

"""simple docstring"""



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"gptj"
    UpperCAmelCase						 =					{
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(     self: str    ,						UpperCamelCase: Union[str, Any]=5_04_00    ,						UpperCamelCase: Any=20_48    ,						UpperCamelCase: Tuple=40_96    ,						UpperCamelCase: Any=28    ,						UpperCamelCase: List[str]=16    ,						UpperCamelCase: List[Any]=64    ,						UpperCamelCase: Optional[Any]=None    ,						UpperCamelCase: Tuple="gelu_new"    ,						UpperCamelCase: Dict=0.0    ,						UpperCamelCase: List[Any]=0.0    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: Any=1e-5    ,						UpperCamelCase: List[Any]=0.02    ,						UpperCamelCase: Any=True    ,						UpperCamelCase: Optional[Any]=5_02_56    ,						UpperCamelCase: Optional[int]=5_02_56    ,						UpperCamelCase: str=False    ,						**UpperCamelCase: Optional[Any]    ,						):






         """simple docstring"""


         A__										=  vocab_size
         A__										=  n_positions
         A__										=  n_embd
         A__										=  n_layer
         A__										=  n_head
         A__										=  n_inner
         A__										=  rotary_dim
         A__										=  activation_function
         A__										=  resid_pdrop
         A__										=  embd_pdrop
         A__										=  attn_pdrop
         A__										=  layer_norm_epsilon
         A__										=  initializer_range
         A__										=  use_cache

         A__										=  bos_token_id
         A__										=  eos_token_id

         super().__init__(
             bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						tie_word_embeddings=UpperCamelCase    ,						**UpperCamelCase       )



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Any    ,						UpperCamelCase: PretrainedConfig    ,						UpperCamelCase: str = "default"    ,						UpperCamelCase: List[PatchingSpec] = None    ,						UpperCamelCase: bool = False    ,						):






         """simple docstring"""


         super().__init__(UpperCamelCase    ,						task=UpperCamelCase    ,						patching_specs=UpperCamelCase    ,						use_past=UpperCamelCase       )
         if not getattr(self._config    ,						"""pad_token_id"""    ,						UpperCamelCase       ):
              # TODO: how to do that better?
              A__										=  0

    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
         if self.use_past:
              self.fill_with_past_key_values_(UpperCamelCase    ,						direction="""inputs"""       )
              A__										=  {0: """batch""", 1: """past_sequence + sequence"""}
         else:
              A__										=  {0: """batch""", 1: """sequence"""}

         return common_inputs

    @property
    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         return self._config.n_layer

    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         return self._config.n_head

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: PreTrainedTokenizer    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: bool = False    ,						UpperCamelCase: Optional[TensorType] = None    ,						):






         """simple docstring"""


         A__										=  super(UpperCamelCase    ,						self       ).generate_dummy_inputs(
             UpperCamelCase    ,						batch_size=UpperCamelCase    ,						seq_length=UpperCamelCase    ,						is_pair=UpperCamelCase    ,						framework=UpperCamelCase       )

         # We need to order the input in the way they appears in the forward()
         A__										=  OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}       )

         # Need to add the past_keys
         if self.use_past:
              if not is_torch_available():
                   raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
              else:
                   import torch

                   A__			,				A__										=  common_inputs["""input_ids"""].shape
                   # Not using the same length for past_key_values
                   A__										=  seqlen + 2
                   A__										=  (
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   A__										=  [
                       (torch.zeros(UpperCamelCase       ), torch.zeros(UpperCamelCase       )) for _ in range(self.num_layers       )
                   ]

         A__										=  common_inputs["""attention_mask"""]
         if self.use_past:
              A__										=  ordered_inputs["""attention_mask"""].dtype
              A__										=  torch.cat(
                  [ordered_inputs["""attention_mask"""], torch.ones(UpperCamelCase    ,						UpperCamelCase    ,						dtype=UpperCamelCase       )]    ,						dim=1       )

         return ordered_inputs





    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 13









"""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 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  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 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  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 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  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






