






'''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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  DPTConfig(embedding_type="hybrid"				)

   if "large" in checkpoint_url:
      lowercase_						:     str							     =  1024
      lowercase_						:     List[str]							     =  4096
      lowercase_						:     Optional[int]							     =  24
      lowercase_						:     List[Any]							     =  16
      lowercase_						:     Union[str, Any]							     =  [5, 11, 17, 23]
      lowercase_						:     List[Any]							     =  [256, 512, 1024, 1024]
      lowercase_						:     Dict							     =  (1, 384, 384)

   if "nyu" or "midas" in checkpoint_url:
      lowercase_						:     Any							     =  768
      lowercase_						:     Tuple							     =  [1, 1, 1, 0.5]
      lowercase_						:     Optional[Any]							     =  [256, 512, 768, 768]
      lowercase_						:     int							     =  150
      lowercase_						:     List[Any]							     =  16
      lowercase_						:     str							     =  (1, 384, 384)
      lowercase_						:     Tuple							     =  False
      lowercase_						:     List[str]							     =  "project"

   if "ade" in checkpoint_url:
      lowercase_						:     Optional[Any]							     =  True
      lowercase_						:     List[Any]							     =  768
      lowercase_						:     List[str]							     =  [1, 1, 1, 0.5]
      lowercase_						:     Dict							     =  150
      lowercase_						:     int							     =  16
      lowercase_						:     Tuple							     =  "huggingface/label-files"
      lowercase_						:     str							     =  "ade20k-id2label.json"
      lowercase_						:     Union[str, Any]							     =  json.load(open(cached_download(hf_hub_url(_UpperCamelCase    ,		_UpperCamelCase    ,		repo_type="dataset"				)				)    ,		"r"				)				)
      lowercase_						:     Dict							     =  {int(_UpperCamelCase				): v for k, v in idalabel.items()}
      lowercase_						:     Any							     =  idalabel
      lowercase_						:     Any							     =  {v: k for k, v in idalabel.items()}
      lowercase_						:     Any							     =  [1, 150, 480, 480]

   return config, expected_shape




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
   for k in ignore_keys:
      state_dict.pop(_UpperCamelCase    ,		_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


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

   if ".." in name:
      lowercase_						:     Tuple							     =  name.replace(".."    ,		"."				)

   if "stem.conv" in name:
      lowercase_						:     List[Any]							     =  name.replace("stem.conv"    ,		"bit.embedder.convolution"				)
   if "blocks" in name:
      lowercase_						:     str							     =  name.replace("blocks"    ,		"layers"				)
   if "convolution" in name and "backbone" in name:
      lowercase_						:     List[Any]							     =  name.replace("convolution"    ,		"conv"				)
   if "layer" in name and "backbone" in name:
      lowercase_						:     Optional[Any]							     =  name.replace("layer"    ,		"layers"				)
   if "backbone.bit.encoder.bit" in name:
      lowercase_						:     Any							     =  name.replace("backbone.bit.encoder.bit"    ,		"backbone.bit"				)
   if "embedder.conv" in name:
      lowercase_						:     Tuple							     =  name.replace("embedder.conv"    ,		"embedder.convolution"				)
   if "backbone.bit.encoder.stem.norm" in name:
      lowercase_						:     List[Any]							     =  name.replace("backbone.bit.encoder.stem.norm"    ,		"backbone.bit.embedder.norm"				)
   return name




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """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_						:     Dict							     =  state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""				)
      lowercase_						:     List[Any]							     =  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_						:     int							     =  in_proj_weight[: config.hidden_size, :]
      lowercase_						:     Any							     =  in_proj_bias[: config.hidden_size]
      lowercase_						:     Optional[int]							     =  in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowercase_						:     List[Any]							     =  in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      lowercase_						:     Union[str, Any]							     =  in_proj_weight[
          -config.hidden_size :, :
      ]
      lowercase_						:     int							     =  in_proj_bias[-config.hidden_size :]




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Tuple							     =  "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_						:     Union[str, Any]							     =  Image.open(requests.get(_UpperCamelCase    ,		stream=_UpperCamelCase				).raw				)
   return im




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_     ,		lowercase_						:     Optional[Any]							     =  get_dpt_config(_UpperCamelCase				)
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)
   # remove certain keys
   remove_ignore_keys_(_UpperCamelCase				)
   # rename keys
   for key in state_dict.copy().keys():
      lowercase_						:     int							     =  state_dict.pop(_UpperCamelCase				)
      lowercase_						:     Union[str, Any]							     =  val
   # read in qkv matrices
   read_in_q_k_v(_UpperCamelCase    ,		_UpperCamelCase				)

   # load HuggingFace model
   lowercase_						:     Any							     =  DPTForSemanticSegmentation(_UpperCamelCase				) if "ade" in checkpoint_url else DPTForDepthEstimation(_UpperCamelCase				)
   model.load_state_dict(_UpperCamelCase				)
   model.eval()

   # Check outputs on an image
   lowercase_						:     str							     =  480 if "ade" in checkpoint_url else 384
   lowercase_						:     List[Any]							     =  DPTImageProcessor(size=_UpperCamelCase				)

   lowercase_						:     int							     =  prepare_img()
   lowercase_						:     int							     =  image_processor(_UpperCamelCase    ,		return_tensors="pt"				)

   # forward pass
   lowercase_						:     List[Any]							     =  model(**_UpperCamelCase				).logits if "ade" in checkpoint_url else model(**_UpperCamelCase				).predicted_depth

   if show_prediction:
      lowercase_						:     int							     =  (
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1				)    ,		size=(image.size[1], image.size[0])    ,		mode="bicubic"    ,		align_corners=_UpperCamelCase    ,		)
          .squeeze()
          .cpu()
          .numpy()
      )

      Image.fromarray((prediction / prediction.max()) * 255				).show()

   if pytorch_dump_folder_path is not None:
      Path(_UpperCamelCase				).mkdir(exist_ok=_UpperCamelCase				)
      print(F"""Saving model to {pytorch_dump_folder_path}"""				)
      model.save_pretrained(_UpperCamelCase				)
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
      image_processor.save_pretrained(_UpperCamelCase				)

   if push_to_hub:
      model.push_to_hub("ybelkada/dpt-hybrid-midas"				)
      image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"				)


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=False,
     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.',
 )
 parser.add_argument(
     '--show_prediction',
     action='store_true',
 )

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












'''simple docstring'''
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor


class 							_UpperCAmelCase    :




    def __init__(      self	:							int	,  a	:							Dict	,  a	:							Optional[Any]=1_3	,  a	:							str=3_0	,  a	:							Dict=2	,  a	:							List[Any]=3	,  a	:							Optional[int]=True	,  a	:							Tuple=True	,  a	:							List[Any]=3_2	,  a	:							int=2	,  a	:							List[str]=4	,  a	:							Dict=3_7	,  a	:							Optional[Any]="gelu"	,  a	:							List[str]=0.1	,  a	:							Dict=0.1	,  a	:							List[Any]=1_0	,  a	:							Union[str, Any]=0.02	,  a	:							Union[str, Any]=3	,  a	:							int=None	,  a	:							Any=2	,  ):






       '''simple docstring'''






       lowercase_						:     Any							     =  parent
       lowercase_						:     Union[str, Any]							     =  batch_size
       lowercase_						:     Any							     =  image_size
       lowercase_						:     str							     =  patch_size
       lowercase_						:     int							     =  num_channels
       lowercase_						:     str							     =  is_training
       lowercase_						:     str							     =  use_labels
       lowercase_						:     List[Any]							     =  hidden_size
       lowercase_						:     List[str]							     =  num_hidden_layers
       lowercase_						:     Dict							     =  num_attention_heads
       lowercase_						:     Optional[int]							     =  intermediate_size
       lowercase_						:     str							     =  hidden_act
       lowercase_						:     Union[str, Any]							     =  hidden_dropout_prob
       lowercase_						:     List[str]							     =  attention_probs_dropout_prob
       lowercase_						:     List[str]							     =  type_sequence_label_size
       lowercase_						:     List[Any]							     =  initializer_range
       lowercase_						:     str							     =  scope
       lowercase_						:     Tuple							     =  encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       lowercase_						:     Any							     =  (image_size // patch_size) ** 2
       lowercase_						:     str							     =  num_patches + 2




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       lowercase_						:     List[Any]							     =  None
       if self.use_labels:
          lowercase_						:     int							     =  ids_tensor([self.batch_size]	,  self.type_sequence_label_size      )

       lowercase_						:     Optional[Any]							     =  self.get_config()

       return config, pixel_values, labels




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       return DeiTConfig(
           image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  is_decoder=a	,  initializer_range=self.initializer_range	,  encoder_stride=self.encoder_stride	,  )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							List[str]	,  a	:							str	,  a	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTModel(config=a      )
       lowercase_						:     int							     =  model(a      )
       self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)      )




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							Optional[int]	,  a	:							List[Any]	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTForMaskedImageModeling(config=a      )
       lowercase_						:     Union[str, Any]							     =  model(a      )
       self.parent.assertEqual(
           result.reconstruction.shape	,  (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       lowercase_						:     Dict							     =  1
       lowercase_						:     Tuple							     =  TFDeiTForMaskedImageModeling(a      )

       lowercase_						:     Tuple							     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowercase_						:     Dict							     =  model(a      )
       self.parent.assertEqual(result.reconstruction.shape	,  (self.batch_size, 1, self.image_size, self.image_size)      )




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[str]	,  a	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     str							     =  self.type_sequence_label_size
       lowercase_						:     Union[str, Any]							     =  TFDeiTForImageClassification(a      )
       lowercase_						:     int							     =  model(a	,  labels=a      )
       self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       lowercase_						:     Dict							     =  1
       lowercase_						:     str							     =  TFDeiTForImageClassification(a      )

       lowercase_						:     Tuple							     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowercase_						:     Any							     =  model(a	,  labels=a      )
       self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)      )





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  self.prepare_config_and_inputs()
       lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  config_and_inputs
       lowercase_						:     Optional[Any]							     =  {"pixel_values": pixel_values}
       return config, inputs_dict


@require_tf
class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Tuple									=     (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase: Tuple									=     (
        {
            'feature-extraction': TFDeiTModel,
            'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    __lowerCamelCase: Union[str, Any]									=     False
    __lowerCamelCase: Any									=     False
    __lowerCamelCase: List[str]									=     False
    __lowerCamelCase: Optional[Any]									=     False




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     int							     =  TFDeiTModelTester(self      )
       lowercase_						:     Any							     =  ConfigTester(self	,  config_class=a	,  has_text_modality=a	,  hidden_size=3_7      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    @unittest.skip(reason="DeiT does not use inputs_embeds"      )
    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Tuple							     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase_						:     Dict							     =  model_class(a      )
          self.assertIsInstance(model.get_input_embeddings()	,  (tf.keras.layers.Layer)      )
          lowercase_						:     Optional[int]							     =  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(a	,  tf.keras.layers.Dense      )      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Union[str, Any]							     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase_						:     Any							     =  model_class(a      )
          lowercase_						:     str							     =  inspect.signature(model.call      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase_						:     int							     =  [*signature.parameters.keys()]

          lowercase_						:     Dict							     =  ["pixel_values"]
          self.assertListEqual(arg_names[:1]	,  a      )




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a      )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							int	,  a	:							Union[str, Any]	,  a	:							Any=False      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  super()._prepare_for_class(a	,  a	,  return_labels=a      )

       if return_labels:
          if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
             del inputs_dict["labels"]

       return inputs_dict





    @slow
    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase_						:     List[Any]							     =  TFDeiTModel.from_pretrained(a      )
          self.assertIsNotNone(a      )




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     str							     =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
   return image





@require_tf
@require_vision
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    @cached_property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return (
           DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
           if is_vision_available()
           else None
       )





    @slow
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

       lowercase_						:     Union[str, Any]							     =  self.default_image_processor
       lowercase_						:     Tuple							     =  prepare_img()
       lowercase_						:     Optional[Any]							     =  image_processor(images=a	,  return_tensors="tf"      )

       # forward pass
       lowercase_						:     int							     =  model(**a      )

       # verify the logits
       lowercase_						:     Union[str, Any]							     =  tf.TensorShape((1, 1_0_0_0)      )
       self.assertEqual(outputs.logits.shape	,  a      )

       lowercase_						:     Optional[Any]							     =  tf.constant([-1.0266, 0.1912, -1.2861]      )

       self.assertTrue(np.allclose(outputs.logits[0, :3]	,  a	,  atol=1e-4      )      )




