



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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  """The Nymphenburg Palace is a beautiful palace in Munich!"""




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
   A							:					Union[str, Any]              =						{
       "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							:					int              =						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							:					List[Any]              =						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=_lowerCamelCase ,    output_all_encodings=_lowerCamelCase ,    use_residual=predefined_args["use_residual"] ,    activation=predefined_args.get("activation" ,    "gelu"    ) ,    layer_norm_eps=predefined_args.get("layer_norm_eps" ,    _lowerCamelCase    ) ,    )

   # Vocab information needs to be fetched first
   # It's the same as RoBERTa, so RobertaTokenizer can be used later
   A							:					List[str]              =						"openwebtext_ccnews_stories_books_cased"

   # Specify download folder to Gluonnlp's vocab
   A							:					Tuple              =						os.path.join(get_home_dir() ,    "models"    )
   A							:					Tuple              =						_load_vocab(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    cls=_lowerCamelCase    )

   A							:					Any              =						nlp.model.BERTModel(
       _lowerCamelCase ,    len(_lowerCamelCase    ) ,    units=predefined_args["units"] ,    embed_size=predefined_args["embed_size"] ,    embed_dropout=predefined_args["embed_dropout"] ,    word_embed=predefined_args["word_embed"] ,    use_pooler=_lowerCamelCase ,    use_token_type_embed=_lowerCamelCase ,    token_type_vocab_size=predefined_args["token_type_vocab_size"] ,    use_classifier=_lowerCamelCase ,    use_decoder=_lowerCamelCase ,    )

   original_bort.load_parameters(_lowerCamelCase ,    cast_dtype=_lowerCamelCase ,    ignore_extra=_lowerCamelCase    )
   A							:					str              =						original_bort._collect_params_with_prefix()

   # Build our config 🤗
   A							:					Union[str, Any]              =						{
       "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(_lowerCamelCase    ),
   }

   A							:					str              =						BertConfig.from_dict(_lowerCamelCase    )
   A							:					List[str]              =						BertForMaskedLM(_lowerCamelCase    )
   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(_lowerCamelCase    ) -> 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(_lowerCamelCase ,    _lowerCamelCase    ):
      A							:					Optional[int]              =						hf_param.shape

      A							:					int              =						to_torch(params[gluon_param]    )
      A							:					List[str]              =						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							:					Union[str, Any]              =						check_and_map_params(
       hf_bort_model.bert.embeddings.word_embeddings.weight ,    "word_embed.0.weight"    )
   A							:					int              =						check_and_map_params(
       hf_bort_model.bert.embeddings.position_embeddings.weight ,    "encoder.position_weight"    )
   A							:					int              =						check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.bias ,    "encoder.layer_norm.beta"    )
   A							:					Any              =						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							:					Union[str, Any]              =						torch.zeros_like(
       hf_bort_model.bert.embeddings.token_type_embeddings.weight.data    )

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

      # self attention
      A							:					BertSelfAttention              =						layer.attention.self

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

      A							:					Union[str, Any]              =						check_and_map_params(
          self_attn.key.weight.data ,    f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""    )
      A							:					str              =						check_and_map_params(
          self_attn.query.bias.data ,    f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""    )
      A							:					Union[str, Any]              =						check_and_map_params(
          self_attn.query.weight.data ,    f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""    )
      A							:					Tuple              =						check_and_map_params(
          self_attn.value.bias.data ,    f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""    )
      A							:					Optional[int]              =						check_and_map_params(
          self_attn.value.weight.data ,    f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""    )

      # self attention output
      A							:					BertSelfOutput              =						layer.attention.output

      A							:					int              =						check_and_map_params(
          self_output.dense.bias ,    f"""encoder.transformer_cells.{i}.proj.bias"""    )
      A							:					Union[str, Any]              =						check_and_map_params(
          self_output.dense.weight ,    f"""encoder.transformer_cells.{i}.proj.weight"""    )
      A							:					Optional[Any]              =						check_and_map_params(
          self_output.LayerNorm.bias ,    f"""encoder.transformer_cells.{i}.layer_norm.beta"""    )
      A							:					Tuple              =						check_and_map_params(
          self_output.LayerNorm.weight ,    f"""encoder.transformer_cells.{i}.layer_norm.gamma"""    )

      # intermediate
      A							:					BertIntermediate              =						layer.intermediate

      A							:					Union[str, Any]              =						check_and_map_params(
          intermediate.dense.bias ,    f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""    )
      A							:					List[Any]              =						check_and_map_params(
          intermediate.dense.weight ,    f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""    )

      # output
      A							:					BertOutput              =						layer.output

      A							:					List[Any]              =						check_and_map_params(
          bert_output.dense.bias ,    f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""    )
      A							:					List[Any]              =						check_and_map_params(
          bert_output.dense.weight ,    f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""    )
      A							:					Optional[int]              =						check_and_map_params(
          bert_output.LayerNorm.bias ,    f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""    )
      A							:					Union[str, Any]              =						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							:					Dict              =						RobertaTokenizer.from_pretrained("roberta-base"    )

   A							:					Tuple              =						tokenizer.encode_plus(_lowerCamelCase    )["input_ids"]

   # Get gluon output
   A							:					Optional[Any]              =						mx.nd.array([input_ids]    )
   A							:					Any              =						original_bort(inputs=_lowerCamelCase ,    token_types=[]    )

   # Get Transformer output (save and reload model again)
   hf_bort_model.save_pretrained(_lowerCamelCase    )
   A							:					List[Any]              =						BertModel.from_pretrained(_lowerCamelCase    )
   hf_bort_model.eval()

   A							:					Optional[int]              =						tokenizer.encode_plus(_lowerCamelCase ,    return_tensors="pt"    )
   A							:					Tuple              =						hf_bort_model(**_lowerCamelCase    )[0]

   A							:					Tuple              =						output_gluon[0].asnumpy()
   A							:					Optional[Any]              =						output_hf[0].detach().numpy()

   A							:					Tuple              =						np.max(np.abs(hf_layer - gluon_layer    )    ).item()
   A							:					Optional[int]              =						np.allclose(_lowerCamelCase ,    _lowerCamelCase ,    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:" ,    _lowerCamelCase    )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE           =  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."""
       )
       __SCREAMING_SNAKE_CASE           =  parser.parse_args()
       convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			List[Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any]=3	,	__lowerCamelCase      :			Union[str, Any]=32	,	__lowerCamelCase      :			List[Any]=3	,	__lowerCamelCase      :			Optional[int]=10	,	__lowerCamelCase      :			Optional[Any]=[10, 20, 30, 40]	,	__lowerCamelCase      :			Tuple=[1, 1, 2, 1]	,	__lowerCamelCase      :			Optional[Any]=True	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			List[str]="relu"	,	__lowerCamelCase      :			Tuple=3	,	__lowerCamelCase      :			Union[str, Any]=None	,	)      ->				str:
									A							:					Optional[Any]              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					List[str]              =						image_size
									A							:					List[str]              =						num_channels
									A							:					Tuple              =						embeddings_size
									A							:					Optional[int]              =						hidden_sizes
									A							:					Dict              =						depths
									A							:					Optional[int]              =						is_training
									A							:					List[str]              =						use_labels
									A							:					List[Any]              =						hidden_act
									A							:					Optional[int]              =						num_labels
									A							:					int              =						scope
									A							:					List[Any]              =						len(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				List[Any]:
									A							:					str              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

									A							:					List[Any]              =						self.get_config()
									return config, pixel_values, labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									return RegNetConfig(
									    num_channels=self.num_channels	,	embeddings_size=self.embeddings_size	,	hidden_sizes=self.hidden_sizes	,	depths=self.depths	,	hidden_act=self.hidden_act	,	num_labels=self.num_labels	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Tuple:
									A							:					List[str]              =						TFRegNetModel(config=__lowerCamelCase )
									A							:					str              =						model(__lowerCamelCase	,	training=__lowerCamelCase )
									# 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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Tuple )      ->				List[str]:
									A							:					List[Any]              =						self.num_labels
									A							:					int              =						TFRegNetForImageClassification(__lowerCamelCase )
									A							:					str              =						model(__lowerCamelCase	,	labels=__lowerCamelCase	,	training=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Dict:
									A							:					Any              =						self.prepare_config_and_inputs()
									A       , A       , A							:					str              =						config_and_inputs
									A							:					Any              =						{"pixel_values": pixel_values}
									return config, inputs_dict







@require_tf
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						a__   =  (
						    {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Optional[Any]              =						TFRegNetModelTester(self )
									A							:					int              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Tuple:
									return







						@unittest.skip(reason="RegNet does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[Any]:
									pass







						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0	,	reason="TF does not support backprop for grouped convolutions on CPU."	,	)
						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									super().test_keras_fit()







						@unittest.skip(reason="RegNet does not support input and output embeddings" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Union[str, Any]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Tuple:
									A       , A							:					List[str]              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												A							:					int              =						inspect.signature(model.call )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A							:					Union[str, Any]              =						[*signature.parameters.keys()]

												A							:					Optional[int]              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Tuple:
									A							:					Tuple              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									def check_hidden_states_output(__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			str ):
												A							:					int              =						model_class(__lowerCamelCase )
												A							:					int              =						model(**self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )	,	training=__lowerCamelCase )

												A							:					Any              =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												A							:					Dict              =						self.model_tester.num_stages
												self.assertEqual(len(__lowerCamelCase )	,	expected_num_stages + 1 )

												# RegNet'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 // 2, self.model_tester.image_size // 2]	,	)

									A       , A							:					int              =						self.model_tester.prepare_config_and_inputs_for_common()
									A							:					str              =						["basic", "bottleneck"]
									for model_class in self.all_model_classes:
												for layer_type in layers_type:
															A							:					List[str]              =						layer_type
															A							:					List[Any]              =						True
															check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															A							:					Union[str, Any]              =						True

															check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Optional[Any]={} ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	return_dict=__lowerCamelCase	,	**__lowerCamelCase )
												A							:					int              =						model(__lowerCamelCase	,	return_dict=__lowerCamelCase	,	**__lowerCamelCase ).to_tuple()

												def recursive_check(__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Any ):
															if isinstance(__lowerCamelCase	,	(List, Tuple) ):
																		for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase	,	__lowerCamelCase ):
																					recursive_check(__lowerCamelCase	,	__lowerCamelCase )
															elif tuple_object is None:
																		return
															else:
																		self.assertTrue(
																		    all(tf.equal(__lowerCamelCase	,	__lowerCamelCase ) )	,	msg=(
																		        "Tuple and dict output are not equal. Difference:"
																		        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
																		    )	,	)

															recursive_check(__lowerCamelCase	,	__lowerCamelCase )

									for model_class in self.all_model_classes:
												A							:					Tuple              =						model_class(__lowerCamelCase )

												A							:					Optional[int]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

												A							:					Optional[int]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					List[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

												A							:					Optional[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												A							:					str              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	{"output_hidden_states": True} )

												A							:					Optional[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Tuple              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	{"output_hidden_states": True} )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					List[str]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Union[str, Any]              =						TFRegNetModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Dict              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image






@require_tf
@require_vision
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[Any]:
									return (
									    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
									    if is_vision_available()
									    else None
									)







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									A							:					List[Any]              =						TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

									A							:					Optional[int]              =						self.default_image_processor
									A							:					List[Any]              =						prepare_img()
									A							:					str              =						image_processor(images=__lowerCamelCase	,	return_tensors="tf" )

									# forward pass
									A							:					List[Any]              =						model(**__lowerCamelCase	,	training=__lowerCamelCase )

									# verify the logits
									A							:					Dict              =						tf.TensorShape((1, 10_00) )
									self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

									A							:					Optional[Any]              =						tf.constant([-0.4180, -1.5051, -3.4836] )

									tf.debugging.assert_near(outputs.logits[0, :3]	,	__lowerCamelCase	,	atol=1e-4 )