

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

SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase				)
class 			a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


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


						def __call__(       self						,					A_						,					**A_	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					**A_	):







										'''simple docstring'''


										return {}, {}, {}


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    load_image(UpperCamelCase_	)
										_UpperCAmelCase       :		Optional[Any] 		=    image.size
										_UpperCAmelCase       :		Optional[Any] 		=    self.image_processor(images=UpperCamelCase_						,					return_tensors=self.framework	)
										return model_inputs


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.model(**UpperCamelCase_	)
										return model_outputs





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


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





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"megatron-bert"


						def __init__(       self						,					A_=29056						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=A_						,					**A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    vocab_size
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		str 		=    type_vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_range
										_UpperCAmelCase       :		Dict 		=    layer_norm_eps
										_UpperCAmelCase       :		Tuple 		=    position_embedding_type
										_UpperCAmelCase       :		Tuple 		=    use_cache


