from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    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():
	from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
	from ..tf_utils import stable_softmax

lowercase__			:				Dict		=	logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_						)
class    lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	def __init__(							self		,			**__SCREAMING_SNAKE_CASE				)						->List[str]:
								super().__init__(**__SCREAMING_SNAKE_CASE				)

								requires_backends(self		,			'''vision'''				)
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == '''tf'''
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING				)


	def __call__(							self		,			__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				)						->Union[str, Any]:
								return super().__call__(__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			**__SCREAMING_SNAKE_CASE				)						->Union[str, Any]:
								lowerCAmelCase			=		{}
								if "candidate_labels" in kwargs:
															lowerCAmelCase			=		kwargs['''candidate_labels''']
								if "hypothesis_template" in kwargs:
															lowerCAmelCase			=		kwargs['''hypothesis_template''']

								return preprocess_params, {}, {}


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE="This is a photo of {}."				)						->Optional[Any]:
								lowerCAmelCase			=		load_image(__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		self.image_processor(images=[image]		,			return_tensors=self.framework				)
								lowerCAmelCase			=		candidate_labels
								lowerCAmelCase			=		[hypothesis_template.format(__SCREAMING_SNAKE_CASE				) for x in candidate_labels]
								lowerCAmelCase			=		self.tokenizer(__SCREAMING_SNAKE_CASE		,			return_tensors=self.framework		,			padding=__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		[text_inputs]
								return inputs


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->List[str]:
								lowerCAmelCase			=		model_inputs.pop('''candidate_labels'''				)
								lowerCAmelCase			=		model_inputs.pop('''text_inputs'''				)
								if isinstance(text_inputs[0]		,			__SCREAMING_SNAKE_CASE				):
															lowerCAmelCase			=		text_inputs[0]
								else:
															# Batching case.
															lowerCAmelCase			=		text_inputs[0][0]

								lowerCAmelCase			=		self.model(**__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				)

								lowerCAmelCase			=		{
								    '''candidate_labels''': candidate_labels,
								    '''logits''': outputs.logits_per_image,
								}
								return model_outputs




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->str:
								lowerCAmelCase			=		model_outputs.pop('''candidate_labels'''				)
								lowerCAmelCase			=		model_outputs['''logits'''][0]
								if self.framework == "pt":
															lowerCAmelCase			=		logits.softmax(dim=-1				).squeeze(-1				)
															lowerCAmelCase			=		probs.tolist()
															if not isinstance(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				):
																						lowerCAmelCase			=		[scores]
								elif self.framework == "tf":
															lowerCAmelCase			=		stable_softmax(__SCREAMING_SNAKE_CASE		,			axis=-1				)
															lowerCAmelCase			=		probs.numpy().tolist()
								else:
															raise ValueError(F"Unsupported framework: {self.framework}"				)

								lowerCAmelCase			=		[
								    {'''score''': score, '''label''': candidate_label}
								    for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)		,			key=lambda __SCREAMING_SNAKE_CASE				: -x[0]				)
								]
								return result








'''simple docstring'''





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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



