


"""simple docstring"""


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()
A     :   Dict			    =			logging.get_logger(__name__)

A     :   List[str]			    =			"The Nymphenburg Palace is a beautiful palace in Munich!"


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			{
			    "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,
			}

			__lowerCAmelCase				    =			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
			__lowerCAmelCase				    =			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
			__lowerCAmelCase				    =			"openwebtext_ccnews_stories_books_cased"

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

			__lowerCAmelCase				    =			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      )
			__lowerCAmelCase				    =			original_bort._collect_params_with_prefix()

			# Build our config 🤗
			__lowerCAmelCase				    =			{
			    "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      ),
			}

			__lowerCAmelCase				    =			BertConfig.from_dict(_UpperCamelCase      )
			__lowerCAmelCase				    =			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      ):
						__lowerCAmelCase				    =			hf_param.shape

						__lowerCAmelCase				    =			to_torch(params[gluon_param]      )
						__lowerCAmelCase				    =			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

			__lowerCAmelCase				    =			check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight    , "word_embed.0.weight"      )
			__lowerCAmelCase				    =			check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight    , "encoder.position_weight"      )
			__lowerCAmelCase				    =			check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias    , "encoder.layer_norm.beta"      )
			__lowerCAmelCase				    =			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)
			__lowerCAmelCase				    =			torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data      )

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

						# self attention
						__lowerCAmelCase				    =			layer.attention.self

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

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

						# self attention output
						__lowerCAmelCase				    =			layer.attention.output

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

						# intermediate
						__lowerCAmelCase				    =			layer.intermediate

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

						# output
						__lowerCAmelCase				    =			layer.output

						__lowerCAmelCase				    =			check_and_map_params(
						    bert_output.dense.bias    , f"encoder.transformer_cells.{i}.ffn.ffn_2.bias"      )
						__lowerCAmelCase				    =			check_and_map_params(
						    bert_output.dense.weight    , f"encoder.transformer_cells.{i}.ffn.ffn_2.weight"      )
						__lowerCAmelCase				    =			check_and_map_params(
						    bert_output.LayerNorm.bias    , f"encoder.transformer_cells.{i}.ffn.layer_norm.beta"      )
						__lowerCAmelCase				    =			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
			__lowerCAmelCase				    =			RobertaTokenizer.from_pretrained("roberta-base"      )

			__lowerCAmelCase				    =			tokenizer.encode_plus(_UpperCamelCase      )["input_ids"]

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

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

			__lowerCAmelCase				    =			tokenizer.encode_plus(_UpperCamelCase    , return_tensors="pt"      )
			__lowerCAmelCase				    =			hf_bort_model(**_UpperCamelCase      )[0]

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

			__lowerCAmelCase				    =			np.max(np.abs(hf_layer - gluon_layer      )      ).item()
			__lowerCAmelCase				    =			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__":
	A     :   Optional[int]			    =			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."
	)
	A     :   Optional[Any]			    =			parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









import inspect
import unittest

from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_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_=32,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=[10, 20, 30, 40],			SCREAMING_SNAKE_CASE_=[2, 2, 3, 2],			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=37,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=10,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=["stage2", "stage3", "stage4"],			SCREAMING_SNAKE_CASE_=[2, 3, 4],			SCREAMING_SNAKE_CASE_=None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	parent
          UpperCamelCase    :  Optional[Any]							=	batch_size
          UpperCamelCase    :  Union[str, Any]							=	image_size
          UpperCamelCase    :  Union[str, Any]							=	num_channels
          UpperCamelCase    :  List[Any]							=	num_stages
          UpperCamelCase    :  Any							=	hidden_sizes
          UpperCamelCase    :  Optional[int]							=	depths
          UpperCamelCase    :  Optional[int]							=	is_training
          UpperCamelCase    :  List[str]							=	use_labels
          UpperCamelCase    :  Dict							=	intermediate_size
          UpperCamelCase    :  List[Any]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	num_labels
          UpperCamelCase    :  str							=	initializer_range
          UpperCamelCase    :  List[str]							=	out_features
          UpperCamelCase    :  List[str]							=	out_indices
          UpperCamelCase    :  str							=	scope
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  int							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

          UpperCamelCase    :  List[Any]							=	self.get_config()
          return config, pixel_values, labels
       def 					snake_case_       (			self       )     ->		Any:
          return ConvNextConfig(
              num_channels=self.num_channels,			hidden_sizes=self.hidden_sizes,			depths=self.depths,			num_stages=self.num_stages,			hidden_act=self.hidden_act,			is_decoder=SCREAMING_SNAKE_CASE_,			initializer_range=self.initializer_range,			out_features=self.out_features,			out_indices=self.out_indices,			num_labels=self.num_labels,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Tuple:
          UpperCamelCase    :  Dict							=	ConvNextModel(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  str							=	model(SCREAMING_SNAKE_CASE_       )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  Optional[int]							=	ConvNextForImageClassification(SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_,			labels=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_       )     ->		Dict:
          UpperCamelCase    :  int							=	ConvNextBackbone(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  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, self.hidden_sizes[1], 4, 4]       )

          # verify channels
          self.parent.assertEqual(len(model.channels       ),			len(config.out_features       )       )
          self.parent.assertListEqual(model.channels,			config.hidden_sizes[1:]       )

          # verify backbone works with out_features=None
          UpperCamelCase    :  Any							=	None
          UpperCamelCase    :  List[str]							=	ConvNextBackbone(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Tuple							=	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, self.hidden_sizes[-1], 1, 1]       )

          # verify channels
          self.parent.assertEqual(len(model.channels       ),			1       )
          self.parent.assertListEqual(model.channels,			[config.hidden_sizes[-1]]       )






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






@require_torch
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					List[Any]        =  (
           (
               ConvNextModel,
               ConvNextForImageClassification,
               ConvNextBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase__				:					Union[str, Any]        =  (
           {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCAmelCase__				:					Dict        =  True
       UpperCAmelCase__				:					List[str]        =  False
       UpperCAmelCase__				:					Tuple        =  False
       UpperCAmelCase__				:					List[Any]        =  False
       UpperCAmelCase__				:					Tuple        =  False
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Dict							=	ConvNextModelTester(self       )
          UpperCamelCase    :  int							=	ConfigTester(self,			config_class=SCREAMING_SNAKE_CASE_,			has_text_modality=SCREAMING_SNAKE_CASE_,			hidden_size=37       )
       def 					snake_case_       (			self       )     ->		List[str]:
          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       )     ->		Union[str, Any]:
          return
       @unittest.skip(reason='ConvNext does not use inputs_embeds'       )
       def 					snake_case_       (			self       )     ->		List[str]:
          pass
       @unittest.skip(reason='ConvNext does not support input and output embeddings'       )
       def 					snake_case_       (			self       )     ->		str:
          pass
       @unittest.skip(reason='ConvNext does not use feedforward chunking'       )
       def 					snake_case_       (			self       )     ->		str:
          pass
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase    :  Union[str, Any]							=	['pixel_values']
             self.assertListEqual(arg_names[:1],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Optional[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  List[Any]							=	model_class(SCREAMING_SNAKE_CASE_       )
             model.to(SCREAMING_SNAKE_CASE_       )
             model.eval()

             with torch.no_grad():
                UpperCamelCase    :  List[Any]							=	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )

             UpperCamelCase    :  List[str]							=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             UpperCamelCase    :  int							=	self.model_tester.num_stages
             self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			expected_num_stages + 1       )

             # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]       ),			[self.model_tester.image_size // 4, self.model_tester.image_size // 4],			)

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

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[int]							=	True
             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

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

             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )






       @slow
       def 					snake_case_       (			self       )     ->		Tuple:
          for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  Union[str, Any]							=	ConvNextModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



@require_torch
@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224'       ) if is_vision_available() else None






       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Dict							=	ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	self.default_image_processor
          UpperCamelCase    :  Any							=	prepare_img()
          UpperCamelCase    :  List[str]							=	image_processor(images=SCREAMING_SNAKE_CASE_,			return_tensors='pt'       ).to(SCREAMING_SNAKE_CASE_       )

          # forward pass
          with torch.no_grad():
             UpperCamelCase    :  Optional[Any]							=	model(**SCREAMING_SNAKE_CASE_       )

          # verify the logits
          UpperCamelCase    :  List[Any]							=	torch.Size((1, 1000)       )
          self.assertEqual(outputs.logits.shape,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[str]							=	torch.tensor([-0.02_60, -0.47_39, 0.19_11]       ).to(SCREAMING_SNAKE_CASE_       )

          self.assertTrue(torch.allclose(outputs.logits[0, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-4       )       )






@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase       ,    a__							):
       UpperCAmelCase__				:					Tuple        =  (ConvNextBackbone,) if is_torch_available() else ()
       UpperCAmelCase__				:					List[str]        =  ConvNextConfig

       UpperCAmelCase__				:					Tuple        =  False
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	ConvNextModelTester(self       )



