
'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__UpperCAmelCase												=   logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCAmelCase												=   '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int   ,     snake_case_				:	Any   ,     snake_case_				:	List[Any]=8							)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 SCREAMING_SNAKE_CASE  :  str								=		width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]   ,     snake_case_				:	int=512   ,     snake_case_				:	List[Any]=512							)       ->	int:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		pil_image.resize((w, h)   ,     resample=Image.BICUBIC   ,     reducing_gap=1							)
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		np.array(pil_image.convert('RGB'							)							)
 SCREAMING_SNAKE_CASE  :  str								=		arr.astype(np.floataa							) / 127.5 - 1
 SCREAMING_SNAKE_CASE  :  List[Any]								=		np.transpose(snake_case_   ,     [2, 0, 1]							)
 SCREAMING_SNAKE_CASE  :  Dict								=		torch.from_numpy(snake_case_							).unsqueeze(0							)
 return image





class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,):
    super().__init__()

    self.register_modules(
        unet=__SCREAMING_SNAKE_CASE    ,scheduler=__SCREAMING_SNAKE_CASE    ,movq=__SCREAMING_SNAKE_CASE    ,)
    SCREAMING_SNAKE_CASE  :  str								=		2 ** (len(self.movq.config.block_out_channels      ) - 1)






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    # get the original timestep using init_timestep
    SCREAMING_SNAKE_CASE  :  Dict								=		min(int(num_inference_steps * strength      )    ,__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Any								=		max(num_inference_steps - init_timestep    ,0      )
    SCREAMING_SNAKE_CASE  :  int								=		self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None      ):
    if not isinstance(__SCREAMING_SNAKE_CASE    ,(torch.Tensor, PIL.Image.Image, list)      ):
     raise ValueError(
         f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE      )}"""      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image.to(device=__SCREAMING_SNAKE_CASE    ,dtype=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		batch_size * num_images_per_prompt

    if image.shape[1] == 4:
     SCREAMING_SNAKE_CASE  :  Any								=		image

    else:
     if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ) and len(__SCREAMING_SNAKE_CASE      ) != batch_size:
      raise ValueError(
          f"""You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE      )}, but requested an effective batch"""
          f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

     elif isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  int								=		[
          self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(__SCREAMING_SNAKE_CASE      )
      ]
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.cat(__SCREAMING_SNAKE_CASE    ,dim=0      )
     else:
      SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.movq.encode(__SCREAMING_SNAKE_CASE      ).latent_dist.sample(__SCREAMING_SNAKE_CASE      )

     SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.movq.config.scaling_factor * init_latents

    SCREAMING_SNAKE_CASE  :  Any								=		torch.cat([init_latents]    ,dim=0      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		init_latents.shape
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		randn_tensor(__SCREAMING_SNAKE_CASE    ,generator=__SCREAMING_SNAKE_CASE    ,device=__SCREAMING_SNAKE_CASE    ,dtype=__SCREAMING_SNAKE_CASE      )

    # get latents
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.scheduler.add_noise(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  int								=		init_latents

    return latents






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE=0      ):
    if is_accelerate_available():
     from accelerate import cpu_offload
    else:
     raise ImportError('Please install accelerate via `pip install accelerate`'      )

    SCREAMING_SNAKE_CASE  :  str								=		torch.device(f"""cuda:{gpu_id}"""      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
      cpu_offload(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE=0      ):
    if is_accelerate_available() and is_accelerate_version('>='    ,'0.17.0.dev0'      ):
     from accelerate import cpu_offload_with_hook
    else:
     raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'      )

    SCREAMING_SNAKE_CASE  :  int								=		torch.device(f"""cuda:{gpu_id}"""      )

    if self.device.type != "cpu":
     self.to('cpu'    ,silence_dtype_warnings=__SCREAMING_SNAKE_CASE      )
     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

    SCREAMING_SNAKE_CASE  :  List[str]								=		None
    for cpu_offloaded_model in [self.unet, self.movq]:
     SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		cpu_offload_with_hook(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,prev_module_hook=__SCREAMING_SNAKE_CASE      )

    # We'll offload the last model manually.
    SCREAMING_SNAKE_CASE  :  str								=		hook






   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 					__a			(       self      ):
    if not hasattr(self.unet    ,'_hf_hook'      ):
     return self.device
    for module in self.unet.modules():
     if (
         hasattr(__SCREAMING_SNAKE_CASE    ,'_hf_hook'      )
         and hasattr(module._hf_hook    ,'execution_device'      )
         and module._hf_hook.execution_device is not None
     ):
      return torch.device(module._hf_hook.execution_device      )
    return self.device






   @torch.no_grad()
   @replace_example_docstring(__SCREAMING_SNAKE_CASE      )
   def __call__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 512    ,__SCREAMING_SNAKE_CASE = 512    ,__SCREAMING_SNAKE_CASE = 100    ,__SCREAMING_SNAKE_CASE = 4.0    ,__SCREAMING_SNAKE_CASE = 0.3    ,__SCREAMING_SNAKE_CASE = 1    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = "pil"    ,__SCREAMING_SNAKE_CASE = True    ,):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self._execution_device

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		guidance_scale > 1.0

    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.cat(__SCREAMING_SNAKE_CASE    ,dim=0      )
    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Dict								=		torch.cat(__SCREAMING_SNAKE_CASE    ,dim=0      )
    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Any								=		torch.cat(__SCREAMING_SNAKE_CASE    ,dim=0      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image_embeds.shape[0]

    if do_classifier_free_guidance:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE    ,dim=0      )
     SCREAMING_SNAKE_CASE  :  List[str]								=		negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE    ,dim=0      )
     SCREAMING_SNAKE_CASE  :  Any								=		hint.repeat_interleave(__SCREAMING_SNAKE_CASE    ,dim=0      )

    SCREAMING_SNAKE_CASE  :  str								=		torch.cat([negative_image_embeds, image_embeds]    ,dim=0      ).to(dtype=self.unet.dtype    ,device=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		torch.cat([hint, hint]    ,dim=0      ).to(dtype=self.unet.dtype    ,device=__SCREAMING_SNAKE_CASE      )

    if not isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  List[str]								=		[image]
    if not all(isinstance(__SCREAMING_SNAKE_CASE    ,(PIL.Image.Image, torch.Tensor)      ) for i in image      ):
     raise ValueError(
         f"""Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE      ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		torch.cat([prepare_image(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ) for i in image]    ,dim=0      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		image.to(dtype=image_embeds.dtype    ,device=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.movq.encode(__SCREAMING_SNAKE_CASE      )['latents']
    SCREAMING_SNAKE_CASE  :  Any								=		latents.repeat_interleave(__SCREAMING_SNAKE_CASE    ,dim=0      )
    self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    ,device=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Any								=		self.get_timesteps(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		timesteps[:1].repeat(batch_size * num_images_per_prompt      )
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[int]								=		downscale_height_and_width(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,self.movq_scale_factor      )
    SCREAMING_SNAKE_CASE  :  Any								=		self.prepare_latents(
        __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,image_embeds.dtype    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE      )      ):
     # expand the latents if we are doing classifier free guidance
     SCREAMING_SNAKE_CASE  :  Any								=		torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'image_embeds': image_embeds, 'hint': hint}
     SCREAMING_SNAKE_CASE  :  Tuple								=		self.unet(
         sample=__SCREAMING_SNAKE_CASE    ,timestep=__SCREAMING_SNAKE_CASE    ,encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,added_cond_kwargs=__SCREAMING_SNAKE_CASE    ,return_dict=__SCREAMING_SNAKE_CASE    ,)[0]

     if do_classifier_free_guidance:
      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[int]								=		noise_pred.split(latents.shape[1]    ,dim=1      )
      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Any								=		noise_pred.chunk(2      )
      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  int								=		variance_pred.chunk(2      )
      SCREAMING_SNAKE_CASE  :  str								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
      SCREAMING_SNAKE_CASE  :  int								=		torch.cat([noise_pred, variance_pred_text]    ,dim=1      )

     if not (
         hasattr(self.scheduler.config    ,'variance_type'      )
         and self.scheduler.config.variance_type in ["learned", "learned_range"]
     ):
      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  str								=		noise_pred.split(latents.shape[1]    ,dim=1      )

     # compute the previous noisy sample x_t -> x_t-1

     SCREAMING_SNAKE_CASE  :  int								=		self.scheduler.step(
         __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,generator=__SCREAMING_SNAKE_CASE    ,)[0]

    # post-processing
    SCREAMING_SNAKE_CASE  :  Any								=		self.movq.decode(__SCREAMING_SNAKE_CASE    ,force_not_quantize=__SCREAMING_SNAKE_CASE      )['sample']

    if output_type not in ["pt", "np", "pil"]:
     raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""      )

    if output_type in ["np", "pil"]:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image * 0.5 + 0.5
     SCREAMING_SNAKE_CASE  :  Dict								=		image.clamp(0    ,1      )
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image.cpu().permute(0    ,2    ,3    ,1      ).float().numpy()

    if output_type == "pil":
     SCREAMING_SNAKE_CASE  :  str								=		self.numpy_to_pil(__SCREAMING_SNAKE_CASE      )

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE      )




'''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


__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 'gpt2'
   A		= ['past_key_values']
   A		= {
       'hidden_size': 'n_embd',
       'max_position_embeddings': 'n_positions',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=50257    ,__SCREAMING_SNAKE_CASE=1024    ,__SCREAMING_SNAKE_CASE=768    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE="gelu_new"    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=1e-5    ,__SCREAMING_SNAKE_CASE=0.02    ,__SCREAMING_SNAKE_CASE="cls_index"    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=False    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		vocab_size
    SCREAMING_SNAKE_CASE  :  Tuple								=		n_positions
    SCREAMING_SNAKE_CASE  :  int								=		n_embd
    SCREAMING_SNAKE_CASE  :  Dict								=		n_layer
    SCREAMING_SNAKE_CASE  :  Dict								=		n_head
    SCREAMING_SNAKE_CASE  :  List[Any]								=		n_inner
    SCREAMING_SNAKE_CASE  :  Dict								=		activation_function
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		resid_pdrop
    SCREAMING_SNAKE_CASE  :  str								=		embd_pdrop
    SCREAMING_SNAKE_CASE  :  int								=		attn_pdrop
    SCREAMING_SNAKE_CASE  :  Tuple								=		layer_norm_epsilon
    SCREAMING_SNAKE_CASE  :  Tuple								=		initializer_range
    SCREAMING_SNAKE_CASE  :  Any								=		summary_type
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_use_proj
    SCREAMING_SNAKE_CASE  :  int								=		summary_activation
    SCREAMING_SNAKE_CASE  :  int								=		summary_first_dropout
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_proj_to_labels
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		scale_attn_weights
    SCREAMING_SNAKE_CASE  :  Any								=		use_cache
    SCREAMING_SNAKE_CASE  :  List[str]								=		scale_attn_by_inverse_layer_idx
    SCREAMING_SNAKE_CASE  :  Any								=		reorder_and_upcast_attn

    SCREAMING_SNAKE_CASE  :  Tuple								=		bos_token_id
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		eos_token_id

    super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,eos_token_id=__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = "default"    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 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?
     SCREAMING_SNAKE_CASE  :  int								=		0






   @property
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
    if self.use_past:
     self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE    ,direction='inputs'      )
     SCREAMING_SNAKE_CASE  :  Dict								=		{0: 'batch', 1: 'past_sequence + sequence'}
    else:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{0: 'batch', 1: 'sequence'}

    return common_inputs






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






   @property
   def 					__a			(       self      ):
    return self._config.n_head






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = False    ,__SCREAMING_SNAKE_CASE = None    ,):
    SCREAMING_SNAKE_CASE  :  List[str]								=		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()
    SCREAMING_SNAKE_CASE  :  int								=		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

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

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		common_inputs['attention_mask']
    if self.use_past:
     SCREAMING_SNAKE_CASE  :  Tuple								=		ordered_inputs['attention_mask'].dtype
     SCREAMING_SNAKE_CASE  :  Any								=		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 					__a			(       self      ):
    return 13


