




'''simple docstring'''




import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
    import tensorflow as tf

    tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset


UpperCAmelCase_       				=      5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Namespace       ):
 '''simple docstring'''





 return PTtoTFCommand(
     args.model_name       ,						args.local_dir       ,						args.max_error       ,						args.new_weights       ,						args.no_pr       ,						args.push       ,						args.extra_commit_description       ,						args.override_model_class       ,						)







class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  @staticmethod
  def 							SCREAMING_SNAKE_CASE__			(						_UpperCAmelCase    :	ArgumentParser ):






   """simple docstring"""






   UpperCAmelCase__										=    parser.add_parser(
       """pt-to-tf"""						,		help=(
           """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
           """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
       )						,		)
   train_parser.add_argument(
       """--model-name"""						,		type=_UpperCAmelCase						,		required=_UpperCAmelCase						,		help="""The model name, including owner/organization, as seen on the hub."""						,		)
   train_parser.add_argument(
       """--local-dir"""						,		type=_UpperCAmelCase						,		default=""""""						,		help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""						,		)
   train_parser.add_argument(
       """--max-error"""						,		type=_UpperCAmelCase						,		default=_UpperCAmelCase						,		help=(
           f'''Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'''
       )						,		)
   train_parser.add_argument(
       """--new-weights"""						,		action="""store_true"""						,		help="""Optional flag to create new TensorFlow weights, even if they already exist."""						,		)
   train_parser.add_argument(
       """--no-pr"""						,		action="""store_true"""						,		help="""Optional flag to NOT open a PR with converted weights.""" )
   train_parser.add_argument(
       """--push"""						,		action="""store_true"""						,		help="""Optional flag to push the weights directly to `main` (requires permissions)"""						,		)
   train_parser.add_argument(
       """--extra-commit-description"""						,		type=_UpperCAmelCase						,		default=""""""						,		help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""						,		)
   train_parser.add_argument(
       """--override-model-class"""						,		type=_UpperCAmelCase						,		default=_UpperCAmelCase						,		help="""If you think you know better than the auto-detector, you can specify the model class here. """
       """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""						,		)
   train_parser.set_defaults(func=_UpperCAmelCase )


  @staticmethod
  def 							SCREAMING_SNAKE_CASE__			(						_UpperCAmelCase    :	Tuple						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    set(pt_outputs.keys() )
   UpperCAmelCase__										=    set(tf_outputs.keys() )
   if pt_out_attrs != tf_out_attrs:
    raise ValueError(
        f'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
        f''' {tf_out_attrs})''' )

   # 2. For each output attribute, computes the difference
   def _find_pt_tf_differences(_UpperCAmelCase    :	Dict						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Dict						,		_UpperCAmelCase    :	Tuple="" ):
    # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
    # recursivelly, keeping the name of the attribute.
    if isinstance(_UpperCAmelCase						,		torch.Tensor ):
     UpperCAmelCase__										=    np.max(np.abs(pt_out.numpy() - tf_out.numpy() ) )
     UpperCAmelCase__										=    tensor_difference
    else:
     UpperCAmelCase__										=    attr_name
     for i, pt_item in enumerate(_UpperCAmelCase ):
      # If it is a named attribute, we keep the name. Otherwise, just its index.
      if isinstance(_UpperCAmelCase						,		_UpperCAmelCase ):
       UpperCAmelCase__										=    root_name + pt_item
       UpperCAmelCase__										=    tf_out[pt_item]
       UpperCAmelCase__										=    pt_out[pt_item]
      else:
       UpperCAmelCase__										=    root_name + f'''[{i}]'''
       UpperCAmelCase__										=    tf_out[i]
      UpperCAmelCase__										=    _find_pt_tf_differences(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )

    return differences

   return _find_pt_tf_differences(_UpperCAmelCase						,		_UpperCAmelCase						,		{} )


  def __init__(						self    :	str						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	float						,		_UpperCAmelCase    :	bool						,		_UpperCAmelCase    :	bool						,		_UpperCAmelCase    :	bool						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	str						,		*_UpperCAmelCase    :	str						,		):






   """simple docstring"""






   UpperCAmelCase__										=    logging.get_logger("""transformers-cli/pt_to_tf""" )
   UpperCAmelCase__										=    model_name
   UpperCAmelCase__										=    local_dir if local_dir else os.path.join("""/tmp"""						,		_UpperCAmelCase )
   UpperCAmelCase__										=    max_error
   UpperCAmelCase__										=    new_weights
   UpperCAmelCase__										=    no_pr
   UpperCAmelCase__										=    push
   UpperCAmelCase__										=    extra_commit_description
   UpperCAmelCase__										=    override_model_class


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Tuple						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	Any ):






   """simple docstring"""






   def _get_audio_input():
    UpperCAmelCase__										=    load_dataset("""hf-internal-testing/librispeech_asr_dummy"""						,		"""clean"""						,		split="""validation""" )
    UpperCAmelCase__										=    ds.sort("""id""" ).select(range(2 ) )[:2]["""audio"""]
    UpperCAmelCase__										=    [x["""array"""] for x in speech_samples]
    return raw_samples

   UpperCAmelCase__										=    type(pt_model.config )
   if model_config_class in PROCESSOR_MAPPING:
    UpperCAmelCase__										=    AutoProcessor.from_pretrained(self._local_dir )
    if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
     UpperCAmelCase__										=    processor.tokenizer.eos_token
   elif model_config_class in IMAGE_PROCESSOR_MAPPING:
    UpperCAmelCase__										=    AutoImageProcessor.from_pretrained(self._local_dir )
   elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
    UpperCAmelCase__										=    AutoFeatureExtractor.from_pretrained(self._local_dir )
   elif model_config_class in TOKENIZER_MAPPING:
    UpperCAmelCase__										=    AutoTokenizer.from_pretrained(self._local_dir )
    if processor.pad_token is None:
     UpperCAmelCase__										=    processor.eos_token
   else:
    raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})''' )

   UpperCAmelCase__										=    set(inspect.signature(pt_model.forward ).parameters.keys() )
   UpperCAmelCase__										=    {}
   if "input_ids" in model_forward_signature:
    processor_inputs.update(
        {
            """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
            """padding""": True,
            """truncation""": True,
        } )
   if "pixel_values" in model_forward_signature:
    UpperCAmelCase__										=    load_dataset("""cifar10"""						,		"""plain_text"""						,		split="""test""" )[:2]["""img"""]
    processor_inputs.update({"""images""": sample_images} )
   if "input_features" in model_forward_signature:
    UpperCAmelCase__										=    inspect.signature(processor.feature_extractor ).parameters
    # Pad to the largest input length by default but take feature extractor default
    # padding value if it exists e.g. "max_length" and is not False or None
    if "padding" in feature_extractor_signature:
     UpperCAmelCase__										=    feature_extractor_signature["""padding"""].default
     if default_strategy is not False and default_strategy is not None:
      UpperCAmelCase__										=    default_strategy
     else:
      UpperCAmelCase__										=    True
    else:
     UpperCAmelCase__										=    True
    processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy} )
   if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
    processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True} )
   UpperCAmelCase__										=    processor(**_UpperCAmelCase						,		return_tensors="""pt""" )
   UpperCAmelCase__										=    processor(**_UpperCAmelCase						,		return_tensors="""tf""" )

   # Extra input requirements, in addition to the input modality
   if (
       config.is_encoder_decoder
       or (hasattr(_UpperCAmelCase						,		"""encoder""" ) and hasattr(_UpperCAmelCase						,		"""decoder""" ))
       or "decoder_input_ids" in tf_dummy_inputs
   ):
    UpperCAmelCase__										=    np.asarray([[1], [1]]						,		dtype=_UpperCAmelCase ) * (pt_model.config.decoder_start_token_id or 0)
    pt_input.update({"""decoder_input_ids""": torch.tensor(_UpperCAmelCase )} )
    tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(_UpperCAmelCase )} )

   return pt_input, tf_input



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   if version.parse(huggingface_hub.__version__ ) < version.parse("""0.9.0""" ):
    raise ImportError(
        """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
        """ installation.""" )
   else:
    from huggingface_hub import Repository, create_commit
    from huggingface_hub._commit_api import CommitOperationAdd

   # Fetch remote data
   UpperCAmelCase__										=    Repository(local_dir=self._local_dir						,		clone_from=self._model_name )

   # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
   UpperCAmelCase__										=    AutoConfig.from_pretrained(self._local_dir )
   UpperCAmelCase__										=    config.architectures
   if self._override_model_class is not None:
    if self._override_model_class.startswith("""TF""" ):
     UpperCAmelCase__										=    [self._override_model_class[2:]]
    else:
     UpperCAmelCase__										=    [self._override_model_class]
    try:
     UpperCAmelCase__										=    getattr(import_module("""transformers""" )						,		architectures[0] )
    except AttributeError:
     raise ValueError(f'''Model class {self._override_model_class} not found in transformers.''' )
    try:
     UpperCAmelCase__										=    getattr(import_module("""transformers""" )						,		"""TF""" + architectures[0] )
    except AttributeError:
     raise ValueError(f'''TF model class TF{self._override_model_class} not found in transformers.''' )
   elif architectures is None:  # No architecture defined -- use auto classes
    UpperCAmelCase__										=    getattr(import_module("""transformers""" )						,		"""AutoModel""" )
    UpperCAmelCase__										=    getattr(import_module("""transformers""" )						,		"""TFAutoModel""" )
    self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel""" )
   else:  # Architecture defined -- use it
    if len(_UpperCAmelCase ) > 1:
     raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})''' )
    self._logger.warning(f'''Detected architecture: {architectures[0]}''' )
    UpperCAmelCase__										=    getattr(import_module("""transformers""" )						,		architectures[0] )
    try:
     UpperCAmelCase__										=    getattr(import_module("""transformers""" )						,		"""TF""" + architectures[0] )
    except AttributeError:
     raise AttributeError(f'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.''' )

        # Check the TF dummy inputs to see what keys we need in the forward pass
   UpperCAmelCase__										=    tf_class.from_config(_UpperCAmelCase )
   UpperCAmelCase__										=    tf_from_pt_model.dummy_inputs

   del tf_from_pt_model  # Try to keep only one model in memory at a time

   # Load the model and get some basic inputs
   UpperCAmelCase__										=    pt_class.from_pretrained(self._local_dir )
   pt_model.eval()

   UpperCAmelCase__							,			UpperCAmelCase__										=    self.get_inputs(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )

   with torch.no_grad():
    UpperCAmelCase__										=    pt_model(**_UpperCAmelCase						,		output_hidden_states=_UpperCAmelCase )
   del pt_model  # will no longer be used, and may have a large memory footprint

   UpperCAmelCase__										=    tf_class.from_pretrained(self._local_dir						,		from_pt=_UpperCAmelCase )
   UpperCAmelCase__										=    tf_from_pt_model(**_UpperCAmelCase						,		output_hidden_states=_UpperCAmelCase						,		training=_UpperCAmelCase )

   # Confirms that cross loading PT weights into TF worked.
   UpperCAmelCase__										=    self.find_pt_tf_differences(_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__										=    {k: v for k, v in crossload_differences.items() if """hidden""" not in k}
   UpperCAmelCase__										=    {k: v for k, v in crossload_differences.items() if """hidden""" in k}
   if len(_UpperCAmelCase ) == 0 and architectures is not None:
    raise ValueError(
        f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
        """ output was found. All outputs start with 'hidden'""" )
   UpperCAmelCase__										=    max(output_differences.values() ) if output_differences else 0.0
   UpperCAmelCase__										=    max(hidden_differences.values() )
   if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
    raise ValueError(
        """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
        + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
        + """\n""".join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error] )
        + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
        + """\n""".join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error] ) )

   # Save the weights in a TF format (if needed) and confirms that the results are still good
   UpperCAmelCase__										=    os.path.join(self._local_dir						,		_UpperCAmelCase )
   UpperCAmelCase__										=    os.path.join(self._local_dir						,		_UpperCAmelCase )
   if (not os.path.exists(_UpperCAmelCase ) and not os.path.exists(_UpperCAmelCase )) or self._new_weights:
    tf_from_pt_model.save_pretrained(self._local_dir )
   del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

   UpperCAmelCase__										=    tf_class.from_pretrained(self._local_dir )
   UpperCAmelCase__										=    tf_model(**_UpperCAmelCase						,		output_hidden_states=_UpperCAmelCase )

   UpperCAmelCase__										=    self.find_pt_tf_differences(_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__										=    {k: v for k, v in conversion_differences.items() if """hidden""" not in k}
   UpperCAmelCase__										=    {k: v for k, v in conversion_differences.items() if """hidden""" in k}
   if len(_UpperCAmelCase ) == 0 and architectures is not None:
    raise ValueError(
        f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
        """ output was found. All outputs start with 'hidden'""" )
   UpperCAmelCase__										=    max(output_differences.values() ) if output_differences else 0.0
   UpperCAmelCase__										=    max(hidden_differences.values() )
   if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
    raise ValueError(
        """The converted TensorFlow model has different outputs, something went wrong!\n"""
        + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
        + """\n""".join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error] )
        + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
        + """\n""".join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error] ) )

   UpperCAmelCase__										=    """Update TF weights""" if self._new_weights else """Add TF weights"""
   if self._push:
    repo.git_add(auto_lfs_track=_UpperCAmelCase )
    repo.git_commit(_UpperCAmelCase )
    repo.git_push(blocking=_UpperCAmelCase )  # this prints a progress bar with the upload
    self._logger.warning(f'''TF weights pushed into {self._model_name}''' )
   elif not self._no_pr:
    self._logger.warning("""Uploading the weights into a new PR...""" )
    UpperCAmelCase__										=    (
        """Model converted by the [`transformers`' `pt_to_tf`"""
        """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
        """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
        f'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
        f'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
        f'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
        f'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
    )
    if self._max_error > MAX_ERROR:
     commit_descrition += (
         f'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
     )
    if self._extra_commit_description:
     commit_descrition += "\n\n" + self._extra_commit_description

    # sharded model -> adds all related files (index and .h5 shards)
    if os.path.exists(_UpperCAmelCase ):
     UpperCAmelCase__										=    [
         CommitOperationAdd(path_in_repo=_UpperCAmelCase						,		path_or_fileobj=_UpperCAmelCase )
     ]
     for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5""" ):
      operations += [
          CommitOperationAdd(path_in_repo=os.path.basename(_UpperCAmelCase )						,		path_or_fileobj=_UpperCAmelCase )
      ]
    else:
     UpperCAmelCase__										=    [CommitOperationAdd(path_in_repo=_UpperCAmelCase						,		path_or_fileobj=_UpperCAmelCase )]

    UpperCAmelCase__										=    create_commit(
        repo_id=self._model_name						,		operations=_UpperCAmelCase						,		commit_message=_UpperCAmelCase						,		commit_description=_UpperCAmelCase						,		repo_type="""model"""						,		create_pr=_UpperCAmelCase						,		).pr_url
    self._logger.warning(f'''PR open in {hub_pr_url}''' )






'''simple docstring'''




import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 DinatBackbone, DinatForImageClassification, DinatModel
    from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	int=13						,		_UpperCAmelCase    :	List[str]=64						,		_UpperCAmelCase    :	List[str]=4						,		_UpperCAmelCase    :	int=3						,		_UpperCAmelCase    :	Tuple=16						,		_UpperCAmelCase    :	Optional[Any]=[1, 2, 1]						,		_UpperCAmelCase    :	List[Any]=[2, 4, 8]						,		_UpperCAmelCase    :	str=3						,		_UpperCAmelCase    :	Tuple=[[3], [1, 2], [1]]						,		_UpperCAmelCase    :	Union[str, Any]=2.0						,		_UpperCAmelCase    :	List[str]=True						,		_UpperCAmelCase    :	Optional[int]=0.0						,		_UpperCAmelCase    :	Optional[Any]=0.0						,		_UpperCAmelCase    :	List[str]=0.1						,		_UpperCAmelCase    :	str="gelu"						,		_UpperCAmelCase    :	List[str]=True						,		_UpperCAmelCase    :	Tuple=0.02						,		_UpperCAmelCase    :	Optional[Any]=1E-5						,		_UpperCAmelCase    :	Any=True						,		_UpperCAmelCase    :	List[str]=None						,		_UpperCAmelCase    :	Any=True						,		_UpperCAmelCase    :	List[str]=10						,		_UpperCAmelCase    :	Any=["stage1", "stage2"]						,		_UpperCAmelCase    :	Union[str, Any]=[1, 2]						,		):






   """simple docstring"""






   UpperCAmelCase__										=    parent
   UpperCAmelCase__										=    batch_size
   UpperCAmelCase__										=    image_size
   UpperCAmelCase__										=    patch_size
   UpperCAmelCase__										=    num_channels
   UpperCAmelCase__										=    embed_dim
   UpperCAmelCase__										=    depths
   UpperCAmelCase__										=    num_heads
   UpperCAmelCase__										=    kernel_size
   UpperCAmelCase__										=    dilations
   UpperCAmelCase__										=    mlp_ratio
   UpperCAmelCase__										=    qkv_bias
   UpperCAmelCase__										=    hidden_dropout_prob
   UpperCAmelCase__										=    attention_probs_dropout_prob
   UpperCAmelCase__										=    drop_path_rate
   UpperCAmelCase__										=    hidden_act
   UpperCAmelCase__										=    patch_norm
   UpperCAmelCase__										=    layer_norm_eps
   UpperCAmelCase__										=    initializer_range
   UpperCAmelCase__										=    is_training
   UpperCAmelCase__										=    scope
   UpperCAmelCase__										=    use_labels
   UpperCAmelCase__										=    num_labels
   UpperCAmelCase__										=    out_features
   UpperCAmelCase__										=    out_indices


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






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

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

   UpperCAmelCase__										=    self.get_config()

   return config, pixel_values, labels


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   return DinatConfig(
       num_labels=self.num_labels						,		image_size=self.image_size						,		patch_size=self.patch_size						,		num_channels=self.num_channels						,		embed_dim=self.embed_dim						,		depths=self.depths						,		num_heads=self.num_heads						,		kernel_size=self.kernel_size						,		dilations=self.dilations						,		mlp_ratio=self.mlp_ratio						,		qkv_bias=self.qkv_bias						,		hidden_dropout_prob=self.hidden_dropout_prob						,		attention_probs_dropout_prob=self.attention_probs_dropout_prob						,		drop_path_rate=self.drop_path_rate						,		hidden_act=self.hidden_act						,		patch_norm=self.patch_norm						,		layer_norm_eps=self.layer_norm_eps						,		initializer_range=self.initializer_range						,		out_features=self.out_features						,		out_indices=self.out_indices						,		)


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatModel(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )

   UpperCAmelCase__										=    UpperCAmelCase__										=    (config.image_size // config.patch_size) // (2 ** (len(config.depths ) - 1))
   UpperCAmelCase__										=    int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

   self.parent.assertEqual(
       result.last_hidden_state.shape						,		(self.batch_size, expected_height, expected_width, expected_dim) )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatForImageClassification(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase						,		labels=_UpperCAmelCase )
   self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels) )

   # test greyscale images
   UpperCAmelCase__										=    1
   UpperCAmelCase__										=    DinatForImageClassification(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()

   UpperCAmelCase__										=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
   UpperCAmelCase__										=    model(_UpperCAmelCase )
   self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels) )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatBackbone(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )

   # verify hidden states
   self.parent.assertEqual(len(result.feature_maps )						,		len(config.out_features ) )
   self.parent.assertListEqual(list(result.feature_maps[0].shape )						,		[self.batch_size, model.channels[0], 16, 16] )

   # verify channels
   self.parent.assertEqual(len(model.channels )						,		len(config.out_features ) )

   # verify backbone works with out_features=None
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    DinatBackbone(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps )						,		1 )
   self.parent.assertListEqual(list(result.feature_maps[0].shape )						,		[self.batch_size, model.channels[-1], 4, 4] )

   # verify channels
   self.parent.assertEqual(len(model.channels )						,		1 )



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    self.prepare_config_and_inputs()
   UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    config_and_inputs
   UpperCAmelCase__										=    {"""pixel_values""": pixel_values}
   return config, inputs_dict

@require_natten
@require_torch
class 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					List[str]				     =						(
      (
          DinatModel,
          DinatForImageClassification,
          DinatBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowerCAmelCase_				:					List[str]				     =						(
      {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
      if is_torch_available()
      else {}
  )
  lowerCAmelCase_				:					Union[str, Any]				     =						False

  lowerCAmelCase_				:					Union[str, Any]				     =						False
  lowerCAmelCase_				:					List[str]				     =						False
  lowerCAmelCase_				:					Any				     =						False
  lowerCAmelCase_				:					Any				     =						False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatModelTester(self )
   UpperCAmelCase__										=    ConfigTester(self						,		config_class=_UpperCAmelCase						,		embed_dim=37 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   return


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*_UpperCAmelCase )


  @unittest.skip(reason="""Dinat does not use inputs_embeds""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   pass


  @unittest.skip(reason="""Dinat does not use feedforward chunking""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   pass


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

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

    UpperCAmelCase__										=    ["""pixel_values"""]
    self.assertListEqual(arg_names[:1]						,		_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   self.skipTest("""Dinat's attention operation is handled entirely by NATTEN.""" )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	Dict ):






   """simple docstring"""






   UpperCAmelCase__										=    model_class(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()

   with torch.no_grad():
    UpperCAmelCase__										=    model(**self._prepare_for_class(_UpperCAmelCase						,		_UpperCAmelCase ) )

   UpperCAmelCase__										=    outputs.hidden_states

   UpperCAmelCase__										=    getattr(
       self.model_tester						,		"""expected_num_hidden_layers"""						,		len(self.model_tester.depths ) + 1 )
   self.assertEqual(len(_UpperCAmelCase )						,		_UpperCAmelCase )

   # Dinat has a different seq_length
   UpperCAmelCase__										=    (
       config.patch_size
       if isinstance(config.patch_size						,		collections.abc.Iterable )
       else (config.patch_size, config.patch_size)
   )

   UpperCAmelCase__										=    image_size[0] // patch_size[0]
   UpperCAmelCase__										=    image_size[1] // patch_size[1]

   self.assertListEqual(
       list(hidden_states[0].shape[-3:] )						,		[height, width, self.model_tester.embed_dim]						,		)

   if model_class.__name__ != "DinatBackbone":
    UpperCAmelCase__										=    outputs.reshaped_hidden_states
    self.assertEqual(len(_UpperCAmelCase )						,		_UpperCAmelCase )

    UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    reshaped_hidden_states[0].shape
    UpperCAmelCase__										=    (
        reshaped_hidden_states[0].view(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase ).permute(0						,		2						,		3						,		1 )
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-3:] )						,		[height, width, self.model_tester.embed_dim]						,		)


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__										=    (
       self.model_tester.image_size
       if isinstance(self.model_tester.image_size						,		collections.abc.Iterable )
       else (self.model_tester.image_size, self.model_tester.image_size)
   )

   for model_class in self.all_model_classes:
    UpperCAmelCase__										=    True
    self.check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    UpperCAmelCase__										=    True

    self.check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )


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






   """simple docstring"""






   for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__										=    DinatModel.from_pretrained(_UpperCAmelCase )
    self.assertIsNotNone(_UpperCAmelCase )



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__										=    _config_zero_init(_UpperCAmelCase )
   for model_class in self.all_model_classes:
    UpperCAmelCase__										=    model_class(config=_UpperCAmelCase )
    for name, param in model.named_parameters():
     if "embeddings" not in name and param.requires_grad:
      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'''						,		)

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







  '''simple docstring'''







  @cached_property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224""" ) if is_vision_available() else None



  @slow
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224""" ).to(_UpperCAmelCase )
   UpperCAmelCase__										=    self.default_image_processor

   UpperCAmelCase__										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   UpperCAmelCase__										=    image_processor(images=_UpperCAmelCase						,		return_tensors="""pt""" ).to(_UpperCAmelCase )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__										=    model(**_UpperCAmelCase )

   # verify the logits
   UpperCAmelCase__										=    torch.Size((1, 10_00) )
   self.assertEqual(outputs.logits.shape						,		_UpperCAmelCase )
   UpperCAmelCase__										=    torch.tensor([-0.1545, -0.7667, 0.4642] ).to(_UpperCAmelCase )
   self.assertTrue(torch.allclose(outputs.logits[0, :3]						,		_UpperCAmelCase						,		atol=1E-4 ) )

@require_torch
@require_natten
class 						lowerCAmelCase_   ( unittest.TestCase			,  lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						(DinatBackbone,) if is_torch_available() else ()
  lowerCAmelCase_				:					Optional[Any]				     =						DinatConfig


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatModelTester(self )
