



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


a_			=					logging.get_logger(__name__)

a_			=					{
    """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  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """gptj"""
 lowerCAmelCase__       					=  {
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }




 def __init__(						self	,				__UpperCAmelCase=50400	,				__UpperCAmelCase=2048	,				__UpperCAmelCase=4096	,				__UpperCAmelCase=28	,				__UpperCAmelCase=16	,				__UpperCAmelCase=64	,				__UpperCAmelCase=None	,				__UpperCAmelCase="gelu_new"	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase=1E-5	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=True	,				__UpperCAmelCase=50256	,				__UpperCAmelCase=50256	,				__UpperCAmelCase=False	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __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=__UpperCAmelCase	,				eos_token_id=__UpperCAmelCase	,				tie_word_embeddings=__UpperCAmelCase	,				**__UpperCAmelCase				)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase = "default"	,				__UpperCAmelCase = None	,				__UpperCAmelCase = 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?
       __lowerCamelCase													=    0




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}				)
    if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase	,				direction='''inputs'''				)
       __lowerCamelCase													=    {0: '''batch''', 1: '''past_sequence + sequence'''}
    else:
       __lowerCamelCase													=    {0: '''batch''', 1: '''sequence'''}

    return common_inputs




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._config.n_layer




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._config.n_head




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = -1	,				__UpperCAmelCase = -1	,				__UpperCAmelCase = False	,				__UpperCAmelCase = None	,				):



    '''simple docstring'''
    __lowerCamelCase													=    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()
    __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(__UpperCAmelCase				), torch.zeros(__UpperCAmelCase				)) 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(__UpperCAmelCase	,				__UpperCAmelCase	,				dtype=__UpperCAmelCase				)]	,				dim=1				)

    return ordered_inputs




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 13











import torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






