
"""simple docstring"""

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class      _UpperCAmelCase							:




      def __init__(							self   ,       lowercase_   ,       lowercase_=9_9   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=9   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=False   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_=8   ,       lowercase_=0.1   ,       lowercase_=0.0_0_2   ,       lowercase_=1   ,       lowercase_=0   ,       lowercase_=0   ,       lowercase_=None   ,       lowercase_=None   ,       )						->					Union[str, Any]:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					encoder_seq_length
          UpperCAmelCase									=					decoder_seq_length
          # For common tests
          UpperCAmelCase									=					self.decoder_seq_length
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_attention_mask
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					vocab_size
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					d_ff
          UpperCAmelCase									=					relative_attention_num_buckets
          UpperCAmelCase									=					dropout_rate
          UpperCAmelCase									=					initializer_factor
          UpperCAmelCase									=					eos_token_id
          UpperCAmelCase									=					pad_token_id
          UpperCAmelCase									=					decoder_start_token_id
          UpperCAmelCase									=					None
          UpperCAmelCase									=					decoder_layers




      def     a_      (							self							)						->					Optional[int]:
          return TaConfig.from_pretrained('google/umt5-base'							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       )						->					Union[str, Any]:
          if attention_mask is None:
              UpperCAmelCase									=					input_ids.ne(config.pad_token_id							)
          if decoder_attention_mask is None:
              UpperCAmelCase									=					decoder_input_ids.ne(config.pad_token_id							)
          if head_mask is None:
              UpperCAmelCase									=					torch.ones(config.num_hidden_layers   ,       config.num_attention_heads   ,       device=lowercase_							)
          if decoder_head_mask is None:
              UpperCAmelCase									=					torch.ones(config.num_decoder_layers   ,       config.num_attention_heads   ,       device=lowercase_							)
          if cross_attn_head_mask is None:
              UpperCAmelCase									=					torch.ones(
                  config.num_decoder_layers   ,       config.num_attention_heads   ,       device=lowercase_							)
          return {
              "input_ids": input_ids,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_mask,
              "decoder_attention_mask": decoder_attention_mask,
              "head_mask": head_mask,
              "decoder_head_mask": decoder_head_mask,
              "cross_attn_head_mask": cross_attn_head_mask,
          }




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.encoder_seq_length]   ,       self.vocab_size							)
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.decoder_seq_length]   ,       self.vocab_size							)

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for NllbMoe the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          UpperCAmelCase									=					input_ids.clamp(self.pad_token_id + 1							)
          UpperCAmelCase									=					decoder_input_ids.clamp(self.pad_token_id + 1							)

          UpperCAmelCase									=					self.get_config()
          UpperCAmelCase									=					config.num_attention_heads
          UpperCAmelCase									=					self.prepare_inputs_dict(lowercase_   ,       lowercase_   ,       lowercase_							)
          return config, input_dict




      def     a_      (							self							)						->					Any:
          UpperCAmelCase			,							UpperCAmelCase									=					self.prepare_config_and_inputs()
          return config, inputs_dict




      def     a_      (							self							)						->					Optional[int]:
          return TaConfig(
              vocab_size=1_6_6   ,       d_model=self.hidden_size   ,       d_ff=self.d_ff   ,       d_kv=self.hidden_size // self.num_attention_heads   ,       num_layers=self.num_hidden_layers   ,       num_decoder_layers=self.decoder_layers   ,       num_heads=self.num_attention_heads   ,       relative_attention_num_buckets=self.relative_attention_num_buckets   ,       dropout_rate=self.dropout_rate   ,       initializer_factor=self.initializer_factor   ,       eos_token_id=self.eos_token_id   ,       bos_token_id=self.pad_token_id   ,       pad_token_id=self.pad_token_id   ,       decoder_start_token_id=self.decoder_start_token_id   ,       )




      def     a_      (							self							)						->					Union[str, Any]:
          return TaConfig(
              vocab_size=self.vocab_size   ,       d_model=self.hidden_size   ,       d_ff=self.d_ff   ,       d_kv=self.hidden_size // self.num_attention_heads   ,       num_layers=self.num_hidden_layers   ,       num_decoder_layers=self.decoder_layers   ,       num_heads=self.num_attention_heads   ,       relative_attention_num_buckets=self.relative_attention_num_buckets   ,       dropout_rate=self.dropout_rate   ,       initializer_factor=self.initializer_factor   ,       eos_token_id=self.eos_token_id   ,       bos_token_id=self.pad_token_id   ,       pad_token_id=self.pad_token_id   ,       decoder_start_token_id=self.decoder_start_token_id   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					Tuple:
          UpperCAmelCase									=					UMTaModel(config=lowercase_							)
          model.to(lowercase_							)
          model.eval()
          UpperCAmelCase									=					model(
              input_ids=lowercase_   ,       decoder_input_ids=lowercase_   ,       attention_mask=lowercase_   ,       decoder_attention_mask=lowercase_   ,       )
          UpperCAmelCase									=					model(input_ids=lowercase_   ,       decoder_input_ids=lowercase_							)
          UpperCAmelCase									=					result.last_hidden_state
          UpperCAmelCase									=					result.past_key_values
          UpperCAmelCase									=					result.encoder_last_hidden_state

          self.parent.assertEqual(encoder_output.size()   ,       (self.batch_size, self.encoder_seq_length, self.hidden_size)							)
          self.parent.assertEqual(decoder_output.size()   ,       (self.batch_size, self.decoder_seq_length, self.hidden_size)							)
          # There should be `num_layers` key value embeddings stored in decoder_past
          self.parent.assertEqual(len(lowercase_							)   ,       config.num_layers							)
          # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
          self.parent.assertEqual(len(decoder_past[0]							)   ,       4							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					Tuple:
          UpperCAmelCase									=					UMTaModel(config=lowercase_							).get_decoder().to(lowercase_							).eval()
          # first forward pass
          UpperCAmelCase									=					model(lowercase_   ,       use_cache=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          UpperCAmelCase									=					model(lowercase_   ,       use_cache=lowercase_							)

          self.parent.assertTrue(len(lowercase_							) == len(lowercase_							)							)
          self.parent.assertTrue(len(lowercase_							) == len(lowercase_							) + 1							)

          UpperCAmelCase			,							UpperCAmelCase									=					outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          UpperCAmelCase									=					ids_tensor((self.batch_size, 1)   ,       config.vocab_size							)

          # append to next input_ids and
          UpperCAmelCase									=					torch.cat([input_ids, next_tokens]   ,       dim=-1							)

          UpperCAmelCase									=					model(lowercase_							)['last_hidden_state']
          UpperCAmelCase									=					model(lowercase_   ,       past_key_values=lowercase_							)['last_hidden_state']

          # select random slice
          UpperCAmelCase									=					ids_tensor((1,)   ,       output_from_past.shape[-1]							).item()
          UpperCAmelCase									=					output_from_no_past[:, -1, random_slice_idx].detach()
          UpperCAmelCase									=					output_from_past[:, 0, random_slice_idx].detach()

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(lowercase_   ,       lowercase_   ,       atol=1E-3							)							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       )						->					int:
          UpperCAmelCase									=					UMTaModel(config=lowercase_							).to(lowercase_							).half().eval()
          UpperCAmelCase									=					model(**lowercase_							)['last_hidden_state']
          self.parent.assertFalse(torch.isnan(lowercase_							).any().item()							)

@require_torch
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	(
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	(UMTaForConditionalGeneration,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	(
          {
              "conversational": UMTaForConditionalGeneration,
              "feature-extraction": UMTaModel,
              "summarization": UMTaForConditionalGeneration,
              "text2text-generation": UMTaForConditionalGeneration,
              "translation": UMTaForConditionalGeneration,
              "question-answering": UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	True
      __SCREAMING_SNAKE_CASE   :							str							      =	False
      __SCREAMING_SNAKE_CASE   :							str							      =	False
      __SCREAMING_SNAKE_CASE   :							Dict							      =	True
      __SCREAMING_SNAKE_CASE   :							Dict							      =	True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	[0.8, 0.9]




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					UMTaModelTester(self							)




      @unittest.skip('Test has a segmentation fault on torch 1.8.0'							)
      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          UpperCAmelCase									=					UMTaModel(config_and_inputs[0]							).to(lowercase_							)
          with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  lowercase_   ,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])   ,       F"{tmpdirname}/t5_test.onnx"   ,       export_params=lowercase_   ,       opset_version=9   ,       input_names=['input_ids', 'decoder_input_ids']   ,       )




      @unittest.skipIf(torch_device == 'cpu'   ,       'Cant do half precision'							)
      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*lowercase_							)




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					['encoder_attentions', 'decoder_attentions', 'cross_attentions']
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          UpperCAmelCase									=					config_and_inputs[0]
          UpperCAmelCase									=					UMTaForConditionalGeneration(lowercase_							).eval()
          model.to(lowercase_							)

          UpperCAmelCase									=					{
              'head_mask': torch.zeros(config.num_layers   ,       config.num_heads   ,       device=lowercase_							),
              'decoder_head_mask': torch.zeros(config.num_decoder_layers   ,       config.num_heads   ,       device=lowercase_							),
              'cross_attn_head_mask': torch.zeros(config.num_decoder_layers   ,       config.num_heads   ,       device=lowercase_							),
          }

          for attn_name, (name, mask) in zip(lowercase_   ,       head_masking.items()							):
              UpperCAmelCase									=					{name: mask}
              # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
              if name == "head_mask":
                  UpperCAmelCase									=					torch.ones(
                      config.num_decoder_layers   ,       config.num_heads   ,       device=lowercase_							)

              UpperCAmelCase									=					model.generate(
                  config_and_inputs[1]['input_ids']   ,       num_beams=1   ,       max_length=3   ,       output_attentions=lowercase_   ,       return_dict_in_generate=lowercase_   ,       **lowercase_   ,       )
              # We check the state of decoder_attentions and cross_attentions just from the last step
              UpperCAmelCase									=					out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
              self.assertEqual(sum([w.sum().item() for w in attn_weights]							)   ,       0.0							)




      @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'							)
      def     a_      (							self							)						->					List[str]:
          pass

@require_torch
@require_sentencepiece
@require_tokenizers
class      _UpperCAmelCase							(		unittest.TestCase							):




      @slow
      @unittest.skip(
          'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'							)
      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					UMTaForConditionalGeneration.from_pretrained('google/umt5-small'   ,       return_dict=lowercase_							).to(lowercase_							)
          UpperCAmelCase									=					AutoTokenizer.from_pretrained('google/umt5-small'   ,       use_fast=lowercase_   ,       legacy=lowercase_							)
          UpperCAmelCase									=					[
              'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
              'No se como puedo <extra_id_0>.',
              'This is the reason why we <extra_id_0> them.',
              'The <extra_id_0> walks in <extra_id_1>, seats',
              'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
          ]
          UpperCAmelCase									=					tokenizer(lowercase_   ,       return_tensors='pt'   ,       padding=lowercase_							).input_ids
          # fmt: off
          UpperCAmelCase									=					torch.tensor(
              [
                  [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
              ]							)
          # fmt: on
          torch.testing.assert_allclose(lowercase_   ,       lowercase_							)

          UpperCAmelCase									=					model.generate(input_ids.to(lowercase_							)							)
          UpperCAmelCase									=					[
              '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
              '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
              '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
              '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
              '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          ]
          UpperCAmelCase									=					tokenizer.batch_decode(lowercase_							)
          self.assertEqual(lowercase_   ,       lowercase_							)





"""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()
SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					int ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					DPTConfig()

    if "large" in checkpoint_url:
        UpperCAmelCase									=					1_024
        UpperCAmelCase									=					4_096
        UpperCAmelCase									=					24
        UpperCAmelCase									=					16
        UpperCAmelCase									=					[5, 11, 17, 23]
        UpperCAmelCase									=					[256, 512, 1_024, 1_024]
        UpperCAmelCase									=					(1, 384, 384)

    if "ade" in checkpoint_url:
        UpperCAmelCase									=					True

        UpperCAmelCase									=					150
        UpperCAmelCase									=					'huggingface/label-files'
        UpperCAmelCase									=					'ade20k-id2label.json'
        UpperCAmelCase									=					json.load(open(cached_download(hf_hub_url(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ) ,				'r' ) )
        UpperCAmelCase									=					{int(lowerCAmelCase ): v for k, v in idalabel.items()}
        UpperCAmelCase									=					idalabel
        UpperCAmelCase									=					{v: k for k, v in idalabel.items()}
        UpperCAmelCase									=					[1, 150, 480, 480]

    return config, expected_shape




def     lowercase__						(					lowerCAmelCase				:					str ) ->      Any:






    """simple docstring"""




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




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

    return name




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					int ) ->      int:






    """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)
        UpperCAmelCase									=					state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
        UpperCAmelCase									=					state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias" )
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase									=					in_proj_weight[: config.hidden_size, :]
        UpperCAmelCase									=					in_proj_bias[: config.hidden_size]
        UpperCAmelCase									=					in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase									=					in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCAmelCase									=					in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase									=					in_proj_bias[-config.hidden_size :]




def     lowercase__						(					) ->      Optional[Any]:






    """simple docstring"""




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




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Union[str, Any] ) ->      Union[str, Any]:






    """simple docstring"""




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

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

    # Check outputs on an image
    UpperCAmelCase									=					480 if 'ade' in checkpoint_url else 384
    UpperCAmelCase									=					DPTImageProcessor(size=lowerCAmelCase )

    UpperCAmelCase									=					prepare_img()
    UpperCAmelCase									=					image_processor(lowerCAmelCase ,				return_tensors='pt' )

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

    # Assert logits
    UpperCAmelCase									=					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:
        UpperCAmelCase									=					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(lowerCAmelCase )
    assert (
        torch.allclose(outputs[0, 0, :3, :3] ,				lowerCAmelCase ,				atol=1E-4 )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3] ,				lowerCAmelCase )
    )

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    					=						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.''',
   )

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



