



"""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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "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      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Optional[int]             =    'gptj'
    a_    :				Optional[int]             =    {
        'max_position_embeddings': 'n_positions',
        'hidden_size': 'n_embd',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					List[Any]=5_04_00							,					SCREAMING_SNAKE_CASE__							:					str=20_48							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=40_96							,					SCREAMING_SNAKE_CASE__							:					str=28							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=16							,					SCREAMING_SNAKE_CASE__							:					str=64							,					SCREAMING_SNAKE_CASE__							:					str=None							,					SCREAMING_SNAKE_CASE__							:					Any="gelu_new"							,					SCREAMING_SNAKE_CASE__							:					str=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=1e-5							,					SCREAMING_SNAKE_CASE__							:					Tuple=0.02							,					SCREAMING_SNAKE_CASE__							:					Dict=True							,					SCREAMING_SNAKE_CASE__							:					str=5_02_56							,					SCREAMING_SNAKE_CASE__							:					Dict=5_02_56							,					SCREAMING_SNAKE_CASE__							:					str=False							,					**SCREAMING_SNAKE_CASE__							:					Dict							,					):
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		n_positions
          lowerCamelCase__           =		n_embd
          lowerCamelCase__           =		n_layer
          lowerCamelCase__           =		n_head
          lowerCamelCase__           =		n_inner
          lowerCamelCase__           =		rotary_dim
          lowerCamelCase__           =		activation_function
          lowerCamelCase__           =		resid_pdrop
          lowerCamelCase__           =		embd_pdrop
          lowerCamelCase__           =		attn_pdrop
          lowerCamelCase__           =		layer_norm_epsilon
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		use_cache

          lowerCamelCase__           =		bos_token_id
          lowerCamelCase__           =		eos_token_id

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





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					PretrainedConfig							,					SCREAMING_SNAKE_CASE__							:					str = "default"							,					SCREAMING_SNAKE_CASE__							:					List[PatchingSpec] = None							,					SCREAMING_SNAKE_CASE__							:					bool = False							,					):
          super().__init__(SCREAMING_SNAKE_CASE__							,					task=SCREAMING_SNAKE_CASE__							,					patching_specs=SCREAMING_SNAKE_CASE__							,					use_past=SCREAMING_SNAKE_CASE__				)
          if not getattr(self._config							,					'pad_token_id'							,					SCREAMING_SNAKE_CASE__				):
                # TODO: how to do that better?
                lowerCamelCase__           =		0
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
          if self.use_past:
                self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__							,					direction='inputs'				)
                lowerCamelCase__           =		{0: 'batch', 1: 'past_sequence + sequence'}
          else:
                lowerCamelCase__           =		{0: 'batch', 1: 'sequence'}

          return common_inputs
    @property
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          return self._config.n_layer
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return self._config.n_head
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					PreTrainedTokenizer							,					SCREAMING_SNAKE_CASE__							:					int = -1							,					SCREAMING_SNAKE_CASE__							:					int = -1							,					SCREAMING_SNAKE_CASE__							:					bool = False							,					SCREAMING_SNAKE_CASE__							:					Optional[TensorType] = None							,					):
          lowerCamelCase__           =		super(SCREAMING_SNAKE_CASE__							,					self				).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE__							,					batch_size=SCREAMING_SNAKE_CASE__							,					seq_length=SCREAMING_SNAKE_CASE__							,					is_pair=SCREAMING_SNAKE_CASE__							,					framework=SCREAMING_SNAKE_CASE__				)

          # We need to order the input in the way they appears in the forward()
          lowerCamelCase__           =		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

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

          lowerCamelCase__           =		common_inputs['attention_mask']
          if self.use_past:
                lowerCamelCase__           =		ordered_inputs['attention_mask'].dtype
                lowerCamelCase__           =		torch.cat(
                    [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)]							,					dim=1				)

          return ordered_inputs


    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          return 13







"""simple docstring"""


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)



