


'''simple docstring'''






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


__A :  List[str]    							=				logging.get_logger(__name__)

__A :  str    							=				{
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 'open-llama'

    def __init__(       self   :  Union[str, Any]   ,						lowerCamelCase   :  Optional[int]=10_00_00   ,						lowerCamelCase   :  int=40_96   ,						lowerCamelCase   :  Union[str, Any]=1_10_08   ,						lowerCamelCase   :  Union[str, Any]=32   ,						lowerCamelCase   :  Optional[int]=32   ,						lowerCamelCase   :  List[Any]="silu"   ,						lowerCamelCase   :  List[str]=20_48   ,						lowerCamelCase   :  List[Any]=0.02   ,						lowerCamelCase   :  Any=1E-6   ,						lowerCamelCase   :  Optional[int]=True   ,						lowerCamelCase   :  Dict=0   ,						lowerCamelCase   :  Dict=1   ,						lowerCamelCase   :  List[str]=2   ,						lowerCamelCase   :  Optional[int]=False   ,						lowerCamelCase   :  Union[str, Any]=True   ,						lowerCamelCase   :  Any=0.1   ,						lowerCamelCase   :  str=0.1   ,						lowerCamelCase   :  Optional[int]=True   ,						lowerCamelCase   :  Tuple=True   ,						lowerCamelCase   :  Any=None   ,						**lowerCamelCase   :  Any   ,						)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Any      =       vocab_size
      lowerCAmelCase_      :				str      =       max_position_embeddings
      lowerCAmelCase_      :				Dict      =       hidden_size
      lowerCAmelCase_      :				int      =       intermediate_size
      lowerCAmelCase_      :				Optional[int]      =       num_hidden_layers
      lowerCAmelCase_      :				List[Any]      =       num_attention_heads
      lowerCAmelCase_      :				List[Any]      =       hidden_act
      lowerCAmelCase_      :				List[str]      =       initializer_range
      lowerCAmelCase_      :				Union[str, Any]      =       rms_norm_eps
      lowerCAmelCase_      :				Dict      =       use_cache
      lowerCAmelCase_      :				Dict      =       kwargs.pop(
          """use_memorry_efficient_attention"""   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       hidden_dropout_prob
      lowerCAmelCase_      :				str      =       attention_dropout_prob
      lowerCAmelCase_      :				int      =       use_stable_embedding
      lowerCAmelCase_      :				Union[str, Any]      =       shared_input_output_embedding
      lowerCAmelCase_      :				Tuple      =       rope_scaling
      self._rope_scaling_validation()

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





    def __lowercase       (       self   :  str		)       ->  Optional[int]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling   ,						lowerCamelCase		) 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_      :				Union[str, Any]      =       self.rope_scaling.get("""type"""   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       self.rope_scaling.get("""factor"""   ,						lowerCamelCase		)
      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(lowerCamelCase   ,						lowerCamelCase		) 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 unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDModel(
          block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



