

'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"gpt_neo"
       lowerCAmelCase_												=			["past_key_values"]
       lowerCAmelCase_												=			{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







       def __init__(				self      :			str    ,   __lowercase      :			Any=50257    ,   __lowercase      :			Any=2048    ,   __lowercase      :			Any=2048    ,   __lowercase      :			Optional[Any]=24    ,   __lowercase      :			int=[[["global", "local"], 12]]    ,   __lowercase      :			Union[str, Any]=16    ,   __lowercase      :			int=None    ,   __lowercase      :			Union[str, Any]=256    ,   __lowercase      :			Optional[int]="gelu_new"    ,   __lowercase      :			int=0.0    ,   __lowercase      :			List[Any]=0.0    ,   __lowercase      :			Dict=0.0    ,   __lowercase      :			Optional[int]=0.1    ,   __lowercase      :			List[str]=1E-5    ,   __lowercase      :			Optional[int]=0.0_2    ,   __lowercase      :			Tuple=True    ,   __lowercase      :			Tuple=50256    ,   __lowercase      :			Tuple=50256    ,   **__lowercase      :			Tuple    ,   ):







            """simple docstring"""







            __lowercase      		=vocab_size
            __lowercase      		=max_position_embeddings
            __lowercase      		=hidden_size
            __lowercase      		=num_layers
            __lowercase      		=num_heads
            __lowercase      		=intermediate_size
            __lowercase      		=window_size
            __lowercase      		=activation_function
            __lowercase      		=resid_dropout
            __lowercase      		=embed_dropout
            __lowercase      		=attention_dropout
            __lowercase      		=classifier_dropout
            __lowercase      		=layer_norm_epsilon
            __lowercase      		=initializer_range
            __lowercase      		=use_cache

            __lowercase      		=bos_token_id
            __lowercase      		=eos_token_id

            __lowercase      		=attention_types
            __lowercase      		=self.expand_attention_types_params(__lowercase   )

            if len(self.attention_layers   ) != self.num_layers:
                 raise ValueError(
                     'Configuration for convolutional module is incorrect. '
                     'It is required that `len(config.attention_layers)` == `config.num_layers` '
                     f'''but is `len(config.attention_layers) = {len(self.attention_layers   )}`, '''
                     f'''`config.num_layers = {self.num_layers}`. '''
                     '`config.attention_layers` is prepared using `config.attention_types`. '
                     'Please verify the value of `config.attention_types` argument.'   )

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







       @staticmethod
       def       snake_case (				__lowercase      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=[]
            for item in attention_types:
                 for _ in range(item[1]   ):
                      attentions.extend(item[0]   )
            return attentions






def 					__UpperCamelCase				(						lowercase__    :	Union[str, Any],       lowercase__    :	Optional[Any],       lowercase__    :	List[str],       lowercase__    :	Optional[int]			):






     '''simple docstring'''





     import torch

     __lowercase      		=input.size()
     __lowercase      		=len(lowercase__			)
     __lowercase      		=shape[dimension]

     __lowercase      		=torch.arange(0,       lowercase__,       lowercase__			)
     __lowercase      		=torch.div(sizedim - size,       lowercase__,       rounding_mode='floor'			) + 1
     __lowercase      		=torch.arange(lowercase__			) + low_indices[:min_length][:, None]

     __lowercase      		=[slice(lowercase__			)] * rank
     __lowercase      		=indices
     __lowercase      		=input[s]

     __lowercase      		=list(range(0,       rank + 1			)			)
     perm.append(perm.pop(dimension + 1			)			)

     return sliced.permute(lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any],       lowercase__    :	Any			):






     '''simple docstring'''





     import torch

     __lowercase      		=torch.arange(1,       lowercase__			)
     __lowercase      		=torch.remainder(lowercase__,       lowercase__			)
     __lowercase      		=remainders == 0
     __lowercase      		=candidates[divisor_indices]
     __lowercase      		=torch.max(lowercase__			)
     return largest_divisor, torch.div(lowercase__,       lowercase__,       rounding_mode='floor'			)







class 	lowerCAmelCase  (   A ):







       @property
       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            __lowercase      		=OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
            if self.use_past:
                 self.fill_with_past_key_values_(__lowercase    ,   direction='inputs'   )
                 __lowercase      		={0: 'batch', 1: 'past_sequence + sequence'}
            else:
                 __lowercase      		={0: 'batch', 1: 'sequence'}

            return common_inputs







       @property
       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            return self._config.num_heads







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			PreTrainedTokenizer    ,   __lowercase      :			int = -1    ,   __lowercase      :			int = -1    ,   __lowercase      :			bool = False    ,   __lowercase      :			Optional[TensorType] = None    ,   ):







            """simple docstring"""







            __lowercase      		=super(__lowercase    ,   self   ).generate_dummy_inputs(
                __lowercase    ,   batch_size=__lowercase    ,   seq_length=__lowercase    ,   is_pair=__lowercase    ,   framework=__lowercase   )

            # We need to order the input in the way they appears in the forward()
            __lowercase      		=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

                      __lowercase		,  __lowercase      		=common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      __lowercase      		=seqlen + 2
                      __lowercase      		=(
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      __lowercase      		=[
                          (torch.zeros(__lowercase   ), torch.zeros(__lowercase   )) for _ in range(self.num_layers   )
                      ]

            __lowercase      		=common_inputs['attention_mask']
            if self.use_past:
                 __lowercase      		=ordered_inputs['attention_mask'].dtype
                 __lowercase      		=torch.cat(
                     [ordered_inputs['attention_mask'], torch.ones(__lowercase    ,   __lowercase    ,   dtype=__lowercase   )]    ,   dim=1   )

            return ordered_inputs







       @property
       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            return 13





'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	Dict			):






     '''simple docstring'''





     __lowercase      		=DPTConfig()

     if "large" in checkpoint_url:
          __lowercase      		=10_24
          __lowercase      		=40_96
          __lowercase      		=24
          __lowercase      		=16
          __lowercase      		=[5, 11, 17, 23]
          __lowercase      		=[2_56, 5_12, 10_24, 10_24]
          __lowercase      		=(1, 3_84, 3_84)

     if "ade" in checkpoint_url:
          __lowercase      		=True

          __lowercase      		=1_50
          __lowercase      		='huggingface/label-files'
          __lowercase      		='ade20k-id2label.json'
          __lowercase      		=json.load(open(cached_download(hf_hub_url(lowercase__,       lowercase__,       repo_type='dataset'			)			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}
          __lowercase      		=[1, 1_50, 4_80, 4_80]

     return config, expected_shape

def 					__UpperCamelCase				(						lowercase__    :	Optional[int]			):






     '''simple docstring'''





     __lowercase      		=['pretrained.model.head.weight', 'pretrained.model.head.bias']
     for k in ignore_keys:
          state_dict.pop(lowercase__,       lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          __lowercase      		=name.replace('pretrained.model',       'dpt.encoder'			)
     if "pretrained.model" in name:
          __lowercase      		=name.replace('pretrained.model',       'dpt.embeddings'			)
     if "patch_embed" in name:
          __lowercase      		=name.replace('patch_embed',       'patch_embeddings'			)
     if "pos_embed" in name:
          __lowercase      		=name.replace('pos_embed',       'position_embeddings'			)
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "proj" in name and "project" not in name:
          __lowercase      		=name.replace('proj',       'projection'			)
     if "blocks" in name:
          __lowercase      		=name.replace('blocks',       'layer'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "scratch.output_conv" in name:
          __lowercase      		=name.replace('scratch.output_conv',       'head'			)
     if "scratch" in name:
          __lowercase      		=name.replace('scratch',       'neck'			)
     if "layer1_rn" in name:
          __lowercase      		=name.replace('layer1_rn',       'convs.0'			)
     if "layer2_rn" in name:
          __lowercase      		=name.replace('layer2_rn',       'convs.1'			)
     if "layer3_rn" in name:
          __lowercase      		=name.replace('layer3_rn',       'convs.2'			)
     if "layer4_rn" in name:
          __lowercase      		=name.replace('layer4_rn',       'convs.3'			)
     if "refinenet" in name:
          __lowercase      		=int(name[len('neck.refinenet'			) : len('neck.refinenet'			) + 1]			)
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          __lowercase      		=name.replace(F'''refinenet{layer_idx}''',       F'''fusion_stage.layers.{abs(layer_idx-4			)}'''			)
     if "out_conv" in name:
          __lowercase      		=name.replace('out_conv',       'projection'			)
     if "resConfUnit1" in name:
          __lowercase      		=name.replace('resConfUnit1',       'residual_layer1'			)
     if "resConfUnit2" in name:
          __lowercase      		=name.replace('resConfUnit2',       'residual_layer2'			)
     if "conv1" in name:
          __lowercase      		=name.replace('conv1',       'convolution1'			)
     if "conv2" in name:
          __lowercase      		=name.replace('conv2',       'convolution2'			)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.0.project.0',       'neck.reassemble_stage.readout_projects.0.0'			)
     if "pretrained.act_postprocess2.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.0.project.0',       'neck.reassemble_stage.readout_projects.1.0'			)
     if "pretrained.act_postprocess3.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess3.0.project.0',       'neck.reassemble_stage.readout_projects.2.0'			)
     if "pretrained.act_postprocess4.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.0.project.0',       'neck.reassemble_stage.readout_projects.3.0'			)
     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.3',       'neck.reassemble_stage.layers.0.projection'			)
     if "pretrained.act_postprocess1.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.4',       'neck.reassemble_stage.layers.0.resize'			)
     if "pretrained.act_postprocess2.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.3',       'neck.reassemble_stage.layers.1.projection'			)
     if "pretrained.act_postprocess2.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.4',       'neck.reassemble_stage.layers.1.resize'			)
     if "pretrained.act_postprocess3.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess3.3',       'neck.reassemble_stage.layers.2.projection'			)
     if "pretrained.act_postprocess4.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.3',       'neck.reassemble_stage.layers.3.projection'			)
     if "pretrained.act_postprocess4.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.4',       'neck.reassemble_stage.layers.3.resize'			)
     if "pretrained" in name:
          __lowercase      		=name.replace('pretrained',       'dpt'			)
     if "bn" in name:
          __lowercase      		=name.replace('bn',       'batch_norm'			)
     if "head" in name:
          __lowercase      		=name.replace('head',       'head.head'			)
     if "encoder.norm" in name:
          __lowercase      		=name.replace('encoder.norm',       'layernorm'			)
     if "auxlayer" in name:
          __lowercase      		=name.replace('auxlayer',       'auxiliary_head.head'			)

     return name

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     for i in range(config.num_hidden_layers			):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __lowercase      		=state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[
              -config.hidden_size :, :
          ]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		='http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowercase      		=Image.open(requests.get(lowercase__,       stream=lowercase__			).raw			)
     return im

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	Optional[Any],       lowercase__    :	str,       lowercase__    :	List[str],       lowercase__    :	List[str]			):






     '''simple docstring'''





     __lowercase		,  __lowercase      		=get_dpt_config(lowercase__			)
     # load original state_dict from URL
     __lowercase      		=torch.hub.load_state_dict_from_url(lowercase__,       map_location='cpu'			)
     # remove certain keys
     remove_ignore_keys_(lowercase__			)
     # rename keys
     for key in state_dict.copy().keys():
          __lowercase      		=state_dict.pop(lowercase__			)
          __lowercase      		=val
     # read in qkv matrices
     read_in_q_k_v(lowercase__,       lowercase__			)

     # load HuggingFace model
     __lowercase      		=DPTForSemanticSegmentation(lowercase__			) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase__			)
     model.load_state_dict(lowercase__			)
     model.eval()

     # Check outputs on an image
     __lowercase      		=4_80 if 'ade' in checkpoint_url else 3_84
     __lowercase      		=DPTImageProcessor(size=lowercase__			)

     __lowercase      		=prepare_img()
     __lowercase      		=image_processor(lowercase__,       return_tensors='pt'			)

     # forward pass
     __lowercase      		=model(**lowercase__			).logits if 'ade' in checkpoint_url else model(**lowercase__			).predicted_depth

     # Assert logits
     __lowercase      		=torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]			)
     if "ade" in checkpoint_url:
          __lowercase      		=torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]			)
     assert outputs.shape == torch.Size(lowercase__			)
     assert (
         torch.allclose(outputs[0, 0, :3, :3],       lowercase__,       atol=1E-4			)
         if "ade" in checkpoint_url
         else torch.allclose(outputs[0, :3, :3],       lowercase__			)
     )

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(F'''Saving model to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)

     if push_to_hub:
          print('Pushing model to hub...'			)
          model.push_to_hub(
              repo_path_or_name=Path(lowercase__,       lowercase__			),       organization='nielsr',       commit_message='Add model',       use_temp_dir=lowercase__,       )
          image_processor.push_to_hub(
              repo_path_or_name=Path(lowercase__,       lowercase__			),       organization='nielsr',       commit_message='Add image processor',       use_temp_dir=lowercase__,       )


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
           type=str,
           help='''URL of the original DPT checkpoint you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument(
           '''--push_to_hub''',
           action='''store_true''',
       )
       parser.add_argument(
           '''--model_name''',
           default='''dpt-large''',
           type=str,
           help='''Name of the model, in case you\'re pushing to the hub.''',
       )

       UpperCAmelCase					 =	parser.parse_args()
       convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


