





'''simple docstring'''






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

from packaging import version


if TYPE_CHECKING:
	from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}




class  _A (					__lowercase		):
		lowercase__:   Any				     =   '''bloom'''
		lowercase__:   List[str]				     =   ['''past_key_values''']
		lowercase__:   Any				     =   {
		    '''num_hidden_layers''': '''n_layer''',
		    '''num_attention_heads''': '''n_head''',
		}

		def __init__(   self					:   str  ,      __magic_name__					:   str=25_08_80  ,      __magic_name__					:   Optional[Any]=64  ,      __magic_name__					:   Dict=2  ,      __magic_name__					:   Optional[int]=8  ,      __magic_name__					:   Optional[int]=1E-5  ,      __magic_name__					:   Optional[int]=0.02  ,      __magic_name__					:   Union[str, Any]=True  ,      __magic_name__					:   int=1  ,      __magic_name__					:   int=2  ,      __magic_name__					:   List[str]=False  ,      __magic_name__					:   str=0.0  ,      __magic_name__					:   List[Any]=0.0  ,      __magic_name__					:   List[Any]=1  ,      __magic_name__					:   Any=False  ,      **__magic_name__					:   List[Any]  ,      )					->    List[str]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     vocab_size
					# Backward compatibility with n_embed kwarg
					__snake_case					:      List[str]							      =     kwargs.pop("""n_embed"""  ,      __magic_name__			)
					__snake_case					:      Union[str, Any]							      =     hidden_size if n_embed is None else n_embed
					__snake_case					:      Union[str, Any]							      =     n_layer
					__snake_case					:      Dict							      =     n_head
					__snake_case					:      Dict							      =     layer_norm_epsilon
					__snake_case					:      int							      =     initializer_range
					__snake_case					:      Optional[int]							      =     use_cache
					__snake_case					:      List[str]							      =     pretraining_tp
					__snake_case					:      int							      =     apply_residual_connection_post_layernorm
					__snake_case					:      str							      =     hidden_dropout
					__snake_case					:      Tuple							      =     attention_dropout

					__snake_case					:      int							      =     bos_token_id
					__snake_case					:      Any							      =     eos_token_id
					__snake_case					:      Union[str, Any]							      =     slow_but_exact

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





class  _A (					__lowercase		):
		lowercase__:   Optional[int]				     =   version.parse('''1.12'''		)

		def __init__(   self					:   Optional[int]  ,      __magic_name__					:   PretrainedConfig  ,      __magic_name__					:   str = "default"  ,      __magic_name__					:   List[PatchingSpec] = None  ,      __magic_name__					:   bool = False  ,      )					->    Optional[int]:


					"""simple docstring"""

					super().__init__(__magic_name__  ,      task=__magic_name__  ,      patching_specs=__magic_name__  ,      use_past=__magic_name__			)
					if not getattr(self._config  ,      """pad_token_id"""  ,      __magic_name__			):
								# TODO: how to do that better?
								__snake_case					:      str							      =     0

		@property
		def 	lowercase__     (   self					:   Tuple			)					->    Mapping[str, Mapping[int, str]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}			)
					if self.use_past:
								# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
								self.fill_with_past_key_values_(__magic_name__  ,      direction="""inputs"""  ,      inverted_values_shape=__magic_name__			)
								__snake_case					:      List[Any]							      =     {0: """batch""", 1: """past_sequence + sequence"""}
					else:
								__snake_case					:      Any							      =     {0: """batch""", 1: """sequence"""}

					return common_inputs

		@property
		def 	lowercase__     (   self					:   Optional[Any]			)					->    int:


					"""simple docstring"""

					return self._config.n_layer

		@property
		def 	lowercase__     (   self					:   Optional[Any]			)					->    int:


					"""simple docstring"""

					return self._config.n_head

		@property
		def 	lowercase__     (   self					:   Optional[int]			)					->    float:


					"""simple docstring"""

					return 1E-3

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   "PreTrainedTokenizer"  ,      __magic_name__					:   int = -1  ,      __magic_name__					:   int = -1  ,      __magic_name__					:   bool = False  ,      __magic_name__					:   Optional["TensorType"] = None  ,      )					->    Mapping[str, Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     super(__magic_name__  ,      self			).generate_dummy_inputs(
					    __magic_name__  ,      batch_size=__magic_name__  ,      seq_length=__magic_name__  ,      is_pair=__magic_name__  ,      framework=__magic_name__			)

					# We need to order the input in the way they appears in the forward()
					__snake_case					:      Optional[Any]							      =     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

											__snake_case      ,     __snake_case					:      str							      =     common_inputs["""input_ids"""].shape
											# Not using the same length for past_key_values
											__snake_case					:      Tuple							      =     seqlen + 2
											__snake_case					:      List[str]							      =     self._config.hidden_size // self.num_attention_heads
											__snake_case					:      Any							      =     (
											    batch * self.num_attention_heads,
											    head_dim,
											    past_key_values_length,
											)
											__snake_case					:      Optional[Any]							      =     (
											    batch * self.num_attention_heads,
											    past_key_values_length,
											    head_dim,
											)
											__snake_case					:      Dict							      =     [
											    (torch.zeros(__magic_name__			), torch.zeros(__magic_name__			)) for _ in range(self.num_layers			)
											]

					__snake_case					:      Union[str, Any]							      =     common_inputs["""attention_mask"""]
					if self.use_past:
								__snake_case					:      Any							      =     ordered_inputs["""attention_mask"""].dtype
								__snake_case					:      int							      =     torch.cat(
								    [ordered_inputs["""attention_mask"""], torch.ones(__magic_name__  ,      __magic_name__  ,      dtype=__magic_name__			)]  ,      dim=1			)

					return ordered_inputs

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    int:


					"""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)


