






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   List[str]      =  StableDiffusionSAGPipeline
				_lowercase				:   Dict      =  TEXT_TO_IMAGE_PARAMS
				_lowercase				:   str      =  TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase				:   Optional[int]      =  TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase				:   Tuple      =  TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase				:   Tuple      =  False



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											torch.manual_seed(0)
											snake_case__	  =       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			,		)
											snake_case__	  =       DDIMScheduler(
											    beta_start=0.0_00_85			,		beta_end=0.0_12			,		beta_schedule="""scaled_linear"""			,		clip_sample=UpperCamelCase__			,		set_alpha_to_one=UpperCamelCase__			,		)
											torch.manual_seed(0)
											snake_case__	  =       AutoencoderKL(
											    block_out_channels=[3_2, 6_4]			,		in_channels=3			,		out_channels=3			,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,		latent_channels=4			,		)
											torch.manual_seed(0)
											snake_case__	  =       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			,		)
											snake_case__	  =       CLIPTextModel(UpperCamelCase__)
											snake_case__	  =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

											snake_case__	  =       {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											return components



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Union[str, Any]=0):






											'''simple docstring'''







											if str(UpperCamelCase__).startswith("""mps"""):
																		snake_case__	  =       torch.manual_seed(UpperCamelCase__)
											else:
																		snake_case__	  =       torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
											snake_case__	  =       {
											    """prompt""": """.""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 1.0,
											    """sag_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											super().test_inference_batch_single_identical(expected_max_diff=3E-3)


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



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""")
											snake_case__	  =       sag_pipe.to(UpperCamelCase__)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       """."""
											snake_case__	  =       torch.manual_seed(0)
											snake_case__	  =       sag_pipe(
											    [prompt]			,		generator=UpperCamelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=2_0			,		output_type="""np""")

											snake_case__	  =       output.images

											snake_case__	  =       image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											snake_case__	  =       np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-2



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
											snake_case__	  =       sag_pipe.to(UpperCamelCase__)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       """."""
											snake_case__	  =       torch.manual_seed(0)
											snake_case__	  =       sag_pipe(
											    [prompt]			,		generator=UpperCamelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=2_0			,		output_type="""np""")

											snake_case__	  =       output.images

											snake_case__	  =       image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											snake_case__	  =       np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-2



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
											snake_case__	  =       sag_pipe.to(UpperCamelCase__)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       """."""
											snake_case__	  =       torch.manual_seed(0)
											snake_case__	  =       sag_pipe(
											    [prompt]			,		width=7_6_8			,		height=5_1_2			,		generator=UpperCamelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=2_0			,		output_type="""np"""			,		)

											snake_case__	  =       output.images

											assert image.shape == (1, 5_1_2, 7_6_8, 3)








from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a__       =     logging.get_logger(__name__)

def _UpperCAmelCase   (	a						:	Union[tf.Tensor, np.ndarray] ):
							if isinstance(a  ,					np.ndarray ):
														return list(tensor.shape )

							snake_case__	  =       tf.shape(a )

							if tensor.shape == tf.TensorShape(a ):
														return dynamic

							snake_case__	  =       tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(a )]

def _UpperCAmelCase   (	a						:	tf.Tensor  ,					a						:	Optional[int] = None  ,					a						:	Optional[str] = None ):
							return tf.nn.softmax(logits=logits + 1e-9  ,					axis=a  ,					name=a )

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Union[str, Any]  ,					a						:	Dict  ,					a						:	int=1e-5  ,					a						:	Dict=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a  ,					a ):
														raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )

							# Get mean and variance on the axis to be normalized
							snake_case__  , snake_case__	  =       tf.nn.moments(a  ,					axes=[axis]  ,					keepdims=a )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														snake_case__	  =       [1] * inputs.shape.rank
														snake_case__	  =       shape_list(a )[axis]
														snake_case__	  =       tf.reshape(a  ,					a )
														snake_case__	  =       tf.reshape(a  ,					a )

							# Compute layer normalization using the batch_normalization
							# function.
							snake_case__	  =       tf.nn.batch_normalization(
							    a  ,					a  ,					a  ,					offset=a  ,					scale=a  ,					variance_epsilon=a  ,					)
							return outputs

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict=0  ,					a						:	List[Any]=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							snake_case__	  =       tf.shape(a )
							snake_case__	  =       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							snake_case__	  =       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  ,					axis=0 )
							return tf.reshape(a  ,					a )

def _UpperCAmelCase   (	a						:	tf.Tensor ):
							if not isinstance(a  ,					tf.Tensor ):
														snake_case__	  =       tf.convert_to_tensor(a )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														snake_case__	  =       encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														snake_case__	  =       encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							snake_case__	  =       (
							    tf.cast(1  ,					encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask

def _UpperCAmelCase   (	a						:	tf.Tensor  ,					a						:	int  ,					a						:	str = "input_ids" ):
							tf.debugging.assert_less(
							    a  ,					tf.cast(a  ,					dtype=tensor.dtype )  ,					message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(a )}) must be smaller than the embedding '''
							        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
							    )  ,					)

def _UpperCAmelCase   (	a						:	str  ,					a						:	Tuple  ,					a						:	Optional[int] ):
							snake_case__	  =       6_4512
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							snake_case__	  =       [x for x in data if len(a ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    """The following attributes cannot be saved to HDF5 file because """
														    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
														    F'''bytes: {bad_attributes}''' )

							snake_case__	  =       np.asarray(a )

							snake_case__	  =       1
							snake_case__	  =       np.array_split(a  ,					a )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														snake_case__	  =       np.array_split(a  ,					a )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(a ):
																					snake_case__	  =       chunk_data
							else:
														snake_case__	  =       data

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Optional[int] ):
							if name in group.attrs:
														snake_case__	  =       [n.decode("""utf8""" ) if hasattr(a  ,					"""decode""" ) else n for n in group.attrs[name]]
							else:
														snake_case__	  =       []
														snake_case__	  =       0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("""utf8""" ) if hasattr(a  ,					"""decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
																					chunk_id += 1
							return data



def _UpperCAmelCase   (	a						:	Optional[int] ):

							def _expand_single_ad_tensor(a						:	Any ):
														if isinstance(a  ,					tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(a  ,					axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor  ,					a )
