"""simple docstring"""
import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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





def  lowerCAmelCase__     (      _UpperCamelCase							:					List[Any]       )       ->					List[Any]:





						"""simple docstring"""






						snake_case             =      tf.image.resize(_UpperCamelCase			,      (3_4_6, 3_4_6)       )
						snake_case             =      tf.image.crop_to_bounding_box(_UpperCamelCase			,      (3_4_6 - 2_8_9) // 2			,      (3_4_6 - 2_8_9) // 2			,      2_8_9			,      2_8_9       )
						return image





def  lowerCAmelCase__     (      )       ->					Union[str, Any]:





						"""simple docstring"""






						snake_case             =      EfficientNetConfig.from_pretrained('google/efficientnet-b7'       )
						snake_case             =      2_8_9
						snake_case             =      6_4_0
						snake_case             =      {'0': 'LABEL_0', '1': 'LABEL_1'}
						snake_case             =      {'LABEL_0': 0, 'LABEL_1': 1}
						snake_case             =      []

						snake_case             =      BertConfig()
						snake_case             =      AlignConfig.from_text_vision_configs(
						    text_config=_UpperCamelCase			,      vision_config=_UpperCamelCase			,      projection_dim=6_4_0       )
						return config





def  lowerCAmelCase__     (      )       ->					Optional[Any]:





						"""simple docstring"""






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





def  lowerCAmelCase__     (      )       ->					Optional[Any]:





						"""simple docstring"""






						snake_case             =      EfficientNetImageProcessor(
						    do_center_crop=_UpperCamelCase			,      rescale_factor=1 / 1_27.5			,      rescale_offset=_UpperCamelCase			,      do_normalize=_UpperCamelCase			,      include_top=_UpperCamelCase			,      resample=Image.BILINEAR			,      )
						snake_case             =      BertTokenizer.from_pretrained('bert-base-uncased'       )
						snake_case             =      6_4
						snake_case             =      AlignProcessor(image_processor=_UpperCamelCase			,      tokenizer=_UpperCamelCase       )
						return processor





def  lowerCAmelCase__     (      _UpperCamelCase							:					Tuple       )       ->					Tuple:





						"""simple docstring"""






						snake_case             =      [v.split('_'       )[0].split('block'       )[1] for v in original_param_names if v.startswith('block'       )]
						snake_case             =      list(set(_UpperCamelCase       )       )
						snake_case             =      sorted(_UpperCamelCase       )
						snake_case             =      len(_UpperCamelCase       )
						snake_case             =      {b: str(_UpperCamelCase       ) for b, i in zip(_UpperCamelCase			,      range(_UpperCamelCase       )       )}

						snake_case             =      []
						rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')       )
						rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')       )
						rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')       )
						rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')       )
						rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')       )

						for b in block_names:
												snake_case             =      block_name_mapping[b]
												rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")       )
												rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")       )
												rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")       )
												rename_keys.append(
												    (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")       )
												rename_keys.append(
												    (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")       )
												rename_keys.append(
												    (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")       )
												rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")       )
												rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")       )
												rename_keys.append(
												    (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")       )
												rename_keys.append(
												    (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")       )

												rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")       )
												rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")       )
												rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")       )
												rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")       )
												rename_keys.append(
												    (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")       )
												rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")       )
												rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")       )
												rename_keys.append(
												    (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")       )
												rename_keys.append(
												    (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")       )

						snake_case             =      {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		snake_case             =      'vision_model.' + item[1]

    # BERT text encoder
						snake_case             =      []
						snake_case             =      'tf_bert_model/bert'
						snake_case             =      'text_model'
						for i in range(1_2       ):
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.query.weight""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.query.bias""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.key.weight""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.key.bias""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.value.weight""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.value.bias""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
												        f"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
												    )       )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
												        f"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
												    )       )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", f"""{new}.encoder.layer.{i}.output.dense.weight""")       )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/dense/bias:0""", f"""{new}.encoder.layer.{i}.output.dense.bias""")       )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")       )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")       )

						rename_keys.append((f"""{old}/embeddings/word_embeddings/weight:0""", f"""{new}.embeddings.word_embeddings.weight""")       )
						rename_keys.append(
						    (f"""{old}/embeddings/position_embeddings/embeddings:0""", f"""{new}.embeddings.position_embeddings.weight""")       )
						rename_keys.append(
						    (f"""{old}/embeddings/token_type_embeddings/embeddings:0""", f"""{new}.embeddings.token_type_embeddings.weight""")       )
						rename_keys.append((f"""{old}/embeddings/LayerNorm/gamma:0""", f"""{new}.embeddings.LayerNorm.weight""")       )
						rename_keys.append((f"""{old}/embeddings/LayerNorm/beta:0""", f"""{new}.embeddings.LayerNorm.bias""")       )

						rename_keys.append((f"""{old}/pooler/dense/kernel:0""", f"""{new}.pooler.dense.weight""")       )
						rename_keys.append((f"""{old}/pooler/dense/bias:0""", f"""{new}.pooler.dense.bias""")       )
						rename_keys.append(('dense/kernel:0', 'text_projection.weight')       )
						rename_keys.append(('dense/bias:0', 'text_projection.bias')       )
						rename_keys.append(('dense/bias:0', 'text_projection.bias')       )
						rename_keys.append(('temperature:0', 'temperature')       )

						for item in rename_keys:
												if item[0] in original_param_names:
																		snake_case             =      item[1]
						return key_mapping





def  lowerCAmelCase__     (      _UpperCamelCase							:					Dict			,      _UpperCamelCase							:					List[Any]			,      _UpperCamelCase							:					List[Any]       )       ->					int:





						"""simple docstring"""






						list(hf_params.keys()       )

						for key, value in tf_params.items():
												if key not in key_mapping:
																		continue

												snake_case             =      key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		snake_case             =      torch.from_numpy(_UpperCamelCase       ).permute(3			,      2			,      0			,      1       )
												elif "embeddings" in key:
																		snake_case             =      torch.from_numpy(_UpperCamelCase       )
												elif "depthwise_kernel" in key:
																		snake_case             =      torch.from_numpy(_UpperCamelCase       ).permute(2			,      3			,      0			,      1       )
												elif "kernel" in key:
																		snake_case             =      torch.from_numpy(np.transpose(_UpperCamelCase       )       )
												elif "temperature" in key:
																		snake_case             =      value
												elif "bn/gamma" or "bn/beta" in key:
																		snake_case             =      torch.from_numpy(np.transpose(_UpperCamelCase       )       ).squeeze()
												else:
																		snake_case             =      torch.from_numpy(_UpperCamelCase       )

												# Replace HF parameters with original TF model parameters
												hf_params[hf_key].copy_(_UpperCamelCase       )





@torch.no_grad()
def  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					Any			,      _UpperCamelCase							:					str			,      _UpperCamelCase							:					List[Any]       )       ->					int:





						"""simple docstring"""






						snake_case             =      6_4
						snake_case             =      Tokenizer(_UpperCamelCase       )
						snake_case             =      align.Align('efficientnet-b7'			,      'bert-base'			,      6_4_0			,      _UpperCamelCase			,      tok.get_vocab_size()       )
						original_model.compile()
						original_model.load_weights(_UpperCamelCase       )

						snake_case             =      original_model.trainable_variables
						snake_case             =      original_model.non_trainable_variables
						snake_case             =      {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												snake_case             =      param.numpy()
						snake_case             =      list(tf_params.keys()       )

						# Load HuggingFace model
						snake_case             =      get_align_config()
						snake_case             =      AlignModel(_UpperCamelCase       ).eval()
						snake_case             =      hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print('Converting parameters...'       )
						snake_case             =      rename_keys(_UpperCamelCase       )
						replace_params(_UpperCamelCase			,      _UpperCamelCase			,      _UpperCamelCase       )

						# Initialize processor
						snake_case             =      get_processor()
						snake_case             =      processor(
						    images=prepare_img()			,      text='A picture of a cat'			,      padding='max_length'			,      max_length=6_4			,      return_tensors='pt'       )

						# HF model inference
						hf_model.eval()
						with torch.no_grad():
												snake_case             =      hf_model(**_UpperCamelCase       )

						snake_case             =      outputs.image_embeds.detach().numpy()
						snake_case             =      outputs.text_embeds.detach().numpy()

						# Original model inference
						snake_case             =      False
						snake_case             =      EfficientNetImageProcessor(
						    do_center_crop=_UpperCamelCase			,      do_rescale=_UpperCamelCase			,      do_normalize=_UpperCamelCase			,      include_top=_UpperCamelCase			,      resample=Image.BILINEAR			,      )
						snake_case             =      tf_image_processor(images=prepare_img()			,      return_tensors='tf'			,      data_format='channels_last'       )['pixel_values']
						snake_case             =      tok(tf.constant(['A picture of a cat']       )       )

						snake_case             =      original_model.image_encoder(_UpperCamelCase			,      training=_UpperCamelCase       )
						snake_case             =      original_model.text_encoder(_UpperCamelCase			,      training=_UpperCamelCase       )

						snake_case             =      tf.nn.la_normalize(_UpperCamelCase			,      axis=-1       )
						snake_case             =      tf.nn.la_normalize(_UpperCamelCase			,      axis=-1       )

						# Check whether original and HF model outputs match  -> np.allclose
						if not np.allclose(_UpperCamelCase			,      _UpperCamelCase			,      atol=1e-3       ):
												raise ValueError('The predicted image features are not the same.'       )
						if not np.allclose(_UpperCamelCase			,      _UpperCamelCase			,      atol=1e-3       ):
												raise ValueError('The predicted text features are not the same.'       )
						print('Model outputs match!'       )

						if save_model:
												# Create folder to save model
												if not os.path.isdir(_UpperCamelCase       ):
																		os.mkdir(_UpperCamelCase       )
												# Save converted model and image processor
												hf_model.save_pretrained(_UpperCamelCase       )
												processor.save_pretrained(_UpperCamelCase       )

						if push_to_hub:
												# Push model and image processor to hub
												print('Pushing converted ALIGN to the hub...'       )
												processor.push_to_hub('align-base'       )
												hf_model.push_to_hub('align-base'       )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__       =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_path",
					    default="./weights/model-weights",
					    type=str,
					    help="Path to the pretrained TF ALIGN checkpoint.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default="hf_model",
					    type=str,
					    help="Path to the output PyTorch model directory.",
					)
					parser.add_argument("--save_model", action="store_true", help="Save model to local")
					parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

					SCREAMING_SNAKE_CASE__       =	parser.parse_args()
					convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



"""simple docstring"""
import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
					from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DPTImageProcessor



class 	lowerCAmelCase_     :



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=2		,     lowerCAmelCase=32		,     lowerCAmelCase=16		,     lowerCAmelCase=3		,     lowerCAmelCase=True		,     lowerCAmelCase=True		,     lowerCAmelCase=32		,     lowerCAmelCase=4		,     lowerCAmelCase=[0, 1, 2, 3]		,     lowerCAmelCase=4		,     lowerCAmelCase=37		,     lowerCAmelCase="gelu"		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.02		,     lowerCAmelCase=3		,     lowerCAmelCase=False		,     lowerCAmelCase=None		,     ):






										"""simple docstring"""

										snake_case             =      parent
										snake_case             =      batch_size
										snake_case             =      image_size
										snake_case             =      patch_size
										snake_case             =      num_channels
										snake_case             =      is_training
										snake_case             =      use_labels
										snake_case             =      hidden_size
										snake_case             =      num_hidden_layers
										snake_case             =      backbone_out_indices
										snake_case             =      num_attention_heads
										snake_case             =      intermediate_size
										snake_case             =      hidden_act
										snake_case             =      hidden_dropout_prob
										snake_case             =      attention_probs_dropout_prob
										snake_case             =      initializer_range
										snake_case             =      num_labels
										snake_case             =      scope
										snake_case             =      is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										snake_case             =      (image_size // patch_size) ** 2
										snake_case             =      num_patches + 1

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

										snake_case             =      None
										if self.use_labels:
																snake_case             =      ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels     )

										snake_case             =      self.get_config()

										return config, pixel_values, labels

				def 		snake_case			(					self     ):






										"""simple docstring"""

										return DPTConfig(
										    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		,     backbone_out_indices=self.backbone_out_indices		,     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=lowerCAmelCase		,     initializer_range=self.initializer_range		,     is_hybrid=self.is_hybrid		,     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      DPTModel(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )
										self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self.num_labels
										snake_case             =      DPTForDepthEstimation(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )
										self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size)     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self.num_labels
										snake_case             =      DPTForSemanticSegmentation(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase		,     labels=lowerCAmelCase     )
										self.parent.assertEqual(
										    result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size, self.image_size)     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.prepare_config_and_inputs()
										snake_case       ,snake_case       ,snake_case             =      config_and_inputs
										snake_case             =      {'pixel_values': pixel_values}
										return config, inputs_dict





@require_torch
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     int      	=    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				_lowerCAmelCase  :     List[str]      	=    (
				    {
				        """depth-estimation""": DPTForDepthEstimation,
				        """feature-extraction""": DPTModel,
				        """image-segmentation""": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				_lowerCAmelCase  :     str      	=    False
				_lowerCAmelCase  :     Any      	=    False
				_lowerCAmelCase  :     Dict      	=    False

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTModelTester(self     )
										snake_case             =      ConfigTester(self		,     config_class=lowerCAmelCase		,     has_text_modality=lowerCAmelCase		,     hidden_size=37     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				@unittest.skip(reason='DPT does not use inputs_embeds'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case             =      model_class(lowerCAmelCase     )
																self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)     )
																snake_case             =      model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase		,     nn.Linear     )     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case             =      model_class(lowerCAmelCase     )
																snake_case             =      inspect.signature(model.forward     )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																snake_case             =      [*signature.parameters.keys()]

																snake_case             =      ['pixel_values']
																self.assertListEqual(arg_names[:1]		,     lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()
																snake_case             =      True

																if model_class in get_values(lowerCAmelCase     ):
																						continue

																snake_case             =      model_class(lowerCAmelCase     )
																model.to(lowerCAmelCase     )
																model.train()
																snake_case             =      self._prepare_for_class(lowerCAmelCase		,     lowerCAmelCase		,     return_labels=lowerCAmelCase     )
																snake_case             =      model(**lowerCAmelCase     ).loss
																loss.backward()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()
																snake_case             =      False
																snake_case             =      True

																if model_class in get_values(lowerCAmelCase     ) or not model_class.supports_gradient_checkpointing:
																						continue
																snake_case             =      model_class(lowerCAmelCase     )
																model.to(lowerCAmelCase     )
																model.gradient_checkpointing_enable()
																model.train()
																snake_case             =      self._prepare_for_class(lowerCAmelCase		,     lowerCAmelCase		,     return_labels=lowerCAmelCase     )
																snake_case             =      model(**lowerCAmelCase     ).loss
																loss.backward()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										snake_case             =      _config_zero_init(lowerCAmelCase     )
										for model_class in self.all_model_classes:
																snake_case             =      model_class(config=lowerCAmelCase     )
																# Skip the check for the backbone
																snake_case             =      []
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																												snake_case             =      [F"""{name}.{key}""" for key in module.state_dict().keys()]
																												break

																for name, param in model.named_parameters():
																						if param.requires_grad:
																												if name in backbone_params:
																																		continue
																												self.assertIn(
																												    ((param.data.mean() * 1E9).round() / 1E9).item()		,     [0.0, 1.0]		,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )

				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case             =      DPTModel.from_pretrained(lowerCAmelCase     )
																self.assertIsNotNone(lowerCAmelCase     )





def  lowerCAmelCase__     (      )       ->					Union[str, Any]:





						"""simple docstring"""






						snake_case             =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
						return image



@require_torch
@require_vision
@slow
class 	lowerCAmelCase_     (      unittest.TestCase       ):



				"""simple docstring"""

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large'     )
										snake_case             =      DPTForDepthEstimation.from_pretrained('Intel/dpt-large'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )
																snake_case             =      outputs.predicted_depth

										# verify the predicted depth
										snake_case             =      torch.Size((1, 3_84, 3_84)     )
										self.assertEqual(predicted_depth.shape		,     lowerCAmelCase     )

										snake_case             =      torch.tensor(
										    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]     ).to(lowerCAmelCase     )

										self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]		,     lowerCAmelCase		,     atol=1E-4     )     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
										snake_case             =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )

										# verify the logits
										snake_case             =      torch.Size((1, 1_50, 4_80, 4_80)     )
										self.assertEqual(outputs.logits.shape		,     lowerCAmelCase     )

										snake_case             =      torch.tensor(
										    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]     ).to(lowerCAmelCase     )

										self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		,     lowerCAmelCase		,     atol=1E-4     )     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
										snake_case             =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )

										snake_case             =      outputs.logits.detach().cpu()

										snake_case             =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase		,     target_sizes=[(5_00, 3_00)]     )
										snake_case             =      torch.Size((5_00, 3_00)     )
										self.assertEqual(segmentation[0].shape		,     lowerCAmelCase     )

										snake_case             =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase     )
										snake_case             =      torch.Size((4_80, 4_80)     )
										self.assertEqual(segmentation[0].shape		,     lowerCAmelCase     )


