


'''simple docstring'''




import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[str]			      = IFInpaintingSuperResolutionPipeline
	_lowerCamelCase   :					Tuple			      = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
	_lowerCamelCase   :					Optional[Any]			      = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}							)
	_lowerCamelCase   :					int			      = PipelineTesterMixin.required_optional_params - {"""latents"""}



	def 				lowercase     (					self    ):
		return self._get_superresolution_dummy_components()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    str      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    Union[str, Any]      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )

		lowerCAmelCase					:    Any      							= floats_tensor((1, 3, 16, 16) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    str      							= floats_tensor((1, 3, 32, 32) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    Dict      							= floats_tensor((1, 3, 32, 32) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )

		lowerCAmelCase					:    Any      							= {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'image': image,
		    'original_image': original_image,
		    'mask_image': mask_image,
		    'generator': generator,
		    'num_inference_steps': 2,
		    'output_type': 'numpy',
		}

		return inputs



	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available() ,					reason='XFormers attention is only available with CUDA and `xformers` installed' ,					)
	def 				lowercase     (					self    ):
		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )



	def 				lowercase     (					self    ):
		self._test_save_load_optional_components()



	@unittest.skipIf(torch_device != 'cuda' ,					reason='float16 requires CUDA'    )
	def 				lowercase     (					self    ):
		# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
		super().test_save_load_floataa(expected_max_diff=1e-1    )



	def 				lowercase     (					self    ):
		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )



	def 				lowercase     (					self    ):
		self._test_save_load_local()



	def 				lowercase     (					self    ):
		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2 ,					)




'''simple docstring'''




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


_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Tuple		     =	{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Union[str, Any]			      = """open-llama"""



	def __init__(					self ,					snake_case__=10_0000 ,					snake_case__=4096 ,					snake_case__=1_1008 ,					snake_case__=32 ,					snake_case__=32 ,					snake_case__="silu" ,					snake_case__=2048 ,					snake_case__=0.0_2 ,					snake_case__=1e-6 ,					snake_case__=True ,					snake_case__=0 ,					snake_case__=1 ,					snake_case__=2 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=None ,					**snake_case__ ,					):
		lowerCAmelCase					:    Tuple      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= max_position_embeddings
		lowerCAmelCase					:    List[Any]      							= hidden_size
		lowerCAmelCase					:    List[Any]      							= intermediate_size
		lowerCAmelCase					:    Tuple      							= num_hidden_layers
		lowerCAmelCase					:    List[Any]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Union[str, Any]      							= initializer_range
		lowerCAmelCase					:    str      							= rms_norm_eps
		lowerCAmelCase					:    Optional[int]      							= use_cache
		lowerCAmelCase					:    Dict      							= kwargs.pop(
		    'use_memorry_efficient_attention' ,					snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= attention_dropout_prob
		lowerCAmelCase					:    Union[str, Any]      							= use_stable_embedding
		lowerCAmelCase					:    Tuple      							= shared_input_output_embedding
		lowerCAmelCase					:    Tuple      							= rope_scaling
		self._rope_scaling_validation()

		super().__init__(
		    pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					tie_word_embeddings=snake_case__ ,					**snake_case__ ,					)



	def 				lowercase     (					self    ):
		if self.rope_scaling is None:
			return

		if not isinstance(self.rope_scaling ,					snake_case__    ) or len(self.rope_scaling    ) != 2:
			raise ValueError(
			    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
			    f"got {self.rope_scaling}"    )
		lowerCAmelCase					:    List[Any]      							= self.rope_scaling.get('type' ,					snake_case__    )
		lowerCAmelCase					:    List[str]      							= self.rope_scaling.get('factor' ,					snake_case__    )
		if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
			raise ValueError(
			    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"    )
		if rope_scaling_factor is None or not isinstance(snake_case__ ,					snake_case__    ) or rope_scaling_factor <= 1.0:
			raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"    )
