





'''simple docstring'''







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


_lowercase       						=			logging.get_logger(__name__)

_lowercase       						=			{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''gpt_neox'''







     def __init__( self    ,   A__=5_04_32    ,   A__=61_44    ,   A__=44    ,   A__=64    ,   A__=2_45_76    ,   A__="gelu"    ,   A__=0.2_5    ,   A__=1_00_00    ,   A__=0.0    ,   A__=0.0    ,   A__=0.1    ,   A__=20_48    ,   A__=0.0_2    ,   A__=1e-5    ,   A__=True    ,   A__=0    ,   A__=2    ,   A__=False    ,   A__=True    ,   A__=None    ,   **A__    ,   ) ->	List[str]:
      super().__init__(bos_token_id=A__    ,   eos_token_id=A__    ,   **A__    )
      snake_case				=			vocab_size
      snake_case				=			max_position_embeddings
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			rotary_pct
      snake_case				=			rotary_emb_base
      snake_case				=			attention_dropout
      snake_case				=			hidden_dropout
      snake_case				=			classifier_dropout
      snake_case				=			initializer_range
      snake_case				=			layer_norm_eps
      snake_case				=			use_cache
      snake_case				=			tie_word_embeddings
      snake_case				=			use_parallel_residual
      snake_case				=			rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
       raise ValueError(
           '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''    )







     def     UpperCamelCase			( self    ) ->	Dict:
      if self.rope_scaling is None:
       return

      if not isinstance(self.rope_scaling    ,   A__    ) 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}"""    )
      snake_case				=			self.rope_scaling.get('''type'''    ,   A__    )
      snake_case				=			self.rope_scaling.get('''factor'''    ,   A__    )
      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(A__    ,   A__    ) or rope_scaling_factor <= 1.0:
       raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""    )








'''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 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					IFInpaintingSuperResolutionPipeline
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
     _UpperCAmelCase										=					PipelineTesterMixin.required_optional_params - {'''latents'''}







     def     UpperCamelCase			( self    ) ->	int:
      return self._get_superresolution_dummy_components()







     def     UpperCamelCase			( self    ,   A__    ,   A__=0    ) ->	Union[str, Any]:
      if str(A__    ).startswith('''mps'''    ):
       snake_case				=			torch.manual_seed(A__    )
      else:
       snake_case				=			torch.Generator(device=A__    ).manual_seed(A__    )

      snake_case				=			floats_tensor((1, 3, 16, 16)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )

      snake_case				=			{
          '''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     UpperCamelCase			( self    ) ->	List[Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_optional_components()







     @unittest.skipIf(torch_device != '''cuda'''    ,   reason='''float16 requires CUDA'''    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      # 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     UpperCamelCase			( self    ) ->	int:
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_local()







     def     UpperCamelCase			( self    ) ->	Dict:
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2    ,   )

