



import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
 raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
 raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
__snake_case						:int      		=     logging.get_logger(__name__)

__snake_case						:str      		=     '''The Nymphenburg Palace is a beautiful palace in Munich!'''





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				{
        '''attention_cell''': '''multi_head''',
        '''num_layers''': 4,
        '''units''': 1024,
        '''hidden_size''': 768,
        '''max_length''': 512,
        '''num_heads''': 8,
        '''scaled''': True,
        '''dropout''': 0.1,
        '''use_residual''': True,
        '''embed_size''': 1024,
        '''embed_dropout''': 0.1,
        '''word_embed''': None,
        '''layer_norm_eps''': 1E-5,
        '''token_type_vocab_size''': 2,
    }

    __a						       =				bort_4_8_768_1024_hparams

    # Let's construct the original Bort model here
    # Taken from official BERT implementation, see:
    # https://github.com/alexa/bort/blob/master/bort/bort.py
    __a						       =				BERTEncoder(
        attention_cell=predefined_args['''attention_cell''']   , num_layers=predefined_args['''num_layers''']   , units=predefined_args['''units''']   , hidden_size=predefined_args['''hidden_size''']   , max_length=predefined_args['''max_length''']   , num_heads=predefined_args['''num_heads''']   , scaled=predefined_args['''scaled''']   , dropout=predefined_args['''dropout''']   , output_attention=_UpperCAmelCase   , output_all_encodings=_UpperCAmelCase   , use_residual=predefined_args['''use_residual''']   , activation=predefined_args.get('''activation'''   , '''gelu'''				)   , layer_norm_eps=predefined_args.get('''layer_norm_eps'''   , _UpperCAmelCase				)   , )

    # Vocab information needs to be fetched first
    # It's the same as RoBERTa, so RobertaTokenizer can be used later
    __a						       =				'''openwebtext_ccnews_stories_books_cased'''

    # Specify download folder to Gluonnlp's vocab
    __a						       =				os.path.join(get_home_dir()   , '''models'''				)
    __a						       =				_load_vocab(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , cls=_UpperCAmelCase				)

    __a						       =				nlp.model.BERTModel(
        _UpperCAmelCase   , len(_UpperCAmelCase				)   , units=predefined_args['''units''']   , embed_size=predefined_args['''embed_size''']   , embed_dropout=predefined_args['''embed_dropout''']   , word_embed=predefined_args['''word_embed''']   , use_pooler=_UpperCAmelCase   , use_token_type_embed=_UpperCAmelCase   , token_type_vocab_size=predefined_args['''token_type_vocab_size''']   , use_classifier=_UpperCAmelCase   , use_decoder=_UpperCAmelCase   , )

    original_bort.load_parameters(_UpperCAmelCase   , cast_dtype=_UpperCAmelCase   , ignore_extra=_UpperCAmelCase				)
    __a						       =				original_bort._collect_params_with_prefix()

    # Build our config 🤗
    __a						       =				{
        '''architectures''': ['''BertForMaskedLM'''],
        '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_act''': '''gelu''',
        '''hidden_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_size''': predefined_args['''embed_size'''],
        '''initializer_range''': 0.02,
        '''intermediate_size''': predefined_args['''hidden_size'''],
        '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
        '''max_position_embeddings''': predefined_args['''max_length'''],
        '''model_type''': '''bort''',
        '''num_attention_heads''': predefined_args['''num_heads'''],
        '''num_hidden_layers''': predefined_args['''num_layers'''],
        '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
        '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
        '''vocab_size''': len(_UpperCAmelCase				),
    }

    __a						       =				BertConfig.from_dict(_UpperCAmelCase				)
    __a						       =				BertForMaskedLM(_UpperCAmelCase				)
    hf_bort_model.eval()

    # Parameter mapping table (Gluonnlp to Transformers)
    # * denotes layer index
    #
    # | Gluon Parameter                                                | Transformers Parameter
    # | -------------------------------------------------------------- | ----------------------
    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

    # Helper function to convert MXNET Arrays to PyTorch
    def to_torch(_UpperCAmelCase				) -> nn.Parameter:
        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()				)				)

    # Check param shapes and map new HF param back
    def check_and_map_params(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				hf_param.shape

        __a						       =				to_torch(params[gluon_param]				)
        __a						       =				gluon_param.shape

        assert (
            shape_hf == shape_gluon
        ), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

        return gluon_param

    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight   , '''word_embed.0.weight'''				)
    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight   , '''encoder.position_weight'''				)
    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias   , '''encoder.layer_norm.beta'''				)
    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.weight   , '''encoder.layer_norm.gamma'''				)

    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
    __a						       =				torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data				)

    for i in range(hf_bort_config.num_hidden_layers				):
        __a						       =				hf_bort_model.bert.encoder.layer[i]

        # self attention
        __a						       =				layer.attention.self

        __a						       =				check_and_map_params(
            self_attn.key.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'				)

        __a						       =				check_and_map_params(
            self_attn.key.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'				)
        __a						       =				check_and_map_params(
            self_attn.query.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'				)
        __a						       =				check_and_map_params(
            self_attn.query.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'				)
        __a						       =				check_and_map_params(
            self_attn.value.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'				)
        __a						       =				check_and_map_params(
            self_attn.value.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'				)

        # self attention output
        __a						       =				layer.attention.output

        __a						       =				check_and_map_params(
            self_output.dense.bias   , f'encoder.transformer_cells.{i}.proj.bias'				)
        __a						       =				check_and_map_params(
            self_output.dense.weight   , f'encoder.transformer_cells.{i}.proj.weight'				)
        __a						       =				check_and_map_params(
            self_output.LayerNorm.bias   , f'encoder.transformer_cells.{i}.layer_norm.beta'				)
        __a						       =				check_and_map_params(
            self_output.LayerNorm.weight   , f'encoder.transformer_cells.{i}.layer_norm.gamma'				)

        # intermediate
        __a						       =				layer.intermediate

        __a						       =				check_and_map_params(
            intermediate.dense.bias   , f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'				)
        __a						       =				check_and_map_params(
            intermediate.dense.weight   , f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'				)

        # output
        __a						       =				layer.output

        __a						       =				check_and_map_params(
            bert_output.dense.bias   , f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'				)
        __a						       =				check_and_map_params(
            bert_output.dense.weight   , f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'				)
        __a						       =				check_and_map_params(
            bert_output.LayerNorm.bias   , f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'				)
        __a						       =				check_and_map_params(
            bert_output.LayerNorm.weight   , f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'				)

    # Save space and energy 🎄
    hf_bort_model.half()

    # Compare output of both models
    __a						       =				RobertaTokenizer.from_pretrained('''roberta-base'''				)

    __a						       =				tokenizer.encode_plus(_UpperCAmelCase				)['''input_ids''']

    # Get gluon output
    __a						       =				mx.nd.array([input_ids]				)
    __a						       =				original_bort(inputs=_UpperCAmelCase   , token_types=[]				)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(_UpperCAmelCase				)
    __a						       =				BertModel.from_pretrained(_UpperCAmelCase				)
    hf_bort_model.eval()

    __a						       =				tokenizer.encode_plus(_UpperCAmelCase   , return_tensors='''pt'''				)
    __a						       =				hf_bort_model(**_UpperCAmelCase				)[0]

    __a						       =				output_gluon[0].asnumpy()
    __a						       =				output_hf[0].detach().numpy()

    __a						       =				np.max(np.abs(hf_layer - gluon_layer				)				).item()
    __a						       =				np.allclose(_UpperCAmelCase   , _UpperCAmelCase   , atol=1E-3				)

    if success:
        print('''✔️ Both model do output the same tensors'''				)
    else:
        print('''❌ Both model do **NOT** output the same tensors'''				)
        print('''Absolute difference is:'''   , _UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Optional[Any]      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __snake_case						:Union[str, Any]      		=     parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""
import collections
import inspect
import unittest

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

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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
						from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Optional[int]						,     snake_case:							Any						,     snake_case:							Optional[Any]=13						,     snake_case:							Tuple=32						,     snake_case:							Optional[int]=2						,     snake_case:							Tuple=3						,     snake_case:							Tuple=16						,     snake_case:							Optional[Any]=[1, 2, 1]						,     snake_case:							Optional[int]=[2, 2, 4]						,     snake_case:							Optional[int]=2						,     snake_case:							int=2.0						,     snake_case:							Union[str, Any]=True						,     snake_case:							List[str]=0.0						,     snake_case:							List[Any]=0.0						,     snake_case:							Optional[Any]=0.1						,     snake_case:							List[Any]="gelu"						,     snake_case:							Optional[int]=False						,     snake_case:							Union[str, Any]=True						,     snake_case:							Union[str, Any]=0.0_2						,     snake_case:							Optional[int]=1E-5						,     snake_case:							Optional[Any]=True						,     snake_case:							List[Any]=None						,     snake_case:							List[Any]=True						,     snake_case:							Optional[Any]=10						,     snake_case:							str=8						,     )   ->	Tuple:
											snake_case_ :Dict						=			parent
											snake_case_ :Any						=			batch_size
											snake_case_ :List[Any]						=			image_size
											snake_case_ :List[Any]						=			patch_size
											snake_case_ :int						=			num_channels
											snake_case_ :Tuple						=			embed_dim
											snake_case_ :str						=			depths
											snake_case_ :str						=			num_heads
											snake_case_ :Optional[int]						=			window_size
											snake_case_ :Tuple						=			mlp_ratio
											snake_case_ :Any						=			qkv_bias
											snake_case_ :List[Any]						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :Union[str, Any]						=			drop_path_rate
											snake_case_ :Any						=			hidden_act
											snake_case_ :Optional[Any]						=			use_absolute_embeddings
											snake_case_ :Union[str, Any]						=			patch_norm
											snake_case_ :Dict						=			layer_norm_eps
											snake_case_ :str						=			initializer_range
											snake_case_ :Tuple						=			is_training
											snake_case_ :Tuple						=			scope
											snake_case_ :Union[str, Any]						=			use_labels
											snake_case_ :Optional[Any]						=			type_sequence_label_size
											snake_case_ :Dict						=			encoder_stride






					def    lowerCAmelCase_				(       self:							int						)   ->	int:
											snake_case_ :List[str]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case_ :Any						=			None
											if self.use_labels:
																	snake_case_ :str						=			ids_tensor([self.batch_size]						,     self.type_sequence_label_size						)

											snake_case_ :int						=			self.get_config()

											return config, pixel_values, labels






					def    lowerCAmelCase_				(       self:							str						)   ->	Union[str, Any]:
											return SwinvaConfig(
											    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						,     window_size=self.window_size						,     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						,     use_absolute_embeddings=self.use_absolute_embeddings						,     path_norm=self.patch_norm						,     layer_norm_eps=self.layer_norm_eps						,     initializer_range=self.initializer_range						,     encoder_stride=self.encoder_stride						,     )






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Optional[int]						,     snake_case:							Dict						,     snake_case:							str						)   ->	List[Any]:
											snake_case_ :Union[str, Any]						=			SwinvaModel(config=snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Optional[int]						=			model(snake_case						)

											snake_case_ :Optional[Any]						=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
											snake_case_ :int						=			int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

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






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							List[str]						,     snake_case:							Tuple						,     snake_case:							int						)   ->	Any:
											snake_case_ :Dict						=			SwinvaForMaskedImageModeling(config=snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Tuple						=			model(snake_case						)
											self.parent.assertEqual(
											    result.logits.shape						,     (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

											# test greyscale images
											snake_case_ :List[Any]						=			1
											snake_case_ :int						=			SwinvaForMaskedImageModeling(snake_case						)
											model.to(snake_case						)
											model.eval()

											snake_case_ :Union[str, Any]						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											snake_case_ :int						=			model(snake_case						)
											self.parent.assertEqual(result.logits.shape						,     (self.batch_size, 1, self.image_size, self.image_size)						)






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Any						,     snake_case:							List[str]						,     snake_case:							Union[str, Any]						)   ->	Tuple:
											snake_case_ :int						=			self.type_sequence_label_size
											snake_case_ :List[Any]						=			SwinvaForImageClassification(snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Dict						=			model(snake_case						,     labels=snake_case						)
											self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.type_sequence_label_size)						)






					def    lowerCAmelCase_				(       self:							int						)   ->	str:
											snake_case_ :Any						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_ :List[str]						=			config_and_inputs
											snake_case_ :List[Any]						=			{"""pixel_values""": pixel_values}
											return config, inputs_dict

@require_torch
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Optional[Any]						=     (
					    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
					)
					_A		:			Any						=     (
					    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					_A		:			List[Any]						=     False
					_A		:			List[str]						=     False
					_A		:			Tuple						=     False
					_A		:			List[str]						=     False






					def    lowerCAmelCase_				(       self:							Dict						)   ->	List[Any]:
											snake_case_ :Optional[int]						=			SwinvaModelTester(self						)
											snake_case_ :List[str]						=			ConfigTester(self						,     config_class=snake_case						,     embed_dim=37						)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[Any]:
											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    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Tuple:
											snake_case_ :List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case						)






					@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""						)
					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	str:
											pass






					@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""						)
					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											pass






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Union[str, Any]:
											snake_case_,       snake_case_ :List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_ :Optional[int]						=			model_class(snake_case						)
																	self.assertIsInstance(model.get_input_embeddings()						,     (nn.Module)						)
																	snake_case_ :List[Any]						=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case						,     nn.Linear						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[int]:
											snake_case_,       snake_case_ :Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	snake_case_ :List[Any]						=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]						,     snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_ :List[str]						=			True

											for model_class in self.all_model_classes:
																	snake_case_ :List[Any]						=			True
																	snake_case_ :Any						=			False
																	snake_case_ :Optional[int]						=			True
																	snake_case_ :Tuple						=			model_class(snake_case						)
																	model.to(snake_case						)
																	model.eval()
																	with torch.no_grad():
																							snake_case_ :Any						=			model(**self._prepare_for_class(snake_case						,     snake_case						)						)
																	snake_case_ :str						=			outputs.attentions
																	snake_case_ :Dict						=			len(self.model_tester.depths						)
																	self.assertEqual(len(snake_case						)						,     snake_case						)

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	snake_case_ :Union[str, Any]						=			True
																	snake_case_ :Tuple						=			config.window_size**2
																	snake_case_ :Any						=			model_class(snake_case						)
																	model.to(snake_case						)
																	model.eval()
																	with torch.no_grad():
																							snake_case_ :Union[str, Any]						=			model(**self._prepare_for_class(snake_case						,     snake_case						)						)
																	snake_case_ :int						=			outputs.attentions
																	self.assertEqual(len(snake_case						)						,     snake_case						)

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]						)						,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,     )
																	snake_case_ :Any						=			len(snake_case						)

																	# Check attention is always last and order is fine
																	snake_case_ :int						=			True
																	snake_case_ :Dict						=			True
																	snake_case_ :Optional[int]						=			model_class(snake_case						)
																	model.to(snake_case						)
																	model.eval()
																	with torch.no_grad():
																							snake_case_ :Dict						=			model(**self._prepare_for_class(snake_case						,     snake_case						)						)

																	if hasattr(self.model_tester						,     """num_hidden_states_types"""						):
																							snake_case_ :Any						=			self.model_tester.num_hidden_states_types
																	else:
																							# also another +1 for reshaped_hidden_states
																							snake_case_ :int						=			2
																	self.assertEqual(out_len + added_hidden_states						,     len(snake_case						)						)

																	snake_case_ :str						=			outputs.attentions

																	self.assertEqual(len(snake_case						)						,     snake_case						)

																	self.assertListEqual(
																	    list(self_attentions[0].shape[-3:]						)						,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,     )






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Dict						,     snake_case:							Dict						,     snake_case:							Optional[Any]						,     snake_case:							Dict						)   ->	List[str]:
											snake_case_ :Dict						=			model_class(snake_case						)
											model.to(snake_case						)
											model.eval()

											with torch.no_grad():
																	snake_case_ :Optional[int]						=			model(**self._prepare_for_class(snake_case						,     snake_case						)						)

											snake_case_ :str						=			outputs.hidden_states

											snake_case_ :List[Any]						=			getattr(
											    self.model_tester						,     """expected_num_hidden_layers"""						,     len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(snake_case						)						,     snake_case						)

											# Swinv2 has a different seq_length
											snake_case_ :List[Any]						=			(
											    config.patch_size
											    if isinstance(config.patch_size						,     collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											snake_case_ :Optional[int]						=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]						)						,     [num_patches, self.model_tester.embed_dim]						,     )

											snake_case_ :str						=			outputs.reshaped_hidden_states
											self.assertEqual(len(snake_case						)						,     snake_case						)

											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Any						=			reshaped_hidden_states[0].shape
											snake_case_ :int						=			(
											    reshaped_hidden_states[0].view(snake_case						,     snake_case						,     height * width						).permute(0						,     2						,     1						)
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]						)						,     [num_patches, self.model_tester.embed_dim]						,     )






					def    lowerCAmelCase_				(       self:							Any						)   ->	Any:
											snake_case_,       snake_case_ :List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_ :Union[str, Any]						=			(
											    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:
																	snake_case_ :Union[str, Any]						=			True
																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     snake_case						)

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

																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_,       snake_case_ :Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_ :Optional[int]						=			3

											snake_case_ :Union[str, Any]						=			(
											    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)
											)
											snake_case_ :str						=			(
											    config.patch_size
											    if isinstance(config.patch_size						,     collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											snake_case_ :Any						=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											snake_case_ :int						=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
																	snake_case_ :str						=			True
																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     (padded_height, padded_width)						)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	snake_case_ :Tuple						=			True
																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     (padded_height, padded_width)						)






					def    lowerCAmelCase_				(       self:							Any						)   ->	Tuple:
											snake_case_ :int						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Dict:
											snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case						)






					@slow
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Dict:
											for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_ :List[str]						=			SwinvaModel.from_pretrained(snake_case						)
																	self.assertIsNotNone(snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	List[Any]:
											snake_case_,       snake_case_ :str						=			self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_ :Optional[int]						=			_config_zero_init(snake_case						)
											for model_class in self.all_model_classes:
																	snake_case_ :Tuple						=			model_class(config=snake_case						)
																	for name, param in model.named_parameters():
																							if "embeddings" not in name and "logit_scale" 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_vision
@require_torch
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					@cached_property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	List[Any]:
											return (
											    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""						)
											    if is_vision_available()
											    else None
											)






					@slow
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[str]:
											snake_case_ :Tuple						=			SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""						).to(
											    snake_case						)
											snake_case_ :str						=			self.default_image_processor

											snake_case_ :List[Any]						=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
											snake_case_ :str						=			image_processor(images=snake_case						,     return_tensors="""pt"""						).to(snake_case						)

											# forward pass
											with torch.no_grad():
																	snake_case_ :Tuple						=			model(**snake_case						)

											# verify the logits
											snake_case_ :Dict						=			torch.Size((1, 1_000)						)
											self.assertEqual(outputs.logits.shape						,     snake_case						)
											snake_case_ :int						=			torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]						).to(snake_case						)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]						,     snake_case						,     atol=1E-4						)						)
