from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

__lowerCamelCase					:	Any       =  logging.get_logger(__name__)






@add_end_docstrings(lowercase__					)
class 		a__						(					lowercase__					):

					def __init__(    self      :       List[Any],*_A      :       List[str],**_A      :       List[str]    ):







							"""simple docstring"""






							super().__init__(*__lowercase,**__lowercase    )
							requires_backends(self,"vision"    )
							self.check_model_type(__lowercase    )

					def __call__(    self      :       int,_A      :       Union[str, List[str], "Image.Image", List["Image.Image"]],**_A      :       Optional[int]    ):







							"""simple docstring"""






							return super().__call__(__lowercase,**__lowercase    )

					def 				__UpperCamelCase   (    self      :       List[str],**_A      :       Optional[Any]    ):







							"""simple docstring"""






							return {}, {}, {}

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    load_image(__lowercase    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image.size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.image_processor(images=__lowercase,return_tensors=self.framework    )
							return model_inputs

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.model(**__lowercase    )
							return model_outputs






					def 				__UpperCamelCase   (    self      :       str,_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model_outputs.predicted_depth
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    torch.nn.functional.interpolate(
							    predicted_depth.unsqueeze(1    ),size=self.image_size[::-1],mode="bicubic",align_corners=__lowercase    )
							SCREAMING_SNAKE_CASE_  :		int				      =    prediction.squeeze().cpu().numpy()
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    (output * 255 / np.max(__lowercase    )).astype("uint8"    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    Image.fromarray(__lowercase    )
							SCREAMING_SNAKE_CASE_  :		int				      =    {}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    predicted_depth
							SCREAMING_SNAKE_CASE_  :		Dict				      =    depth
							return output_dict








from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


