






"""simple docstring"""
import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	@property
	def  lowerCamelCase(self				):
		torch.manual_seed(0				)
		A_		:    Any						     = UNetaDModel(
		    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  sample_size=32  ,  in_channels=3  ,  out_channels=3  ,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")  ,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")  ,  )
		return model





	@property
	def  lowerCamelCase(self				):
		torch.manual_seed(0				)
		A_		:    Optional[int]						     = VQModel(
		    block_out_channels=[32, 64]  ,  in_channels=3  ,  out_channels=3  ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,  latent_channels=3  ,  )
		return model





	@property
	def  lowerCamelCase(self				):
		torch.manual_seed(0				)
		A_		:    Dict						     = CLIPTextConfig(
		    bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=32  ,  intermediate_size=37  ,  layer_norm_eps=1e-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=1000  ,  )
		return CLIPTextModel(lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.dummy_uncond_unet
		A_		:    Optional[Any]						     = DDIMScheduler()
		A_		:    List[str]						     = self.dummy_vq_model

		A_		:    Dict						     = LDMPipeline(unet=lowerCAmelCase_  ,  vqvae=lowerCAmelCase_  ,  scheduler=lowerCAmelCase_				)
		ldm.to(lowerCAmelCase_				)
		ldm.set_progress_bar_config(disable=lowerCAmelCase_				)

		A_		:    Optional[int]						     = torch.manual_seed(0				)
		A_		:    Any						     = ldm(generator=lowerCAmelCase_  ,  num_inference_steps=2  ,  output_type="""numpy"""				).images

		A_		:    List[str]						     = torch.manual_seed(0				)
		A_		:    Optional[int]						     = ldm(generator=lowerCAmelCase_  ,  num_inference_steps=2  ,  output_type="""numpy"""  ,  return_dict=lowerCAmelCase_				)[0]

		A_		:    Union[str, Any]						     = image[0, -3:, -3:, -1]
		A_		:    Any						     = image_from_tuple[0, -3:, -3:, -1]

		assert image.shape == (1, 64, 64, 3)
		A_		:    Dict						     = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]				)
		A_		:    Tuple						     = 1e-2 if torch_device != """mps""" else 3e-2

		assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < tolerance






@slow
@require_torch
class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    Dict						     = LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""				)
		ldm.to(lowerCAmelCase_				)
		ldm.set_progress_bar_config(disable=lowerCAmelCase_				)

		A_		:    Dict						     = torch.manual_seed(0				)
		A_		:    List[str]						     = ldm(generator=lowerCAmelCase_  ,  num_inference_steps=5  ,  output_type="""numpy"""				).images

		A_		:    Union[str, Any]						     = image[0, -3:, -3:, -1]

		assert image.shape == (1, 256, 256, 3)
		A_		:    Tuple						     = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]				)
		A_		:    str						     = 1e-2 if torch_device != """mps""" else 3e-2

		assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance









"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@register_to_config
	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = False  ,  ):
		super().__init__()

		A_		:    Tuple						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    Any						     = False

		A_		:    Tuple						     = nn.Dropout(p=lowerCAmelCase_				)

		A_		:    List[str]						     = TaConfig(
		    vocab_size=lowerCAmelCase_  ,  d_model=lowerCAmelCase_  ,  num_heads=lowerCAmelCase_  ,  d_kv=lowerCAmelCase_  ,  d_ff=lowerCAmelCase_  ,  dropout_rate=lowerCAmelCase_  ,  feed_forward_proj=lowerCAmelCase_  ,  is_decoder=lowerCAmelCase_  ,  is_encoder_decoder=lowerCAmelCase_  ,  )

		A_		:    Optional[Any]						     = nn.ModuleList()
		for lyr_num in range(lowerCAmelCase_				):
			A_		:    Tuple						     = TaBlock(lowerCAmelCase_				)
			self.encoders.append(lowerCAmelCase_				)

		A_		:    Any						     = TaLayerNorm(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = nn.Dropout(p=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.token_embedder(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = encoder_input_tokens.shape[1]
		A_		:    Any						     = torch.arange(lowerCAmelCase_  ,  device=encoder_input_tokens.device				)
		x += self.position_encoding(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.dropout_pre(lowerCAmelCase_				)

		# inverted the attention mask
		A_		:    int						     = encoder_input_tokens.size()
		A_		:    Optional[Any]						     = self.get_extended_attention_mask(lowerCAmelCase_  ,  lowerCAmelCase_				)

		for lyr in self.encoders:
			A_		:    int						     = lyr(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]
		A_		:    List[str]						     = self.layer_norm(lowerCAmelCase_				)

		return self.dropout_post(lowerCAmelCase_				), encoder_inputs_mask

