from typing import List, Union

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


a							=logging.get_logger(__name__)

a							={
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Dict  							=	'''led'''
				A__				:       Tuple  							=	{
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''attention_probs_dropout_prob''': '''attention_dropout''',
				    '''initializer_range''': '''init_std''',
				}

				def __init__(				self     ,			_lowerCamelCase=50265     ,			_lowerCamelCase=16384     ,			_lowerCamelCase=1024     ,			_lowerCamelCase=12     ,			_lowerCamelCase=4096     ,			_lowerCamelCase=16     ,			_lowerCamelCase=12     ,			_lowerCamelCase=4096     ,			_lowerCamelCase=16     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=1024     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=2     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=1     ,			_lowerCamelCase=0     ,			_lowerCamelCase=2     ,			_lowerCamelCase = 512     ,			**_lowerCamelCase     ,			):
										lowerCamelCase__ =vocab_size
										lowerCamelCase__ =max_encoder_position_embeddings
										lowerCamelCase__ =max_decoder_position_embeddings
										lowerCamelCase__ =d_model
										lowerCamelCase__ =encoder_ffn_dim
										lowerCamelCase__ =encoder_layers
										lowerCamelCase__ =encoder_attention_heads
										lowerCamelCase__ =decoder_ffn_dim
										lowerCamelCase__ =decoder_layers
										lowerCamelCase__ =decoder_attention_heads
										lowerCamelCase__ =dropout
										lowerCamelCase__ =attention_dropout
										lowerCamelCase__ =activation_dropout
										lowerCamelCase__ =activation_function
										lowerCamelCase__ =init_std
										lowerCamelCase__ =encoder_layerdrop
										lowerCamelCase__ =decoder_layerdrop
										lowerCamelCase__ =classifier_dropout
										lowerCamelCase__ =use_cache
										lowerCamelCase__ =encoder_layers
										lowerCamelCase__ =attention_window

										super().__init__(
										    pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase     ,			is_encoder_decoder=_lowerCamelCase     ,			decoder_start_token_id=_lowerCamelCase     ,			**_lowerCamelCase     ,			)



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

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

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": 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 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				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 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


