



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


A__  :  Tuple       = logging.get_logger(__name__)

A__  :  Tuple       = {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			'''opt'''
							_a      				=			['''past_key_values''']




							def __init__(	self					:	Optional[int]    ,    A_					:	Any=5_0_2_7_2    ,    A_					:	Tuple=7_6_8    ,    A_					:	Optional[Any]=1_2    ,    A_					:	str=3_0_7_2    ,    A_					:	Dict=2_0_4_8    ,    A_					:	List[Any]=True    ,    A_					:	Optional[int]=False    ,    A_					:	Optional[int]=None    ,    A_					:	Dict=0.1    ,    A_					:	str=0.0    ,    A_					:	Union[str, Any]=1_2    ,    A_					:	List[str]="relu"    ,    A_					:	Dict=0.0    ,    A_					:	Union[str, Any]=0.02    ,    A_					:	Optional[Any]=True    ,    A_					:	Optional[Any]=1    ,    A_					:	List[Any]=2    ,    A_					:	Dict=2    ,    A_					:	Optional[int]=True    ,    A_					:	Optional[int]=True    ,    **A_					:	List[Any]    ,    ):
									super().__init__(
									    pad_token_id=A_    ,    bos_token_id=A_    ,    eos_token_id=A_    ,    **A_    ,    )
									lowerCAmelCase_      :					List[Any]     =			vocab_size
									lowerCAmelCase_      :					Union[str, Any]     =			max_position_embeddings
									lowerCAmelCase_      :					Optional[int]     =			num_attention_heads
									lowerCAmelCase_      :					int     =			word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
									lowerCAmelCase_      :					List[Any]     =			ffn_dim
									lowerCAmelCase_      :					Any     =			hidden_size
									lowerCAmelCase_      :					Union[str, Any]     =			num_hidden_layers
									lowerCAmelCase_      :					str     =			dropout
									lowerCAmelCase_      :					List[str]     =			attention_dropout
									lowerCAmelCase_      :					Any     =			activation_function
									lowerCAmelCase_      :					Union[str, Any]     =			init_std
									lowerCAmelCase_      :					int     =			layerdrop
									lowerCAmelCase_      :					Optional[int]     =			use_cache
									lowerCAmelCase_      :					int     =			do_layer_norm_before
									# We keep these variables at `True` for backward compatibility.
									lowerCAmelCase_      :					Optional[Any]     =			enable_bias
									lowerCAmelCase_      :					Optional[int]     =			layer_norm_elementwise_affine

									# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
									# with checkpoints that have been fine-tuned before transformers v4.20.1
									# see https://github.com/facebookresearch/metaseq/pull/164
									lowerCAmelCase_      :					Optional[Any]     =			_remove_final_layer_norm










import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class     __snake_case						(					UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a      				=			IFImgaImgPipeline
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_a      				=			PipelineTesterMixin.required_optional_params - {'''latents'''}




							def        UpperCAmelCase__      (	self					:	Any):
									return self._get_dummy_components()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Optional[int]    ,    A_					:	List[Any]=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_      :					Dict     =			torch.manual_seed(A_)
									else:
											lowerCAmelCase_      :					List[Any]     =			torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_      :					List[Any]     =			floats_tensor((1, 3, 3_2, 3_2)    ,    rng=random.Random(A_)).to(A_)

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

									return inputs




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_save_load_optional_components()




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




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Dict):
									# 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)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Tuple):
									super().test_floataa_inference(expected_max_diff=1e-1)




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




							def        UpperCAmelCase__      (	self					:	int):
									self._test_save_load_local()







							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2    ,    )





