






import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


__UpperCAmelCase      =				logging.get_logger(__name__)  # pylint: disable=invalid-name


__UpperCAmelCase      =				"lfs-multipart-upload"
class 	a_(		lowercase__	):



     """simple docstring"""



     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	ArgumentParser)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		parser.add_parser(
          'lfs-enable-largefiles'  ,  help=(
              'Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'
          )  ,  )
      enable_parser.add_argument('path'  ,  type=lowerCAmelCase__  ,  help='Local path to repository you want to configure.')
      enable_parser.set_defaults(func=lambda lowerCAmelCase__: LfsEnableCommand(lowerCAmelCase__))

      SCREAMING_SNAKE_CASE				    =		parser.add_parser(
          lowerCAmelCase__  ,  help=(
              'Deprecated: use `huggingface-cli` instead. '
              'Command will get called by git-lfs, do not call it directly.'
          )  ,  )
      upload_parser.set_defaults(func=lambda lowerCAmelCase__: LfsUploadCommand(lowerCAmelCase__))

class 	a_:



     """simple docstring"""



     def __init__(		self					:	List[Any]  ,  lowerCAmelCase__					:	List[str])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		args





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Dict:

      """simple docstring"""
      warnings.warn(
          'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.')
      SCREAMING_SNAKE_CASE				    =		os.path.abspath(self.args.path)
      if not os.path.isdir(lowerCAmelCase__):
       print('This does not look like a valid git repo.')
       exit(1)
      subprocess.run(
          'git config lfs.customtransfer.multipart.path transformers-cli'.split()  ,  check=lowerCAmelCase__  ,  cwd=lowerCAmelCase__)
      subprocess.run(
          f'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split()  ,  check=lowerCAmelCase__  ,  cwd=lowerCAmelCase__  ,  )
      print('Local repo set up for largefiles')





def 						A_					(						lowercase_			)				->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		json.dumps(lowercase_			) + '\n'
 sys.stdout.write(lowercase_			)
 sys.stdout.flush()





def 						A_					(						)				->Optional[Dict]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		json.loads(sys.stdin.readline().strip()			)

 if "terminate" in (msg.get('type'			), msg.get('event'			)):
  # terminate message received
  return None

 if msg.get('event'			) not in ("download", "upload"):
  logger.critical('Received unexpected message'			)
  sys.exit(1			)

 return msg
class 	a_(		lowercase__	):



     """simple docstring"""



     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	int)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		filepath
      SCREAMING_SNAKE_CASE				    =		seek_from
      SCREAMING_SNAKE_CASE				    =		read_limit
      SCREAMING_SNAKE_CASE				    =		0





     def __enter__(		self					:	Any)							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		open(self.filepath  ,  'rb')
      self.f.seek(self.seek_from)
      return self





     def __len__(		self					:	Any)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		os.fstat(self.f.fileno()).st_size
      return min(self.read_limit  ,  total_length - self.seek_from)





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	str=-1)							->	Dict:

      """simple docstring"""
      if self.n_seen >= self.read_limit:
       return b""
      SCREAMING_SNAKE_CASE				    =		self.read_limit - self.n_seen
      SCREAMING_SNAKE_CASE				    =		self.f.read(remaining_amount if n < 0 else min(lowerCAmelCase__  ,  lowerCAmelCase__))
      self.n_seen += len(lowerCAmelCase__)
      return data





     def __iter__(		self					:	Any)							->	Tuple:

      """simple docstring"""
      yield self.read(n=4 * 1_0_2_4 * 1_0_2_4)





     def __exit__(		self					:	Union[str, Any]  ,  *lowerCAmelCase__					:	Optional[Any])							->	Tuple:

      """simple docstring"""
      self.f.close()

class 	a_:



     """simple docstring"""



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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		args





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      # Immediately after invoking a custom transfer process, git-lfs
      # sends initiation data to the process over stdin.
      # This tells the process useful information about the configuration.
      SCREAMING_SNAKE_CASE				    =		json.loads(sys.stdin.readline().strip())
      if not (init_msg.get('event') == "init" and init_msg.get('operation') == "upload"):
       write_msg({'error': {'code': 3_2, 'message': 'Wrong lfs init operation'}})
       sys.exit(1)

      # The transfer process should use the information it needs from the
      # initiation structure, and also perform any one-off setup tasks it
      # needs to do. It should then respond on stdout with a simple empty
      # confirmation structure, as follows:
      write_msg({})

      # After the initiation exchange, git-lfs will send any number of
      # transfer requests to the stdin of the transfer process, in a serial sequence.
      while True:
       SCREAMING_SNAKE_CASE				    =		read_msg()
       if msg is None:
        # When all transfers have been processed, git-lfs will send
        # a terminate event to the stdin of the transfer process.
        # On receiving this message the transfer process should
        # clean up and terminate. No response is expected.
        sys.exit(0)

       SCREAMING_SNAKE_CASE				    =		msg['oid']
       SCREAMING_SNAKE_CASE				    =		msg['path']
       SCREAMING_SNAKE_CASE				    =		msg['action']['href']
       SCREAMING_SNAKE_CASE				    =		msg['action']['header']
       SCREAMING_SNAKE_CASE				    =		int(header.pop('chunk_size'))
       SCREAMING_SNAKE_CASE				    =		list(header.values())

       SCREAMING_SNAKE_CASE				    =		[]
       for i, presigned_url in enumerate(lowerCAmelCase__):
        with FileSlice(lowerCAmelCase__  ,  seek_from=i * chunk_size  ,  read_limit=lowerCAmelCase__) as data:
         SCREAMING_SNAKE_CASE				    =		requests.put(lowerCAmelCase__  ,  data=lowerCAmelCase__)
         r.raise_for_status()
         parts.append(
             {
                 'etag': r.headers.get('etag'),
                 'partNumber': i + 1,
             })
         # In order to support progress reporting while data is uploading / downloading,
         # the transfer process should post messages to stdout
         write_msg(
             {
                 'event': 'progress',
                 'oid': oid,
                 'bytesSoFar': (i + 1) * chunk_size,
                 'bytesSinceLast': chunk_size,
             })
         # Not precise but that's ok.

       SCREAMING_SNAKE_CASE				    =		requests.post(
           lowerCAmelCase__  ,  json={
               'oid': oid,
               'parts': parts,
           }  ,  )
       r.raise_for_status()

       write_msg({'event': 'complete', 'oid': oid})










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     A					:







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

										"""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        lowercase__							(    self   :	Dict				)		->					Tuple:

										"""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        lowercase__							(    self   :	Any				)		->					List[str]:

										"""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        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	Tuple   ,     __UpperCAmelCase   :	List[Any]   ,     __UpperCAmelCase   :	Optional[int]				)		->					Union[str, Any]:

										"""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        lowercase__							(    self   :	List[str]   ,     __UpperCAmelCase   :	Any   ,     __UpperCAmelCase   :	Optional[Any]   ,     __UpperCAmelCase   :	Optional[Any]				)		->					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        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict   ,     __UpperCAmelCase   :	Union[str, Any]   ,     __UpperCAmelCase   :	Dict				)		->					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        lowercase__							(    self   :	int				)		->					Any:

										"""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     A					(				lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		int			  =       (
							    (
							        DinatModel,
							        DinatForImageClassification,
							        DinatBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							_SCREAMING_SNAKE_CASE  :		int			  =       (
							    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
							    if is_torch_available()
							    else {}
							)
							_SCREAMING_SNAKE_CASE  :		List[str]			  =       False

							_SCREAMING_SNAKE_CASE  :		Any			  =       False
							_SCREAMING_SNAKE_CASE  :		Dict			  =       False
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       False
							_SCREAMING_SNAKE_CASE  :		Optional[Any]			  =       False







							def        lowercase__							(    self   :	Any				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModelTester(self				)
										UpperCamelCase_     				= ConfigTester(self   ,     config_class=__UpperCAmelCase   ,     embed_dim=37				)







							def        lowercase__							(    self   :	Optional[Any]				)		->					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        lowercase__							(    self   :	Dict				)		->					List[str]:

										"""simple docstring"""



										return







							def        lowercase__							(    self   :	Optional[Any]				)		->					Union[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__UpperCAmelCase				)







							def        lowercase__							(    self   :	List[str]				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)







							def        lowercase__							(    self   :	Dict				)		->					Optional[int]:

										"""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        lowercase__							(    self   :	Tuple				)		->					Optional[Any]:

										"""simple docstring"""



										pass







							@unittest.skip(reason='Dinat does not use feedforward chunking'				)
							def        lowercase__							(    self   :	List[Any]				)		->					List[Any]:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	List[Any]				)		->					List[str]:

										"""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        lowercase__							(    self   :	Dict				)		->					Union[str, 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        lowercase__							(    self   :	Dict				)		->					Optional[int]:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	List[Any]   ,     __UpperCAmelCase   :	int   ,     __UpperCAmelCase   :	str   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""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        lowercase__							(    self   :	Tuple				)		->					List[str]:

										"""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        lowercase__							(    self   :	List[Any]				)		->					Dict:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	List[Any]				)		->					str:

										"""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     A					(				unittest.TestCase       ):







							@cached_property
							def        lowercase__							(    self   :	Dict				)		->					Dict:

										"""simple docstring"""



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







							@slow
							def        lowercase__							(    self   :	Tuple				)		->					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, 1000)				)
										self.assertEqual(outputs.logits.shape   ,     __UpperCAmelCase				)
										UpperCamelCase_     				= torch.tensor([-0.1_545, -0.7_667, 0.4_642]				).to(__UpperCAmelCase				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,     __UpperCAmelCase   ,     atol=1E-4				)				)







@require_torch
@require_natten
class     A					(				unittest.TestCase  ,						lowerCamelCase_       ):
							_SCREAMING_SNAKE_CASE  :		str			  =       (DinatBackbone,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE  :		int			  =       DinatConfig







							def        lowercase__							(    self   :	Optional[Any]				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModelTester(self				)
