
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


lowercase  :					Dict						    =							logging.get_logger(__name__)

lowercase  :					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 __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"gptj"
    _a		:						Tuple=				{
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }
    def __init__(  self		,snake_case=50400		,snake_case=2048		,snake_case=4096		,snake_case=28		,snake_case=16		,snake_case=64		,snake_case=None		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Optional[Any]       	=      n_positions
           lowercase							:     Any       	=      n_embd
           lowercase							:     Tuple       	=      n_layer
           lowercase							:     Any       	=      n_head
           lowercase							:     List[Any]       	=      n_inner
           lowercase							:     Optional[Any]       	=      rotary_dim
           lowercase							:     List[str]       	=      activation_function
           lowercase							:     List[str]       	=      resid_pdrop
           lowercase							:     List[str]       	=      embd_pdrop
           lowercase							:     Optional[int]       	=      attn_pdrop
           lowercase							:     Optional[Any]       	=      layer_norm_epsilon
           lowercase							:     Tuple       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Tuple       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

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






class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,snake_case		,snake_case = "default"		,snake_case = None		,snake_case = False		,):



           '''simple docstring'''







           super().__init__(snake_case		,task=snake_case		,patching_specs=snake_case		,use_past=snake_case      )
           if not getattr(self._config		,"""pad_token_id"""		,snake_case      ):
                  # TODO: how to do that better?
                  lowercase							:     List[Any]       	=      0
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Tuple       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     str       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.n_layer
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.n_head
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     Tuple       	=      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

                         lowercase	,			lowercase							:     Any       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     List[Any]       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Union[str, Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Union[str, Any]       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Any       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13



'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase :      Optional[int]					 =     False







class 							A__    (       unittest.TestCase  ):
  pass







@slow
@require_torch_gpu
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Tuple )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion' )
       pipe.to(_a )
       pipe.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =pipe(
           image=_a							,					generator=_a							,					guidance_scale=7.5							,					num_inference_steps=50							,					output_type='numpy'							,					).images

       _SCREAMING_SNAKE_CASE							    =image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )

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