, -3:, -1]

           assert rgb.shape == (1, 64, 64, 3)
           assert depth.shape == (1, 64, 64)

           _A							:     Any			=						np.array(
               [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]				)
           _A							:     Dict			=						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				)						->							List[str]:
           _A							:     int			=						self.get_dummy_components()
           _A							:     Optional[Any]			=						StableDiffusionLDMaDPipeline(**_a				)
           _A							:     Any			=						ldmad_pipe.to(_a				)
           ldmad_pipe.set_progress_bar_config(disable=_a				)

           _A							:     str			=						self.get_dummy_inputs(_a				)
           _A							:     List[str]			=						3 * [inputs["""prompt"""]]

           # forward
           _A							:     Tuple			=						ldmad_pipe(**_a				)
           _A			,   _A							:     Optional[int]			=						output.rgb, output.depth
           _A							:     Dict			=						rgb_slice_a[0, -3:, -3:, -1]
           _A							:     int			=						depth_slice_a[0, -3:, -1]

           _A							:     Optional[Any]			=						self.get_dummy_inputs(_a				)
           _A							:     Optional[Any]			=						3 * [inputs.pop("""prompt"""				)]

           _A							:     Optional[Any]			=						ldmad_pipe.tokenizer(
               _a		,     padding="""max_length"""		,     max_length=ldmad_pipe.tokenizer.model_max_length		,     truncation=_a		,     return_tensors="""pt"""		,     )
           _A							:     Optional[Any]			=						text_inputs["""input_ids"""].to(_a				)

           _A							:     Dict			=						ldmad_pipe.text_encoder(_a				)[0]

           _A							:     Dict			=						prompt_embeds

           # forward
           _A							:     Dict			=						ldmad_pipe(**_a				)
           _A			,   _A							:     Optional[Any]			=						output.rgb, output.depth
           _A							:     Any			=						rgb_slice_a[0, -3:, -3:, -1]
           _A							:     Any			=						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				)						->							Tuple:
           _A							:     Optional[int]			=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           _A							:     List[str]			=						self.get_dummy_components()
           _A							:     Optional[int]			=						PNDMScheduler(skip_prk_steps=_a				)
           _A							:     Optional[int]			=						StableDiffusionLDMaDPipeline(**_a				)
           _A							:     int			=						ldmad_pipe.to(_a				)
           ldmad_pipe.set_progress_bar_config(disable=_a				)

           _A							:     Any			=						self.get_dummy_inputs(_a				)
           _A							:     Any			=						"""french fries"""
           _A							:     List[Any]			=						ldmad_pipe(**_a		,     negative_prompt=_a				)

           _A			,   _A							:     Dict			=						output.rgb, output.depth
           _A							:     Dict			=						rgb[0, -3:, -3:, -1]
           _A							:     int			=						depth[0, -3:, -1]

           assert rgb.shape == (1, 64, 64, 3)
           assert depth.shape == (1, 64, 64)

           _A							:     int			=						np.array(
               [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]				)
           _A							:     str			=						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				)						->							List[str]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 					a__	(  self		,     _a		,     _a="cpu"		,     _a=torch.floataa		,     _a=0				)						->							Any:
           _A							:     Tuple			=						torch.Generator(device=_a				).manual_seed(_a				)
           _A							:     Union[str, Any]			=						np.random.RandomState(_a				).standard_normal((1, 4, 64, 64)				)
           _A							:     Dict			=						torch.from_numpy(_a				).to(device=_a		,     dtype=_a				)
           _A							:     int			=						{
               """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				)						->							str:
           _A							:     List[str]			=						StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""				)
           _A							:     List[str]			=						ldmad_pipe.to(_a				)
           ldmad_pipe.set_progress_bar_config(disable=_a				)

           _A							:     Optional[Any]			=						self.get_inputs(_a				)
           _A							:     Optional[Any]			=						ldmad_pipe(**_a				)
           _A			,   _A							:     Union[str, Any]			=						output.rgb, output.depth
           _A							:     Optional[Any]			=						rgb[0, -3:, -3:, -1].flatten()
           _A							:     Dict			=						rgb[0, -3:, -1].flatten()

           assert rgb.shape == (1, 512, 512, 3)
           assert depth.shape == (1, 512, 512)

           _A							:     List[str]			=						np.array(
               [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]				)
           _A							:     Any			=						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				)						->							Tuple:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 					a__	(  self		,     _a		,     _a="cpu"		,     _a=torch.floataa		,     _a=0				)						->							Any:
           _A							:     List[Any]			=						torch.Generator(device=_a				).manual_seed(_a				)
           _A							:     Optional[Any]			=						np.random.RandomState(_a				).standard_normal((1, 4, 64, 64)				)
           _A							:     int			=						torch.from_numpy(_a				).to(device=_a		,     dtype=_a				)
           _A							:     int			=						{
               """prompt""": """a photograph of an astronaut riding a horse""",
               """latents""": latents,
               """generator""": generator,
               """num_inference_steps""": 50,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs

     def 					a__	(  self				)						->							Dict:
           _A							:     List[str]			=						StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""				).to(_a				)
           ldmad_pipe.set_progress_bar_config(disable=_a				)

           _A							:     List[Any]			=						self.get_inputs(_a				)
           _A							:     Dict			=						ldmad_pipe(**_a				)
           _A			,   _A							:     Dict			=						output.rgb, output.depth

           _A							:     Any			=						0.495586
           _A							:     str			=						0.33795515
           _A							:     List[Any]			=						112.48518
           _A							:     Optional[int]			=						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				)						->							Optional[int]:
           _A							:     Dict			=						StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c"""				).to(_a				)
           ldmad_pipe.set_progress_bar_config(disable=_a				)

           _A							:     Any			=						self.get_inputs(_a				)
           _A							:     int			=						ldmad_pipe(**_a				)
           _A			,   _A							:     str			=						output.rgb, output.depth

           _A							:     Any			=						0.4194127
           _A							:     int			=						0.35375586
           _A							:     int			=						0.5638502
           _A							:     Tuple			=						0.34686103

           assert rgb.shape == (1, 512, 512, 3)
           assert depth.shape == (1, 512, 512, 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







"""simple docstring"""




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)

lowerCAmelCase							:			List[str]     							=							{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						"sew-d"





				def __init__(       self		, _a=32		, _a=768		, _a=12		, _a=12		, _a=3_072		, _a=2		, _a=512		, _a=256		, _a=True		, _a=True		, _a=("p2c", "c2p")		, _a="layer_norm"		, _a="gelu_python"		, _a=0.1		, _a=0.1		, _a=0.1		, _a=0.0		, _a=0.1		, _a=0.02		, _a=1e-7		, _a=1e-5		, _a="group"		, _a="gelu"		, _a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		, _a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		, _a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		, _a=False		, _a=128		, _a=16		, _a=True		, _a=0.05		, _a=10		, _a=2		, _a=0.0		, _a=10		, _a=0		, _a="mean"		, _a=False		, _a=False		, _a=256		, _a=0		, _a=1		, _a=2		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a		, pad_token_id=_a		, bos_token_id=_a		, eos_token_id=_a )
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					feat_extract_norm
							lowerCamelCase			      =					feat_extract_activation
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					conv_bias
							lowerCamelCase			      =					num_conv_pos_embeddings
							lowerCamelCase			      =					num_conv_pos_embedding_groups
							lowerCamelCase			      =					len(self.conv_dim )
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					squeeze_factor
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					position_buckets
							lowerCamelCase			      =					share_att_key
							lowerCamelCase			      =					relative_attention
							lowerCamelCase			      =					norm_rel_ebd
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					hidden_dropout
							lowerCamelCase			      =					attention_dropout
							lowerCamelCase			      =					activation_dropout
							lowerCamelCase			      =					feat_proj_dropout
							lowerCamelCase			      =					final_dropout
							lowerCamelCase			      =					layer_norm_eps
							lowerCamelCase			      =					feature_layer_norm_eps
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					vocab_size

							if (
							    (len(self.conv_stride ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    """Configuration for convolutional layers is incorrect."""
										    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
										    f'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
										    f'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCamelCase			      =					apply_spec_augment
							lowerCamelCase			      =					mask_time_prob
							lowerCamelCase			      =					mask_time_length
							lowerCamelCase			      =					mask_time_min_masks
							lowerCamelCase			      =					mask_feature_prob
							lowerCamelCase			      =					mask_feature_length
							lowerCamelCase			      =					mask_feature_min_masks

							# ctc loss
							lowerCamelCase			      =					ctc_loss_reduction
							lowerCamelCase			      =					ctc_zero_infinity

							# sequence classification
							lowerCamelCase			      =					use_weighted_layer_sum
							lowerCamelCase			      =					classifier_proj_size





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return functools.reduce(operator.mul		, self.conv_stride		, 1 )



