



'''simple docstring'''





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


_UpperCamelCase											=						logging.get_logger(__name__)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=False       )      ->      Tuple:
  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
        ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
        ' instructions.'       )
    raise

  if not is_sharded:
    __lowerCamelCase       :       str           = os.path.abspath(_lowerCAmelCase       )
    logger.info(F'Loading PyTorch weights from {pt_path}'       )

    __lowerCamelCase       :       Optional[Any]           = torch.load(_lowerCAmelCase       ,map_location='cpu'       )
    logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters.'       )

    __lowerCamelCase       :       Tuple           = convert_pytorch_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    __lowerCamelCase       :       Union[str, Any]           = convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  return flax_state_dict

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      (Tuple[str], np.ndarray):

  def is_key_or_prefix_key_in_dict(_lowerCAmelCase       ) -> bool:
    return len(set(_lowerCAmelCase       ) & {key, (model_prefix,) + key}       ) > 0

  # layer norm
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('scale',)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  __lowerCamelCase       :       int           = pt_tuple_key[:-1] + ('mean',)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('var',)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('embedding',)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[Any]           = pt_tensor.transpose(2       ,3       ,1       ,0       )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  __lowerCamelCase       :       Optional[Any]           = pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    __lowerCamelCase       :       Dict           = pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  __lowerCamelCase       :       int           = pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  __lowerCamelCase       :       Optional[int]           = None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    __lowerCamelCase       :       Union[str, Any]           = pt_tuple_key[-2] + '_g'
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    __lowerCamelCase       :       Tuple           = pt_tuple_key[-2] + '_v'
  if name is not None:
    __lowerCamelCase       :       Any           = pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  # convert pytorch tensor to numpy
  __lowerCamelCase       :       Optional[int]           = {k: v.numpy() for k, v in pt_state_dict.items()}

  __lowerCamelCase       :       str           = flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    __lowerCamelCase       :       List[Any]           = flax_model.params['params']
  else:
    __lowerCamelCase       :       List[str]           = flax_model.params
  __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    __lowerCamelCase       :       Optional[int]           = flatten_dict(flax_model.params['batch_stats']       )
    random_flax_state_dict.update(_lowerCAmelCase       )

  __lowerCamelCase       :       str           = {}

  __lowerCamelCase       :       Union[str, Any]           = (model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
  )
  __lowerCamelCase       :       List[str]           = (model_prefix in flax_model_params) and (
      model_prefix not in {k.split('.'       )[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    __lowerCamelCase       :       Any           = tuple(pt_key.split('.'       )       )

    # remove base model prefix if necessary
    __lowerCamelCase       :       Any           = pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      __lowerCamelCase       :       Optional[Any]           = pt_tuple_key[1:]

    # Correctly rename weight parameters
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = rename_key_and_reshape_tensor(
        _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )

    # add model prefix if necessary
    __lowerCamelCase       :       Dict           = (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Dict           = (model_prefix,) + flax_key

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
            F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        __lowerCamelCase       :       List[Any]           = jnp.asarray(_lowerCAmelCase       )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
        continue

      # also add unexpected weight so that warning is thrown
      __lowerCamelCase       :       List[Any]           = jnp.asarray(_lowerCAmelCase       )

    else:
      # also add unexpected weight so that warning is thrown
      __lowerCamelCase       :       str           = jnp.asarray(_lowerCAmelCase       )

  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Any:
  import torch

  # Load the index
  __lowerCamelCase       :       Optional[int]           = {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    __lowerCamelCase       :       Dict           = torch.load(_lowerCAmelCase       )
    __lowerCamelCase       :       Dict           = {k: v.numpy() for k, v in pt_state_dict.items()}

    __lowerCamelCase       :       Optional[Any]           = flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      __lowerCamelCase       :       str           = flax_model.params['params']

      __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )
      random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']       )       )
    else:
      __lowerCamelCase       :       Dict           = flax_model.params
      __lowerCamelCase       :       Optional[int]           = flatten_dict(_lowerCAmelCase       )

    __lowerCamelCase       :       List[Any]           = (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
    )
    __lowerCamelCase       :       List[str]           = (model_prefix in flax_model_params) and (
        model_prefix not in {k.split('.'       )[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      __lowerCamelCase       :       Optional[Any]           = tuple(pt_key.split('.'       )       )

      # remove base model prefix if necessary
      __lowerCamelCase       :       List[Any]           = pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        __lowerCamelCase       :       Optional[int]           = pt_tuple_key[1:]

      # Correctly rename weight parameters
      __lowerCamelCase       ,__lowerCamelCase       :       Any           = rename_key_and_reshape_tensor(
          _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
      # add model prefix if necessary
      __lowerCamelCase       :       Tuple           = (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        __lowerCamelCase       :       Optional[Any]           = (model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
          raise ValueError(
              F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
              F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          __lowerCamelCase       :       int           = jnp.asarray(_lowerCAmelCase       )
          continue
        if "var" in flax_key[-1]:
          __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
          continue

        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Optional[int]           = jnp.asarray(_lowerCAmelCase       )

      else:
        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Union[str, Any]:
  __lowerCamelCase       :       Union[str, Any]           = os.path.abspath(_lowerCAmelCase       )
  logger.info(F'Loading Flax weights from {flax_checkpoint_path}'       )

  # import correct flax class
  __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,'Flax' + model.__class__.__name__       )

  # load flax weight dict
  with open(_lowerCAmelCase       ,'rb'       ) as state_f:
    try:
      __lowerCamelCase       :       Tuple           = from_bytes(_lowerCAmelCase       ,state_f.read()       )
    except UnpicklingError:
      raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '       )

  return load_flax_weights_in_pytorch_model(_lowerCAmelCase       ,_lowerCAmelCase       )


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
        ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
        ' instructions.'       )
    raise

  # check if we have bf16 weights
  __lowerCamelCase       :       Any           = flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase       : x.dtype == jnp.bfloataa       ,_lowerCAmelCase       )       ).values()
  if any(_lowerCAmelCase       ):
    # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
    # and bf16 is not fully supported in PT yet.
    logger.warning(
        'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
        'before loading those in PyTorch model.'       )
    __lowerCamelCase       :       Dict           = jax.tree_util.tree_map(
        lambda _lowerCAmelCase       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = flatten_dict(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = pt_model.state_dict()

  __lowerCamelCase       :       Union[str, Any]           = (pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split('.'       )[0] for k in pt_model_dict.keys()}
  )
  __lowerCamelCase       :       Tuple           = (pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split('.'       )[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  __lowerCamelCase       :       Any           = []
  __lowerCamelCase       :       Union[str, Any]           = set(pt_model_dict.keys()       )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __lowerCamelCase       :       List[str]           = flax_key_tuple[0] == pt_model.base_model_prefix
    __lowerCamelCase       :       Dict           = '.'.join((pt_model.base_model_prefix,) + flax_key_tuple       ) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      __lowerCamelCase       :       List[Any]           = flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Optional[Any]           = (pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCAmelCase       ) not in pt_model_dict:
      # conv layer
      __lowerCamelCase       :       Tuple           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       Tuple           = jnp.transpose(_lowerCAmelCase       ,(3, 2, 0, 1)       )
    elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase       ) not in pt_model_dict:
      # linear layer
      __lowerCamelCase       :       Dict           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       str           = flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      __lowerCamelCase       :       Optional[Any]           = flax_key_tuple[:-1] + ('weight',)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      __lowerCamelCase       :       str           = flax_key_tuple[:-1] + ('running_mean',)
    elif "var" in flax_key_tuple[-1]:
      __lowerCamelCase       :       int           = flax_key_tuple[:-1] + ('running_var',)

    if "batch_stats" in flax_state:
      __lowerCamelCase       :       Tuple           = '.'.join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
    else:
      __lowerCamelCase       :       Optional[int]           = '.'.join(_lowerCAmelCase       )

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    __lowerCamelCase       :       Tuple           = {}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      __lowerCamelCase       :       str           = key.split('.'       )
      __lowerCamelCase       :       Tuple           = None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        __lowerCamelCase       :       Any           = key_components[-2] + '_g'
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        __lowerCamelCase       :       Tuple           = key_components[-2] + '_v'
      if name is not None:
        __lowerCamelCase       :       Optional[int]           = key_components[:-3] + [name]
        __lowerCamelCase       :       Union[str, Any]           = '.'.join(_lowerCAmelCase       )
        __lowerCamelCase       :       Optional[int]           = key

    if flax_key in special_pt_names:
      __lowerCamelCase       :       int           = special_pt_names[flax_key]

    if flax_key in pt_model_dict:
      if flax_tensor.shape != pt_model_dict[flax_key].shape:
        raise ValueError(
            F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
            F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )
      else:
        # add weight to pytorch dict
        __lowerCamelCase       :       Tuple           = np.asarray(_lowerCAmelCase       ) if not isinstance(_lowerCAmelCase       ,np.ndarray       ) else flax_tensor
        __lowerCamelCase       :       List[str]           = torch.from_numpy(_lowerCAmelCase       )
        # remove from missing keys
        missing_keys.remove(_lowerCAmelCase       )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(_lowerCAmelCase       )

  pt_model.load_state_dict(_lowerCAmelCase       )

  # re-transform missing_keys to list
  __lowerCamelCase       :       int           = list(_lowerCAmelCase       )

  if len(_lowerCAmelCase       ) > 0:
    logger.warning(
        'Some weights of the Flax model were not used when initializing the PyTorch model'
        F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
        F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
        ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
        F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
        ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
        ' FlaxBertForSequenceClassification model).'       )
  else:
    logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'       )
  if len(_lowerCAmelCase       ) > 0:
    logger.warning(
        F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
        F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
        ' use it for predictions and inference.'       )
  else:
    logger.warning(
        F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
        'If your task is similar to the task the model of the checkpoint was trained on, '
        F'you can already use {pt_model.__class__.__name__} for predictions without further training.'       )

  return pt_model











'''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      :	Tuple			,	_a      :	List[Any]			,	_a      :	Dict=2			,	_a      :	Dict=32			,	_a      :	int=16			,	_a      :	str=3			,	_a      :	Optional[int]=True			,	_a      :	List[Any]=True			,	_a      :	int=32			,	_a      :	int=4			,	_a      :	Optional[Any]=[0, 1, 2, 3]			,	_a      :	int=4			,	_a      :	Union[str, Any]=37			,	_a      :	List[str]="gelu"			,	_a      :	List[str]=0.1			,	_a      :	List[str]=0.1			,	_a      :	Union[str, Any]=0.02			,	_a      :	str=3			,	_a      :	int=[1, 384, 24, 24]			,	_a      :	Optional[Any]=True			,	_a      :	Tuple=None			,	)					->					Tuple:
    __lowerCamelCase       :       Dict           = parent
    __lowerCamelCase       :       List[Any]           = batch_size
    __lowerCamelCase       :       int           = image_size
    __lowerCamelCase       :       Any           = patch_size
    __lowerCamelCase       :       Tuple           = num_channels
    __lowerCamelCase       :       Dict           = is_training
    __lowerCamelCase       :       List[str]           = use_labels
    __lowerCamelCase       :       Union[str, Any]           = hidden_size
    __lowerCamelCase       :       List[Any]           = num_hidden_layers
    __lowerCamelCase       :       List[Any]           = backbone_out_indices
    __lowerCamelCase       :       Tuple           = num_attention_heads
    __lowerCamelCase       :       Optional[Any]           = intermediate_size
    __lowerCamelCase       :       Any           = hidden_act
    __lowerCamelCase       :       List[str]           = hidden_dropout_prob
    __lowerCamelCase       :       Tuple           = attention_probs_dropout_prob
    __lowerCamelCase       :       List[str]           = initializer_range
    __lowerCamelCase       :       Dict           = num_labels
    __lowerCamelCase       :       List[Any]           = backbone_featmap_shape
    __lowerCamelCase       :       Optional[int]           = scope
    __lowerCamelCase       :       str           = is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    __lowerCamelCase       :       Union[str, Any]           = (image_size // patch_size) ** 2
    __lowerCamelCase       :       Optional[Any]           = num_patches + 1




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[int]           = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

    __lowerCamelCase       :       List[str]           = None
    if self.use_labels:
      __lowerCamelCase       :       Dict           = ids_tensor([self.batch_size, self.image_size, self.image_size]			,	self.num_labels   )

    __lowerCamelCase       :       Dict           = self.get_config()

    return config, pixel_values, labels




  def 						_lowercase     ( self      :	Optional[Any]   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = {
        'global_padding': 'same',
        'layer_type': 'bottleneck',
        'depths': [3, 4, 9],
        'out_features': ['stage1', 'stage2', 'stage3'],
        'embedding_dynamic_padding': True,
        'hidden_sizes': [96, 192, 384, 768],
        'num_groups': 2,
    }

    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=_a			,	initializer_range=self.initializer_range			,	is_hybrid=self.is_hybrid			,	backbone_config=_a			,	backbone_featmap_shape=self.backbone_featmap_shape			,	)




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	int			,	_a      :	str			,	_a      :	Optional[int]   )					->					Optional[int]:
    __lowerCamelCase       :       Any           = DPTModel(config=_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)   )




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Union[str, Any]			,	_a      :	Optional[Any]			,	_a      :	List[Any]   )					->					List[Any]:
    __lowerCamelCase       :       Dict           = self.num_labels
    __lowerCamelCase       :       List[Any]           = DPTForDepthEstimation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.predicted_depth.shape			,	(self.batch_size, self.image_size, self.image_size)   )




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Optional[Any]			,	_a      :	Tuple			,	_a      :	Tuple   )					->					List[Any]:
    __lowerCamelCase       :       Union[str, Any]           = self.num_labels
    __lowerCamelCase       :       Optional[Any]           = DPTForSemanticSegmentation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       int           = model(_a			,	labels=_a   )
    self.parent.assertEqual(
        result.logits.shape			,	(self.batch_size, self.num_labels, self.image_size, self.image_size)   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Tuple           = self.prepare_config_and_inputs()
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       str           = config_and_inputs
    __lowerCamelCase       :       Optional[Any]           = {'pixel_values': pixel_values}
    return config, inputs_dict






@require_torch
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  a_												=(
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  a_												=False
  a_												=False
  a_												=False




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[Any]           = DPTModelTester(self   )
    __lowerCamelCase       :       Union[str, Any]           = ConfigTester(self			,	config_class=_a			,	has_text_modality=_a			,	hidden_size=37   )




  def 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    self.config_tester.run_common_tests()




  @unittest.skip(reason='DPT does not use inputs_embeds'   )
  def 						_lowercase     ( self      :	Dict   )					->					Union[str, Any]:
    pass




  def 						_lowercase     ( self      :	Dict   )					->					str:
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __lowerCamelCase       :       Union[str, Any]           = model_class(_a   )
      self.assertIsInstance(model.get_input_embeddings()			,	(nn.Module)   )
      __lowerCamelCase       :       List[Any]           = model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(_a			,	nn.Linear   )   )




  def 						_lowercase     ( self      :	List[str]   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Any           = self.model_tester.prepare_config_and_inputs_for_common()

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

      __lowerCamelCase       :       Optional[int]           = ['pixel_values']
      self.assertListEqual(arg_names[:1]			,	_a   )




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					int:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    __lowerCamelCase       :       Union[str, Any]           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*_a   )




  def 						_lowercase     ( self      :	Optional[int]   )					->					Dict:
    for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
        continue

      __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase       :       Optional[int]           = True

      if model_class in get_values(_a   ):
        continue

      __lowerCamelCase       :       Tuple           = model_class(_a   )
      model.to(_a   )
      model.train()
      __lowerCamelCase       :       str           = self._prepare_for_class(_a			,	_a			,	return_labels=_a   )
      __lowerCamelCase       :       Dict           = model(**_a   ).loss
      loss.backward()




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					str:
    for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
        continue

      __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase       :       Tuple           = False
      __lowerCamelCase       :       List[str]           = True

      if model_class in get_values(_a   ) or not model_class.supports_gradient_checkpointing:
        continue
      __lowerCamelCase       :       Optional[int]           = model_class(_a   )
      model.to(_a   )
      model.gradient_checkpointing_enable()
      model.train()
      __lowerCamelCase       :       List[Any]           = self._prepare_for_class(_a			,	_a			,	return_labels=_a   )
      __lowerCamelCase       :       str           = model(**_a   ).loss
      loss.backward()




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()

    __lowerCamelCase       :       Union[str, Any]           = _config_zero_init(_a   )
    for model_class in self.all_model_classes:
      __lowerCamelCase       :       List[Any]           = model_class(config=_a   )
      # Skip the check for the backbone
      __lowerCamelCase       :       Any           = []
      for name, module in model.named_modules():
        if module.__class__.__name__ == "DPTViTHybridEmbeddings":
          __lowerCamelCase       :       Dict           = [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 						_lowercase     ( self      :	Dict   )					->					Optional[int]:
    pass




  @slow
  def 						_lowercase     ( self      :	Any   )					->					int:
    for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
      __lowerCamelCase       :       Union[str, Any]           = DPTModel.from_pretrained(_a   )
      self.assertIsNotNone(_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					List[Any]:
    # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
    __lowerCamelCase       ,__lowerCamelCase       :       str           = self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase       :       List[Any]           = 'add'
    with self.assertRaises(_a   ):
      __lowerCamelCase       :       int           = DPTForDepthEstimation(_a   )




def        a_     (							)      ->      str:
  __lowerCamelCase       :       List[str]           = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image






@require_torch
@require_vision
@slow
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Dict   )					->					Tuple:
    __lowerCamelCase       :       Any           = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
    __lowerCamelCase       :       Any           = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(_a   )

    __lowerCamelCase       :       Any           = prepare_img()
    __lowerCamelCase       :       Dict           = image_processor(images=_a			,	return_tensors='pt'   ).to(_a   )

    # forward pass
    with torch.no_grad():
      __lowerCamelCase       :       Any           = model(**_a   )
      __lowerCamelCase       :       Any           = outputs.predicted_depth

    # verify the predicted depth
    __lowerCamelCase       :       int           = torch.Size((1, 384, 384)   )
    self.assertEqual(predicted_depth.shape			,	_a   )

    __lowerCamelCase       :       Tuple           = torch.tensor(
        [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(_a   )

    self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100			,	_a			,	atol=1e-4   )   )






