





from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


_UpperCAmelCase				:       Any	   =				logging.get_logger(__name__)

@add_end_docstrings(__UpperCamelCase			)
class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      int			,     **UpperCAmelCase  :      Any	)			->	Tuple:
     super().__init__(**UpperCAmelCase	)

     if self.framework != "pt":
         raise ValueError(F"""The {self.__class__} is only available in PyTorch."""	)
     # No specific FOR_XXX available yet
 def __call__(      self  :      Any			,     UpperCAmelCase  :      Union[np.ndarray, bytes, str]			,     **UpperCAmelCase  :      Dict	)			->	Tuple:
     return super().__call__(UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Any			,     **UpperCAmelCase  :      Tuple	)			->	Optional[int]:
     lowerCamelCase__    :      List[str]					 =		{}
     if "candidate_labels" in kwargs:
         lowerCamelCase__    :      str					 =		kwargs['candidate_labels']
     if "hypothesis_template" in kwargs:
         lowerCamelCase__    :      List[str]					 =		kwargs['hypothesis_template']

     return preprocess_params, {}, {}
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Optional[Any]=None			,     UpperCAmelCase  :      List[Any]="This is a sound of {}."	)			->	List[str]:
     if isinstance(UpperCAmelCase			,     UpperCAmelCase	):
         if audio.startswith('http://'	) or audio.startswith('https://'	):
             # We need to actually check for a real protocol, otherwise it's impossible to use a local file
             # like http_huggingface_co.png
             lowerCamelCase__    :      Dict					 =		requests.get(UpperCAmelCase	).content
         else:
             with open(UpperCAmelCase			,     'rb'	) as f:
                 lowerCamelCase__    :      List[Any]					 =		f.read()

     if isinstance(UpperCAmelCase			,     UpperCAmelCase	):
         lowerCamelCase__    :      List[Any]					 =		ffmpeg_read(UpperCAmelCase			,     self.feature_extractor.sampling_rate	)

     if not isinstance(UpperCAmelCase			,     np.ndarray	):
         raise ValueError('We expect a numpy ndarray as input'	)
     if len(audio.shape	) != 1:
         raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'	)

     lowerCamelCase__    :      Optional[int]					 =		self.feature_extractor(
         [audio]			,     sampling_rate=self.feature_extractor.sampling_rate			,     return_tensors='pt'	)
     lowerCamelCase__    :      Dict					 =		candidate_labels
     lowerCamelCase__    :      Any					 =		[hypothesis_template.format(UpperCAmelCase	) for x in candidate_labels]
     lowerCamelCase__    :      Optional[int]					 =		self.tokenizer(UpperCAmelCase			,     return_tensors=self.framework			,     padding=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		[text_inputs]
     return inputs
 def      A_	(      self  :      Any			,     UpperCAmelCase  :      Tuple	)			->	List[str]:
     lowerCamelCase__    :      str					 =		model_inputs.pop('candidate_labels'	)
     lowerCamelCase__    :      Dict					 =		model_inputs.pop('text_inputs'	)
     if isinstance(text_inputs[0]			,     UpperCAmelCase	):
         lowerCamelCase__    :      str					 =		text_inputs[0]
     else:
         # Batching case.
         lowerCamelCase__    :      Tuple					 =		text_inputs[0][0]

     lowerCamelCase__    :      int					 =		self.model(**UpperCAmelCase			,     **UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		{
         'candidate_labels': candidate_labels,
         'logits': outputs.logits_per_audio,
     }
     return model_outputs







 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[int]:
     lowerCamelCase__    :      Optional[int]					 =		model_outputs.pop('candidate_labels'	)
     lowerCamelCase__    :      List[Any]					 =		model_outputs['logits'][0]

     if self.framework == "pt":
         lowerCamelCase__    :      Optional[int]					 =		logits.softmax(dim=0	)
         lowerCamelCase__    :      Tuple					 =		probs.tolist()
     else:
         raise ValueError('`tf` framework not supported.'	)

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












import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[str]:
    lowerCamelCase__    :      str					 =		OrderedDict()
    for key, value in state_dict.items():
        if key.startswith('module.encoder' ):
            lowerCamelCase__    :      Optional[Any]					 =		key.replace('module.encoder'						,	'glpn.encoder' )
        if key.startswith('module.decoder' ):
            lowerCamelCase__    :      List[str]					 =		key.replace('module.decoder'						,	'decoder.stages' )
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            lowerCamelCase__    :      Dict					 =		key[key.find('patch_embed' ) + len('patch_embed' )]
            lowerCamelCase__    :      Tuple					 =		key.replace(F"""patch_embed{idx}"""						,	F"""patch_embeddings.{int(_UpperCAmelCase )-1}""" )
        if "norm" in key:
            lowerCamelCase__    :      str					 =		key.replace('norm'						,	'layer_norm' )
        if "glpn.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            lowerCamelCase__    :      Dict					 =		key[key.find('glpn.encoder.layer_norm' ) + len('glpn.encoder.layer_norm' )]
            lowerCamelCase__    :      str					 =		key.replace(F"""layer_norm{idx}"""						,	F"""layer_norm.{int(_UpperCAmelCase )-1}""" )
        if "layer_norm1" in key:
            lowerCamelCase__    :      Optional[int]					 =		key.replace('layer_norm1'						,	'layer_norm_1' )
        if "layer_norm2" in key:
            lowerCamelCase__    :      Optional[int]					 =		key.replace('layer_norm2'						,	'layer_norm_2' )
        if "block" in key:
            # replace for example block1 by block.0
            lowerCamelCase__    :      List[Any]					 =		key[key.find('block' ) + len('block' )]
            lowerCamelCase__    :      int					 =		key.replace(F"""block{idx}"""						,	F"""block.{int(_UpperCAmelCase )-1}""" )
        if "attn.q" in key:
            lowerCamelCase__    :      Union[str, Any]					 =		key.replace('attn.q'						,	'attention.self.query' )
        if "attn.proj" in key:
            lowerCamelCase__    :      Union[str, Any]					 =		key.replace('attn.proj'						,	'attention.output.dense' )
        if "attn" in key:
            lowerCamelCase__    :      Dict					 =		key.replace('attn'						,	'attention.self' )
        if "fc1" in key:
            lowerCamelCase__    :      Dict					 =		key.replace('fc1'						,	'dense1' )
        if "fc2" in key:
            lowerCamelCase__    :      Any					 =		key.replace('fc2'						,	'dense2' )
        if "linear_pred" in key:
            lowerCamelCase__    :      Dict					 =		key.replace('linear_pred'						,	'classifier' )
        if "linear_fuse" in key:
            lowerCamelCase__    :      Tuple					 =		key.replace('linear_fuse.conv'						,	'linear_fuse' )
            lowerCamelCase__    :      List[str]					 =		key.replace('linear_fuse.bn'						,	'batch_norm' )
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            lowerCamelCase__    :      Optional[Any]					 =		key[key.find('linear_c' ) + len('linear_c' )]
            lowerCamelCase__    :      Dict					 =		key.replace(F"""linear_c{idx}"""						,	F"""linear_c.{int(_UpperCAmelCase )-1}""" )
        if "bot_conv" in key:
            lowerCamelCase__    :      str					 =		key.replace('bot_conv'						,	'0.convolution' )
        if "skip_conv1" in key:
            lowerCamelCase__    :      Union[str, Any]					 =		key.replace('skip_conv1'						,	'1.convolution' )
        if "skip_conv2" in key:
            lowerCamelCase__    :      List[Any]					 =		key.replace('skip_conv2'						,	'2.convolution' )
        if "fusion1" in key:
            lowerCamelCase__    :      Optional[int]					 =		key.replace('fusion1'						,	'1.fusion' )
        if "fusion2" in key:
            lowerCamelCase__    :      Union[str, Any]					 =		key.replace('fusion2'						,	'2.fusion' )
        if "fusion3" in key:
            lowerCamelCase__    :      List[Any]					 =		key.replace('fusion3'						,	'3.fusion' )
        if "fusion" in key and "conv" in key:
            lowerCamelCase__    :      str					 =		key.replace('conv'						,	'convolutional_layer' )
        if key.startswith('module.last_layer_depth' ):
            lowerCamelCase__    :      Dict					 =		key.replace('module.last_layer_depth'						,	'head.head' )
        lowerCamelCase__    :      str					 =		value

    return new_state_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    # for each of the encoder blocks:
    for i in range(config.num_encoder_blocks ):
        for j in range(config.depths[i] ):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            lowerCamelCase__    :      Any					 =		state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
            lowerCamelCase__    :      Optional[Any]					 =		state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
            # next, add keys and values (in that order) to the state dict
            lowerCamelCase__    :      Optional[int]					 =		kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__    :      Optional[int]					 =		kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__    :      Any					 =		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__    :      Dict					 =		kv_bias[config.hidden_sizes[i] :]


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      List[str]					 =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__    :      Tuple					 =		Image.open(requests.get(_UpperCAmelCase						,	stream=_UpperCAmelCase ).raw )

    return image


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False						,	_UpperCAmelCase=None )			->     Optional[int]:
    lowerCamelCase__    :      str					 =		GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,	decoder_hidden_size=64						,	depths=[3, 8, 27, 3] )

    # load image processor (only resize + rescale)
    lowerCamelCase__    :      Union[str, Any]					 =		GLPNImageProcessor()

    # prepare image
    lowerCamelCase__    :      str					 =		prepare_img()
    lowerCamelCase__    :      Tuple					 =		image_processor(images=_UpperCAmelCase						,	return_tensors='pt' ).pixel_values

    logger.info('Converting model...' )

    # load original state dict
    lowerCamelCase__    :      Any					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )

    # rename keys
    lowerCamelCase__    :      str					 =		rename_keys(_UpperCAmelCase )

    # key and value matrices need special treatment
    read_in_k_v(_UpperCAmelCase						,	_UpperCAmelCase )

    # create HuggingFace model and load state dict
    lowerCamelCase__    :      Dict					 =		GLPNForDepthEstimation(_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )
    model.eval()

    # forward pass
    lowerCamelCase__    :      List[str]					 =		model(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowerCamelCase__    :      List[Any]					 =		torch.tensor(
                [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
        elif "kitti" in model_name:
            lowerCamelCase__    :      List[str]					 =		torch.tensor(
                [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
        else:
            raise ValueError(F"""Unknown model name: {model_name}""" )

        lowerCamelCase__    :      Tuple					 =		torch.Size([1, 480, 640] )

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]						,	_UpperCAmelCase						,	atol=1e-4 )
        print('Looks ok!' )

    # finally, push to hub if required
    if push_to_hub:
        logger.info('Pushing model and image processor to the hub...' )
        model.push_to_hub(
            repo_path_or_name=Path(_UpperCAmelCase						,	_UpperCAmelCase )						,	organization='nielsr'						,	commit_message='Add model'						,	use_temp_dir=_UpperCAmelCase						,	)
        image_processor.push_to_hub(
            repo_path_or_name=Path(_UpperCAmelCase						,	_UpperCAmelCase )						,	organization='nielsr'						,	commit_message='Add image processor'						,	use_temp_dir=_UpperCAmelCase						,	)


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()

    parser.add_argument(
        """--checkpoint_path""",
        default=None,
        type=str,
        help="""Path to the original PyTorch checkpoint (.pth file).""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
    )
    parser.add_argument(
        """--model_name""",
        default="""glpn-kitti""",
        type=str,
        help="""Name of the model in case you're pushing to the hub.""",
    )
    _UpperCAmelCase				:       int	   =				parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





