






import copy
import os
from typing import Union

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


lowerCamelCase       :       Union[str, Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       Optional[int]        =				{
    '''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Any     =					'''altclip_text_model'''



					def __init__(     self :		List[Any]						,   __a :		List[str]=250002						,   __a :		int=1024						,   __a :		Tuple=24						,   __a :		Any=16						,   __a :		Tuple=4096						,   __a :		Dict="gelu"						,   __a :		List[str]=0.1						,   __a :		List[str]=0.1						,   __a :		List[Any]=514						,   __a :		Any=1						,   __a :		Any=0.02						,   __a :		Optional[Any]=0.02						,   __a :		Any=1E-05						,   __a :		List[str]=1						,   __a :		Optional[int]=0						,   __a :		List[str]=2						,   __a :		Any="absolute"						,   __a :		Optional[Any]=True						,   __a :		Optional[Any]=768						,   **__a :		Tuple						,   )	->     List[str]:


												"""simple docstring"""

												super().__init__(pad_token_id=__a						,   bos_token_id=__a						,   eos_token_id=__a						,   **__a							)

												__lowercase					:							List[Any]			=   vocab_size
												__lowercase					:							str			=   hidden_size
												__lowercase					:							Optional[Any]			=   num_hidden_layers
												__lowercase					:							str			=   num_attention_heads
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							List[Any]			=   intermediate_size
												__lowercase					:							str			=   hidden_dropout_prob
												__lowercase					:							Optional[Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   max_position_embeddings
												__lowercase					:							List[Any]			=   type_vocab_size
												__lowercase					:							List[str]			=   initializer_range
												__lowercase					:							List[Any]			=   initializer_factor
												__lowercase					:							Any			=   layer_norm_eps
												__lowercase					:							str			=   position_embedding_type
												__lowercase					:							Optional[Any]			=   use_cache
												__lowercase					:							Union[str, Any]			=   project_dim







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Any     =					'''altclip_vision_model'''



					def __init__(     self :		str						,   __a :		Any=768						,   __a :		str=3072						,   __a :		Dict=512						,   __a :		Tuple=12						,   __a :		Union[str, Any]=12						,   __a :		Union[str, Any]=3						,   __a :		Union[str, Any]=224						,   __a :		str=32						,   __a :		Tuple="quick_gelu"						,   __a :		Optional[Any]=1E-5						,   __a :		Dict=0.0						,   __a :		str=0.02						,   __a :		Tuple=1.0						,   **__a :		Dict						,   )	->     int:


												"""simple docstring"""

												super().__init__(**__a							)

												__lowercase					:							str			=   hidden_size
												__lowercase					:							Union[str, Any]			=   intermediate_size
												__lowercase					:							List[str]			=   projection_dim
												__lowercase					:							Optional[int]			=   num_hidden_layers
												__lowercase					:							Optional[int]			=   num_attention_heads
												__lowercase					:							Tuple			=   num_channels
												__lowercase					:							Any			=   patch_size
												__lowercase					:							Tuple			=   image_size
												__lowercase					:							int			=   initializer_range
												__lowercase					:							Union[str, Any]			=   initializer_factor
												__lowercase					:							List[str]			=   attention_dropout
												__lowercase					:							Any			=   layer_norm_eps
												__lowercase					:							Optional[Any]			=   hidden_act



					@classmethod
					def 					lowerCAmelCase (     cls :		Tuple						,   __a :		Union[str, os.PathLike]						,   **__a :		str							)	->     "PretrainedConfig":


												"""simple docstring"""

												cls._set_token_in_kwargs(__a							)

												__lowercase	,	__lowercase					:							Any			=   cls.get_config_dict(__a						,   **__a							)

												# get the vision config dict if we are loading from AltCLIPConfig
												if config_dict.get("""model_type"""							) == "altclip":
																			__lowercase					:							str			=   config_dict["""vision_config"""]

												if "model_type" in config_dict and hasattr(cls						,   """model_type"""							) and config_dict["model_type"] != cls.model_type:
																			logger.warning(
																			    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
																			    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

												return cls.from_dict(__a						,   **__a							)







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''altclip'''
					_A  :	Optional[Any]     =					True



					def __init__(     self :		List[str]						,   __a :		Dict=None						,   __a :		Optional[Any]=None						,   __a :		List[Any]=768						,   __a :		Union[str, Any]=2.6592						,   **__a :		Union[str, Any]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   kwargs.pop("""text_config_dict"""						,   __a							)
												__lowercase					:							Tuple			=   kwargs.pop("""vision_config_dict"""						,   __a							)

												super().__init__(**__a							)

												# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
												# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
												# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
												if text_config_dict is not None:
																			if text_config is None:
																										__lowercase					:							Dict			=   {}

																			# This is the complete result when using `text_config_dict`.
																			__lowercase					:							Tuple			=   AltCLIPTextConfig(**__a							).to_dict()

																			# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																			for key, value in _text_config_dict.items():
																										if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																																	# If specified in `text_config_dict`
																																	if key in text_config_dict:
																																								__lowercase					:							Union[str, Any]			=   (
																																								    F"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
																																								    F"The value `text_config_dict[\"{key}\"]` will be used instead."
																																								)
																																	# If inferred from default argument values (just to be super careful)
																																	else:
																																								__lowercase					:							int			=   (
																																								    F"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
																																								    F"value `text_config[\"{key}\"]` will be overriden."
																																								)
																																	logger.warning(__a							)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																			text_config.update(_text_config_dict							)

												if vision_config_dict is not None:
																			if vision_config is None:
																										__lowercase					:							Optional[int]			=   {}

																			# This is the complete result when using `vision_config_dict`.
																			__lowercase					:							Union[str, Any]			=   AltCLIPVisionConfig(**__a							).to_dict()
																			# convert keys to string instead of integer
																			if "id2label" in _vision_config_dict:
																										__lowercase					:							List[Any]			=   {
																										    str(__a							): value for key, value in _vision_config_dict["""id2label"""].items()
																										}

																			# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																			for key, value in _vision_config_dict.items():
																										if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																																	# If specified in `vision_config_dict`
																																	if key in vision_config_dict:
																																								__lowercase					:							Union[str, Any]			=   (
																																								    F"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
																																								    F"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
																																								)
																																	# If inferred from default argument values (just to be super careful)
																																	else:
																																								__lowercase					:							int			=   (
																																								    F"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
																																								    F"The value `vision_config[\"{key}\"]` will be overriden."
																																								)
																																	logger.warning(__a							)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																			vision_config.update(_vision_config_dict							)

												if text_config is None:
																			__lowercase					:							List[str]			=   {}
																			logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."""							)

												if vision_config is None:
																			__lowercase					:							int			=   {}
																			logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values."""							)

												__lowercase					:							Any			=   AltCLIPTextConfig(**__a							)
												__lowercase					:							int			=   AltCLIPVisionConfig(**__a							)

												__lowercase					:							List[Any]			=   projection_dim
												__lowercase					:							Dict			=   logit_scale_init_value
												__lowercase					:							Optional[Any]			=   1.0



					@classmethod
					def 					lowerCAmelCase (     cls :		List[Any]						,   __a :		AltCLIPTextConfig						,   __a :		AltCLIPVisionConfig						,   **__a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												return cls(text_config=text_config.to_dict()						,   vision_config=vision_config.to_dict()						,   **__a							)



					def 					lowerCAmelCase (     self :		Optional[Any]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   copy.deepcopy(self.__dict__							)
												__lowercase					:							List[str]			=   self.text_config.to_dict()
												__lowercase					:							Tuple			=   self.vision_config.to_dict()
												__lowercase					:							Dict			=   self.__class__.model_type
												return output







import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort

class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



					def 					lowerCAmelCase (     self :		int						,   __a :		List[Any]=0							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Any			=   floats_tensor((1, 3, 128, 128)						,   rng=random.Random(__a							)							)
												__lowercase					:							Any			=   np.random.RandomState(__a							)
												__lowercase					:							Union[str, Any]			=   {
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 3,
												    """strength""": 0.75,
												    """guidance_scale""": 7.5,
												    """output_type""": """numpy""",
												}
												return inputs



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Any:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Optional[Any]			=   self.get_dummy_inputs()
												__lowercase					:							str			=   pipe(**__a							).images
												__lowercase					:							Union[str, Any]			=   image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							List[Any]			=   np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]							)
												assert np.abs(image_slice - expected_slice							).max() < 1E-1



					def 					lowerCAmelCase (     self :		Any							)	->     Any:


												"""simple docstring"""

												__lowercase					:							str			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Union[str, Any]			=   PNDMScheduler.from_config(pipe.scheduler.config						,   skip_prk_steps=__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Dict			=   self.get_dummy_inputs()
												__lowercase					:							Optional[int]			=   pipe(**__a							).images
												__lowercase					:							Union[str, Any]			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Any			=   np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]							)

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



					def 					lowerCAmelCase (     self :		int							)	->     str:


												"""simple docstring"""

												__lowercase					:							int			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Union[str, Any]			=   LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												# warmup pass to apply optimizations
												__lowercase					:							Optional[int]			=   pipe(**self.get_dummy_inputs()							)

												__lowercase					:							Dict			=   self.get_dummy_inputs()
												__lowercase					:							str			=   pipe(**__a							).images
												__lowercase					:							Optional[Any]			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Any			=   np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]							)

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



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							List[Any]			=   EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Any			=   self.get_dummy_inputs()
												__lowercase					:							int			=   pipe(**__a							).images
												__lowercase					:							Dict			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Optional[int]			=   np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]							)

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



					def 					lowerCAmelCase (     self :		List[Any]							)	->     str:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Dict			=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Tuple			=   self.get_dummy_inputs()
												__lowercase					:							Union[str, Any]			=   pipe(**__a							).images
												__lowercase					:							int			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Union[str, Any]			=   np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]							)

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



					def 					lowerCAmelCase (     self :		Dict							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Tuple			=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							str			=   self.get_dummy_inputs()
												__lowercase					:							Dict			=   pipe(**__a							).images
												__lowercase					:							int			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Dict			=   np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]							)

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







@nightly
@require_onnxruntime
@require_torch_gpu
class 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					@property
					def 					lowerCAmelCase (     self :		Tuple							)	->     Optional[int]:


												"""simple docstring"""

												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)



					@property
					def 					lowerCAmelCase (     self :		str							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   ort.SessionOptions()
												__lowercase					:							Dict			=   False
												return options



					def 					lowerCAmelCase (     self :		int							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Union[str, Any]			=   init_image.resize((768, 512)							)
												# using the PNDM scheduler by default
												__lowercase					:							Optional[int]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    """CompVis/stable-diffusion-v1-4"""						,   revision="""onnx"""						,   safety_checker=__a						,   feature_extractor=__a						,   provider=self.gpu_provider						,   sess_options=self.gpu_options						,   )
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							int			=   """A fantasy landscape, trending on artstation"""

												__lowercase					:							int			=   np.random.RandomState(0							)
												__lowercase					:							List[str]			=   pipe(
												    prompt=__a						,   image=__a						,   strength=0.75						,   guidance_scale=7.5						,   num_inference_steps=10						,   generator=__a						,   output_type="""np"""						,   )
												__lowercase					:							Any			=   output.images
												__lowercase					:							Optional[int]			=   images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												__lowercase					:							Optional[int]			=   np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]							)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



					def 					lowerCAmelCase (     self :		List[Any]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Any			=   init_image.resize((768, 512)							)
												__lowercase					:							int			=   LMSDiscreteScheduler.from_pretrained(
												    """runwayml/stable-diffusion-v1-5"""						,   subfolder="""scheduler"""						,   revision="""onnx"""							)
												__lowercase					:							str			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    """runwayml/stable-diffusion-v1-5"""						,   revision="""onnx"""						,   scheduler=__a						,   safety_checker=__a						,   feature_extractor=__a						,   provider=self.gpu_provider						,   sess_options=self.gpu_options						,   )
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Union[str, Any]			=   """A fantasy landscape, trending on artstation"""

												__lowercase					:							Any			=   np.random.RandomState(0							)
												__lowercase					:							Optional[Any]			=   pipe(
												    prompt=__a						,   image=__a						,   strength=0.75						,   guidance_scale=7.5						,   num_inference_steps=20						,   generator=__a						,   output_type="""np"""						,   )
												__lowercase					:							str			=   output.images
												__lowercase					:							Dict			=   images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												__lowercase					:							Union[str, Any]			=   np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]							)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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