
'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase					:    Optional[int]       				=			logging.get_logger(__name__)

_lowercase					:    List[Any]       				=			{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Any				):



						"""simple docstring"""





						for attribute in key.split('''.'''				):
												lowercase_      :	Optional[int]						=			getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						if weight_type is not None:
												lowercase_      :	Optional[int]						=			getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).shape
						else:
												lowercase_      :	Optional[Any]						=			hf_pointer.shape

						assert hf_shape == value.shape, (
						    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
						    F''' {value.shape} for {full_name}'''
						)

						if weight_type == "weight":
												lowercase_      :	int						=			value
						elif weight_type == "weight_g":
												lowercase_      :	Dict						=			value
						elif weight_type == "weight_v":
												lowercase_      :	Dict						=			value
						elif weight_type == "bias":
												lowercase_      :	Union[str, Any]						=			value
						else:
												lowercase_      :	Optional[Any]						=			value

						logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





						lowercase_      :	Tuple						=			[]
						lowercase_      :	Union[str, Any]						=			fairseq_model.state_dict()

						lowercase_      :	Dict						=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase_      :	str						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == '''group''' , )
																		lowercase_      :	int						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase_      :	Union[str, Any]						=			'''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																								if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
																														lowercase_      :	Dict						=			True
																														if "*" in mapped_key:
																																				lowercase_      :	str						=			name.split(__SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				lowercase_      :	Tuple						=			mapped_key.replace('''*''' , __SCREAMING_SNAKE_CASE				)
																														if "weight_g" in name:
																																				lowercase_      :	Dict						=			'''weight_g'''
																														elif "weight_v" in name:
																																				lowercase_      :	Dict						=			'''weight_v'''
																														elif "weight" in name:
																																				lowercase_      :	int						=			'''weight'''
																														elif "bias" in name:
																																				lowercase_      :	List[Any]						=			'''bias'''
																														else:
																																				lowercase_      :	Optional[Any]						=			None
																														set_recursively(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)
																								continue
												if not is_used:
																		unused_weights.append(__SCREAMING_SNAKE_CASE				)

						logger.warning(F'''Unused weights: {unused_weights}'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Optional[int]				):



						"""simple docstring"""





						lowercase_      :	int						=			full_name.split('''conv_layers.'''				)[-1]
						lowercase_      :	int						=			name.split('''.'''				)
						lowercase_      :	int						=			int(items[0]				)
						lowercase_      :	List[Any]						=			int(items[1]				)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
																		)
																		lowercase_      :	int						=			value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
																		)
																		lowercase_      :	Any						=			value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
																		    " found."
																		)
																		lowercase_      :	int						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
																		)
																		lowercase_      :	List[str]						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						else:
												unused_weights.append(__SCREAMING_SNAKE_CASE				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





						lowercase_      :	str						=			SEWDConfig()
						if is_finetuned:
												lowercase_      :	Optional[int]						=			model.wav_encoder.wav_model.cfg
						else:
												lowercase_      :	Dict						=			model.cfg

						lowercase_      :	Tuple						=			fs_config.conv_bias
						lowercase_      :	Dict						=			eval(fs_config.conv_feature_layers				)
						lowercase_      :	Union[str, Any]						=			[x[0] for x in conv_layers]
						lowercase_      :	Dict						=			[x[1] for x in conv_layers]
						lowercase_      :	List[str]						=			[x[2] for x in conv_layers]
						lowercase_      :	Any						=			'''gelu'''
						lowercase_      :	Union[str, Any]						=			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						lowercase_      :	Union[str, Any]						=			0.0
						lowercase_      :	str						=			fs_config.activation_fn.name
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_embed_dim
						lowercase_      :	Any						=			0.02
						lowercase_      :	Any						=			fs_config.encoder_ffn_embed_dim
						lowercase_      :	List[str]						=			1E-5
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_layerdrop
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_attention_heads
						lowercase_      :	Dict						=			fs_config.conv_pos_groups
						lowercase_      :	int						=			fs_config.conv_pos
						lowercase_      :	Union[str, Any]						=			len(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[Any]						=			fs_config.encoder_layers
						lowercase_      :	List[Any]						=			fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						lowercase_      :	Union[str, Any]						=			fs_config.max_position_embeddings
						lowercase_      :	Tuple						=			fs_config.position_buckets
						lowercase_      :	str						=			fs_config.share_att_key
						lowercase_      :	List[Any]						=			fs_config.relative_attention
						lowercase_      :	Any						=			fs_config.position_biased_input
						lowercase_      :	Union[str, Any]						=			tuple(fs_config.pos_att_type.split('''|'''				)				)
						lowercase_      :	List[str]						=			fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												lowercase_      :	List[Any]						=			model.cfg
												lowercase_      :	List[str]						=			fs_config.final_dropout
												lowercase_      :	Union[str, Any]						=			fs_config.layerdrop
						lowercase_      :	Optional[int]						=			fs_config.activation_dropout
						lowercase_      :	Dict						=			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						lowercase_      :	int						=			fs_config.attention_dropout
						lowercase_      :	Union[str, Any]						=			fs_config.dropout_input
						lowercase_      :	Any						=			fs_config.dropout
						lowercase_      :	List[str]						=			fs_config.mask_channel_length
						lowercase_      :	Union[str, Any]						=			fs_config.mask_channel_prob
						lowercase_      :	str						=			fs_config.mask_length
						lowercase_      :	List[Any]						=			fs_config.mask_prob

						lowercase_      :	Optional[int]						=			'''Wav2Vec2FeatureExtractor'''
						lowercase_      :	Dict						=			'''Wav2Vec2CTCTokenizer'''

						return config
@torch.no_grad()
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					int=None , __SCREAMING_SNAKE_CASE   :					List[Any]=None , __SCREAMING_SNAKE_CASE   :					Tuple=True				):



						"""simple docstring"""





						if is_finetuned:
												lowercase_		,					lowercase_		,					lowercase_      :	Optional[Any]						=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
						else:
												lowercase_		,					lowercase_		,					lowercase_      :	List[str]						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

						if config_path is not None:
												lowercase_      :	Union[str, Any]						=			SEWDConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	List[str]						=			convert_config(model[0] , __SCREAMING_SNAKE_CASE				)
						lowercase_      :	Tuple						=			model[0].eval()

						lowercase_      :	Any						=			True if config.feat_extract_norm == '''layer''' else False
						lowercase_      :	List[Any]						=			WavaVecaFeatureExtractor(
						    feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , )

						if is_finetuned:
												if dict_path:
																		lowercase_      :	Optional[int]						=			Dictionary.load(__SCREAMING_SNAKE_CASE				)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowercase_      :	int						=			target_dict.pad_index
																		lowercase_      :	List[Any]						=			target_dict.bos_index
																		lowercase_      :	Dict						=			target_dict.pad_index
																		lowercase_      :	Dict						=			target_dict.bos_index
																		lowercase_      :	List[str]						=			target_dict.eos_index
																		lowercase_      :	Dict						=			len(target_dict.symbols				)
																		lowercase_      :	Optional[Any]						=			os.path.join(__SCREAMING_SNAKE_CASE , '''vocab.json'''				)
																		if not os.path.isdir(__SCREAMING_SNAKE_CASE				):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__SCREAMING_SNAKE_CASE				)				)
																								return
																		os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE				)
																		with open(__SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8'''				) as vocab_handle:
																								json.dump(target_dict.indices , __SCREAMING_SNAKE_CASE				)
																		lowercase_      :	List[str]						=			WavaVecaCTCTokenizer(
																		    __SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__SCREAMING_SNAKE_CASE , )
																		lowercase_      :	Optional[int]						=			WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE				)
																		processor.save_pretrained(__SCREAMING_SNAKE_CASE				)

												lowercase_      :	int						=			SEWDForCTC(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	Union[str, Any]						=			SEWDModel(__SCREAMING_SNAKE_CASE				)
												feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE				)

						recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						hf_model.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
							_lowercase					:    List[str]       				=			argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument(
							    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							_lowercase					:    str       				=			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)





'''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__  :






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=13							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=4							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=16							,				__SCREAMING_SNAKE_CASE=[1, 2, 1]							,				__SCREAMING_SNAKE_CASE=[2, 4, 8]							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=[[3], [1, 2], [1]]							,				__SCREAMING_SNAKE_CASE=2.0							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.1							,				__SCREAMING_SNAKE_CASE="gelu"							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=0.02							,				__SCREAMING_SNAKE_CASE=1E-5							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=10							,				__SCREAMING_SNAKE_CASE=["stage1", "stage2"]							,				__SCREAMING_SNAKE_CASE=[1, 2]							,				):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	str						=			image_size
											lowercase_      :	Optional[Any]						=			patch_size
											lowercase_      :	Union[str, Any]						=			num_channels
											lowercase_      :	List[Any]						=			embed_dim
											lowercase_      :	Optional[int]						=			depths
											lowercase_      :	Any						=			num_heads
											lowercase_      :	Any						=			kernel_size
											lowercase_      :	str						=			dilations
											lowercase_      :	Optional[Any]						=			mlp_ratio
											lowercase_      :	Optional[Any]						=			qkv_bias
											lowercase_      :	Union[str, Any]						=			hidden_dropout_prob
											lowercase_      :	str						=			attention_probs_dropout_prob
											lowercase_      :	List[Any]						=			drop_path_rate
											lowercase_      :	str						=			hidden_act
											lowercase_      :	Optional[int]						=			patch_norm
											lowercase_      :	Optional[int]						=			layer_norm_eps
											lowercase_      :	str						=			initializer_range
											lowercase_      :	Optional[Any]						=			is_training
											lowercase_      :	Any						=			scope
											lowercase_      :	Dict						=			use_labels
											lowercase_      :	Union[str, Any]						=			num_labels
											lowercase_      :	List[str]						=			out_features
											lowercase_      :	str						=			out_indices






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

											lowercase_      :	Union[str, Any]						=			self.get_config()

											return config, pixel_values, labels






					def       _snake_case       (  self						):


											"""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       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModel(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	int						=			(config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
											lowercase_      :	Dict						=			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       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	int						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE							,				labels=__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)						)

											# test greyscale images
											lowercase_      :	Union[str, Any]						=			1
											lowercase_      :	List[str]						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

											lowercase_      :	str						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											lowercase_      :	Optional[int]						=			model(__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Dict						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)

											# 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
											lowercase_      :	str						=			None
											lowercase_      :	Optional[int]						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	str						=			model(__SCREAMING_SNAKE_CASE						)

											# 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       _snake_case       (  self						):


											"""simple docstring"""





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







@require_natten
@require_torch
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(
					    (
					        DinatModel,
					        DinatForImageClassification,
					        DinatBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase_	  =	(
					    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
					    if is_torch_available()
					    else {}
					)
					lowerCAmelCase_	  =	False

					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModelTester(self						)
											lowercase_      :	Optional[Any]						=			ConfigTester(self							,				config_class=__SCREAMING_SNAKE_CASE							,				embed_dim=37						)






					def       _snake_case       (  self						):


											"""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       _snake_case       (  self						):


											"""simple docstring"""





											return






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Any						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE						)






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


											"""simple docstring"""





											pass






					@unittest.skip(reason='''Dinat does not use feedforward chunking'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

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






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	int						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase_      :	Dict						=			['''pixel_values''']
																	self.assertListEqual(arg_names[:1]							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			model_class(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

											with torch.no_grad():
																	lowercase_      :	Optional[Any]						=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)						)

											lowercase_      :	int						=			outputs.hidden_states

											lowercase_      :	List[str]						=			getattr(
											    self.model_tester							,				'''expected_num_hidden_layers'''							,				len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

											# Dinat has a different seq_length
											lowercase_      :	Optional[Any]						=			(
											    config.patch_size
											    if isinstance(config.patch_size							,				collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											lowercase_      :	Union[str, Any]						=			image_size[0] // patch_size[0]
											lowercase_      :	Any						=			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":
																	lowercase_      :	Any						=			outputs.reshaped_hidden_states
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

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






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

											lowercase_      :	List[str]						=			(
											    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:
																	lowercase_      :	Dict						=			True
																	self.check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowercase_      :	str						=			True

																	self.check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :	Optional[Any]						=			DinatModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

											lowercase_      :	Tuple						=			_config_zero_init(__SCREAMING_SNAKE_CASE						)
											for model_class in self.all_model_classes:
																	lowercase_      :	List[str]						=			model_class(config=__SCREAMING_SNAKE_CASE						)
																	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	):






					@cached_property
					def       _snake_case       (  self						):


											"""simple docstring"""





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






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						).to(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	str						=			self.default_image_processor

											lowercase_      :	Tuple						=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
											lowercase_      :	Tuple						=			image_processor(images=__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	lowercase_      :	str						=			model(**__SCREAMING_SNAKE_CASE						)

											# verify the logits
											lowercase_      :	List[Any]						=			torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Union[str, Any]						=			torch.tensor([-0.1_545, -0.7_667, 0.4_642]						).to(__SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]							,				__SCREAMING_SNAKE_CASE							,				atol=1E-4						)						)







@require_torch
@require_natten
class 					lowerCAmelCase__  (    unittest.TestCase       ,		lowerCamelCase_	):
					lowerCAmelCase_	  =	(DinatBackbone,) if is_torch_available() else ()
					lowerCAmelCase_	  =	DinatConfig






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			DinatModelTester(self						)



