




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__a			:	Optional[Any]							     =		logging.get_logger(__name__)

__a			:	Optional[Any]							     =		{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}




class     A					(				lowerCamelCase_       ):
							_SCREAMING_SNAKE_CASE  :		Optional[Any]			  =       '''blenderbot'''
							_SCREAMING_SNAKE_CASE  :		List[Any]			  =       ['''past_key_values''']
							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







							def __init__(    self   :	Any   ,     __UpperCAmelCase   :	List[Any]=8008   ,     __UpperCAmelCase   :	Tuple=128   ,     __UpperCAmelCase   :	Dict=2   ,     __UpperCAmelCase   :	Tuple=10240   ,     __UpperCAmelCase   :	Union[str, Any]=32   ,     __UpperCAmelCase   :	int=24   ,     __UpperCAmelCase   :	Optional[Any]=10240   ,     __UpperCAmelCase   :	Optional[int]=32   ,     __UpperCAmelCase   :	Any=0.0   ,     __UpperCAmelCase   :	Optional[int]=0.0   ,     __UpperCAmelCase   :	Tuple=True   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	Dict="gelu"   ,     __UpperCAmelCase   :	List[str]=2560   ,     __UpperCAmelCase   :	Optional[Any]=0.1   ,     __UpperCAmelCase   :	Tuple=0.0   ,     __UpperCAmelCase   :	List[str]=0.0   ,     __UpperCAmelCase   :	int=0.02   ,     __UpperCAmelCase   :	List[str]=1   ,     __UpperCAmelCase   :	str=False   ,     __UpperCAmelCase   :	Union[str, Any]=0   ,     __UpperCAmelCase   :	List[Any]=1   ,     __UpperCAmelCase   :	Any=2   ,     __UpperCAmelCase   :	Union[str, Any]=3   ,     __UpperCAmelCase   :	Optional[Any]=2   ,     **__UpperCAmelCase   :	Tuple   ,     )		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= vocab_size
										UpperCamelCase_     				= max_position_embeddings
										UpperCamelCase_     				= d_model
										UpperCamelCase_     				= encoder_ffn_dim
										UpperCamelCase_     				= encoder_layers
										UpperCamelCase_     				= encoder_attention_heads
										UpperCamelCase_     				= decoder_ffn_dim
										UpperCamelCase_     				= decoder_layers
										UpperCamelCase_     				= decoder_attention_heads
										UpperCamelCase_     				= dropout
										UpperCamelCase_     				= attention_dropout
										UpperCamelCase_     				= activation_dropout
										UpperCamelCase_     				= activation_function
										UpperCamelCase_     				= init_std
										UpperCamelCase_     				= encoder_layerdrop
										UpperCamelCase_     				= decoder_layerdrop
										UpperCamelCase_     				= use_cache
										UpperCamelCase_     				= encoder_layers
										UpperCamelCase_     				= scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    pad_token_id=__UpperCAmelCase   ,     bos_token_id=__UpperCAmelCase   ,     eos_token_id=__UpperCAmelCase   ,     is_encoder_decoder=__UpperCAmelCase   ,     decoder_start_token_id=__UpperCAmelCase   ,     encoder_no_repeat_ngram_size=__UpperCAmelCase   ,     forced_eos_token_id=__UpperCAmelCase   ,     **__UpperCAmelCase   ,     )







class     A					(				lowerCamelCase_       ):







							@property
							def        lowercase__							(    self   :	Optional[int]				)		->					Mapping[str, Mapping[int, str]]:

										"""simple docstring"""



										if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase_     				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]				)
													if self.use_past:
																UpperCamelCase_     				= {0: 'batch'}
																UpperCamelCase_     				= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																UpperCamelCase_     				= {0: 'batch', 1: 'decoder_sequence'}
																UpperCamelCase_     				= {0: 'batch', 1: 'decoder_sequence'}
													if self.use_past:
																self.fill_with_past_key_values_(__UpperCAmelCase   ,     direction='inputs'				)
										elif self.task == "causal-lm":
													UpperCamelCase_     				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]				)
													if self.use_past:
																UpperCamelCase_				,   UpperCamelCase_     				= self.num_layers
																for i in range(__UpperCAmelCase				):
																			UpperCamelCase_     				= {0: 'batch', 2: 'past_sequence + sequence'}
																			UpperCamelCase_     				= {0: 'batch', 2: 'past_sequence + sequence'}
										else:
													UpperCamelCase_     				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
													        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
													    ]				)

										return common_inputs







							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
							def        lowercase__							(    self   :	Tuple				)		->					Mapping[str, Mapping[int, str]]:

										"""simple docstring"""



										if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase_     				= super().outputs
										else:
													UpperCamelCase_     				= super(__UpperCAmelCase   ,     self				).outputs
													if self.use_past:
																UpperCamelCase_				,   UpperCamelCase_     				= self.num_layers
																for i in range(__UpperCAmelCase				):
																			UpperCamelCase_     				= {0: 'batch', 2: 'past_sequence + sequence'}
																			UpperCamelCase_     				= {0: 'batch', 2: 'past_sequence + sequence'}
										return common_outputs







							def        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	PreTrainedTokenizer   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	bool = False   ,     __UpperCAmelCase   :	Optional[TensorType] = None   ,     )		->					Mapping[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)
										# Generate decoder inputs
										UpperCamelCase_     				= seq_length if not self.use_past else 1
										UpperCamelCase_     				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)
										UpperCamelCase_     				= {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										UpperCamelCase_     				= dict(**__UpperCAmelCase   ,     **__UpperCAmelCase				)

										if self.use_past:
													if not is_torch_available():
																raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
													else:
																import torch
													UpperCamelCase_				,   UpperCamelCase_     				= common_inputs['input_ids'].shape
													UpperCamelCase_     				= common_inputs['decoder_input_ids'].shape[1]
													UpperCamelCase_				,   UpperCamelCase_     				= self.num_attention_heads
													UpperCamelCase_     				= (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													UpperCamelCase_     				= decoder_seq_length
													UpperCamelCase_     				= (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)
													UpperCamelCase_     				= torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCAmelCase   ,     __UpperCAmelCase				)]   ,     dim=1				)
													UpperCamelCase_     				= []
													UpperCamelCase_				,   UpperCamelCase_     				= self.num_layers

													for _ in range(__UpperCAmelCase				):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(__UpperCAmelCase				),
																        torch.zeros(__UpperCAmelCase				),
																        torch.zeros(__UpperCAmelCase				),
																        torch.zeros(__UpperCAmelCase				),
																    )				)
										return common_inputs







							def        lowercase__							(    self   :	Optional[int]   ,     __UpperCAmelCase   :	PreTrainedTokenizer   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	bool = False   ,     __UpperCAmelCase   :	Optional[TensorType] = None   ,     )		->					Mapping[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)

										if self.use_past:
													if not is_torch_available():
																raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
													else:
																import torch
													UpperCamelCase_				,   UpperCamelCase_     				= common_inputs['input_ids'].shape
													UpperCamelCase_     				= seqlen
													UpperCamelCase_				,   UpperCamelCase_     				= self.num_layers
													UpperCamelCase_				,   UpperCamelCase_     				= self.num_attention_heads
													UpperCamelCase_     				= (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													UpperCamelCase_     				= common_inputs['attention_mask'].dtype
													UpperCamelCase_     				= torch.cat(
													    [common_inputs['attention_mask'], torch.ones(__UpperCAmelCase   ,     __UpperCAmelCase   ,     dtype=__UpperCAmelCase				)]   ,     dim=1				)
													UpperCamelCase_     				= [
													    (torch.zeros(__UpperCAmelCase				), torch.zeros(__UpperCAmelCase				)) for _ in range(__UpperCAmelCase				)
													]
										return common_inputs







							def        lowercase__							(    self   :	Union[str, Any]   ,     __UpperCAmelCase   :	PreTrainedTokenizer   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	bool = False   ,     __UpperCAmelCase   :	Optional[TensorType] = None   ,     )		->					Mapping[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= compute_effective_axis_dimension(
										    __UpperCAmelCase   ,     fixed_dimension=OnnxConfig.default_fixed_batch   ,     num_token_to_add=0				)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										UpperCamelCase_     				= tokenizer.num_special_tokens_to_add(__UpperCAmelCase				)
										UpperCamelCase_     				= compute_effective_axis_dimension(
										    __UpperCAmelCase   ,     fixed_dimension=OnnxConfig.default_fixed_sequence   ,     num_token_to_add=__UpperCAmelCase				)

										# Generate dummy inputs according to compute batch and sequence
										UpperCamelCase_     				= [' '.join([tokenizer.unk_token]				) * seq_length] * batch_size
										UpperCamelCase_     				= dict(tokenizer(__UpperCAmelCase   ,     return_tensors=__UpperCAmelCase				)				)
										return common_inputs







							def        lowercase__							(    self   :	Union[str, Any]   ,     __UpperCAmelCase   :	PreTrainedTokenizer   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	int = -1   ,     __UpperCAmelCase   :	bool = False   ,     __UpperCAmelCase   :	Optional[TensorType] = None   ,     )		->					Mapping[str, Any]:

										"""simple docstring"""



										if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase_     				= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    __UpperCAmelCase   ,     batch_size=__UpperCAmelCase   ,     seq_length=__UpperCAmelCase   ,     is_pair=__UpperCAmelCase   ,     framework=__UpperCAmelCase				)

										elif self.task == "causal-lm":
													UpperCamelCase_     				= self._generate_dummy_inputs_for_causal_lm(
													    __UpperCAmelCase   ,     batch_size=__UpperCAmelCase   ,     seq_length=__UpperCAmelCase   ,     is_pair=__UpperCAmelCase   ,     framework=__UpperCAmelCase				)
										else:
													UpperCamelCase_     				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    __UpperCAmelCase   ,     batch_size=__UpperCAmelCase   ,     seq_length=__UpperCAmelCase   ,     is_pair=__UpperCAmelCase   ,     framework=__UpperCAmelCase				)

										return common_inputs







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

										"""simple docstring"""



										if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase_     				= super()._flatten_past_key_values_(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)
										else:
													UpperCamelCase_     				= super(__UpperCAmelCase   ,     self				)._flatten_past_key_values_(
													    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)







							def        lowercase__							(    self   :	Any   ,     __UpperCAmelCase   :	Mapping[str, Mapping[int, str]]   ,     __UpperCAmelCase   :	str				)		->					Union[str, Any]:

										"""simple docstring"""



										if direction not in ["inputs", "outputs"]:
													raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''				)

										UpperCamelCase_     				= 'past_key_values' if direction == 'inputs' else 'present'
										UpperCamelCase_				,   UpperCamelCase_     				= self.num_layers

										UpperCamelCase_     				= 'past_encoder_sequence'
										UpperCamelCase_     				= 'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

										for i in range(__UpperCAmelCase				):
													UpperCamelCase_     				= {0: 'batch', 2: decoder_sequence}
													UpperCamelCase_     				= {0: 'batch', 2: decoder_sequence}
													UpperCamelCase_     				= {0: 'batch', 2: encoder_sequence}
													UpperCamelCase_     				= {0: 'batch', 2: encoder_sequence}






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				)
