





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
snake_case 				=       logging.get_logger(__name__)

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Dict:


  if "resnet-50" in model_name:
    _lowercase							= ResNetConfig.from_pretrained('microsoft/resnet-50'     )
  elif "resnet-101" in model_name:
    _lowercase							= ResNetConfig.from_pretrained('microsoft/resnet-101'     )
  else:
    raise ValueError('Model name should include either resnet50 or resnet101'     )

  _lowercase							= DetrConfig(use_timm_backbone=__lowerCAmelCase			,							backbone_config=__lowerCAmelCase     )

  # set label attributes
  _lowercase							= 'panoptic' in model_name
  if is_panoptic:
    _lowercase							= 250
  else:
    _lowercase							= 91
    _lowercase							= 'huggingface/label-files'
    _lowercase							= 'coco-detection-id2label.json'
    _lowercase							= json.load(open(hf_hub_download(__lowerCAmelCase			,							__lowerCAmelCase			,							repo_type='dataset'     )			,							'r'     )     )
    _lowercase							= {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}
    _lowercase							= idalabel
    _lowercase							= {v: k for k, v in idalabel.items()}

  return config, is_panoptic

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]     )      ->						Optional[Any]:
  _lowercase							= []

  # stem
  # fmt: off
  rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')     )
  rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')     )
  rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')     )
  rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')     )
  rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')     )
  # stages
  for stage_idx in range(len(config.backbone_config.depths     )     ):
    for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
            )     )
      # 3 convs
      for i in range(3     ):
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
            )     )
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
            )     )
    # fmt: on

  for i in range(config.encoder_layers     ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (
            F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
            F"""encoder.layers.{i}.self_attn.out_proj.weight""",
        )     )
    rename_keys.append(
        (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")     )
    rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")     )
    rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")     )
    rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")     )
    rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")     )
    rename_keys.append(
        (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")     )
    rename_keys.append(
        (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")     )
    rename_keys.append(
        (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")     )
    rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")     )
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (
            F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
            F"""decoder.layers.{i}.self_attn.out_proj.weight""",
        )     )
    rename_keys.append(
        (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")     )
    rename_keys.append(
        (
            F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
            F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
        )     )
    rename_keys.append(
        (
            F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
            F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
        )     )
    rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")     )
    rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")     )
    rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")     )
    rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")     )
    rename_keys.append(
        (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")     )
    rename_keys.append(
        (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")     )
    rename_keys.append(
        (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")     )
    rename_keys.append(
        (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")     )
    rename_keys.append(
        (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")     )
    rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")     )

  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  rename_keys.extend(
      [
          ('input_proj.weight', 'input_projection.weight'),
          ('input_proj.bias', 'input_projection.bias'),
          ('query_embed.weight', 'query_position_embeddings.weight'),
          ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
          ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
          ('class_embed.weight', 'class_labels_classifier.weight'),
          ('class_embed.bias', 'class_labels_classifier.bias'),
          ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
          ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
          ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
          ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
          ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
          ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
      ]     )

  return rename_keys

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :Tuple			,							snake_case__  :List[Any]     )      ->						List[str]:
  _lowercase							= state_dict.pop(__lowerCAmelCase     )
  _lowercase							= val

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :Dict=False     )      ->						Dict:
  _lowercase							= ''
  if is_panoptic:
    _lowercase							= 'detr.'

  # first: transformer encoder
  for i in range(6     ):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    _lowercase							= state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
    _lowercase							= state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""     )
    # next, add query, keys and values (in that order) to the state dict
    _lowercase							= in_proj_weight[:256, :]
    _lowercase							= in_proj_bias[:256]
    _lowercase							= in_proj_weight[256:512, :]
    _lowercase							= in_proj_bias[256:512]
    _lowercase							= in_proj_weight[-256:, :]
    _lowercase							= in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6     ):
    # read in weights + bias of input projection layer of self-attention
    _lowercase							= state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
    _lowercase							= state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""     )
    # next, add query, keys and values (in that order) to the state dict
    _lowercase							= in_proj_weight[:256, :]
    _lowercase							= in_proj_bias[:256]
    _lowercase							= in_proj_weight[256:512, :]
    _lowercase							= in_proj_bias[256:512]
    _lowercase							= in_proj_weight[-256:, :]
    _lowercase							= in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    _lowercase							= state_dict.pop(
        F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""     )
    _lowercase							= state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""     )
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    _lowercase							= in_proj_weight_cross_attn[:256, :]
    _lowercase							= in_proj_bias_cross_attn[:256]
    _lowercase							= in_proj_weight_cross_attn[256:512, :]
    _lowercase							= in_proj_bias_cross_attn[256:512]
    _lowercase							= in_proj_weight_cross_attn[-256:, :]
    _lowercase							= in_proj_bias_cross_attn[-256:]

def 							SCREAMING_SNAKE_CASE__       (  )      ->						str:
  _lowercase							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase							= Image.open(requests.get(__lowerCAmelCase			,							stream=__lowerCAmelCase     ).raw     )

  return im

@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :Tuple=None			,							snake_case__  :str=False     )      ->						List[Any]:
  _lowercase						,		_lowercase							= get_detr_config(__lowerCAmelCase     )

  # load original model from torch hub
  _lowercase							= {
      'detr-resnet-50': 'detr_resnet50',
      'detr-resnet-101': 'detr_resnet101',
  }
  logger.info(F"""Converting model {model_name}..."""     )
  _lowercase							= torch.hub.load('facebookresearch/detr'			,							model_name_to_original_name[model_name]			,							pretrained=__lowerCAmelCase     ).eval()
  _lowercase							= detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(__lowerCAmelCase     ):
    if is_panoptic:
      _lowercase							= 'detr.' + src
    rename_key(__lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase     )
  # query, key and value matrices need special treatment
  read_in_q_k_v(__lowerCAmelCase			,							is_panoptic=__lowerCAmelCase     )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  _lowercase							= 'detr.model.' if is_panoptic else 'model.'
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('detr'     )
          and not key.startswith('class_labels_classifier'     )
          and not key.startswith('bbox_predictor'     )
      ):
        _lowercase							= state_dict.pop(__lowerCAmelCase     )
        _lowercase							= val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        _lowercase							= state_dict.pop(__lowerCAmelCase     )
        _lowercase							= val
      elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
        continue
      else:
        _lowercase							= state_dict.pop(__lowerCAmelCase     )
        _lowercase							= val
    else:
      if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
        _lowercase							= state_dict.pop(__lowerCAmelCase     )
        _lowercase							= val

    # finally, create HuggingFace model and load state dict
  _lowercase							= DetrForSegmentation(__lowerCAmelCase     ) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase     )
  model.load_state_dict(__lowerCAmelCase     )
  model.eval()

  # verify our conversion on an image
  _lowercase							= 'coco_panoptic' if is_panoptic else 'coco_detection'
  _lowercase							= DetrImageProcessor(format=__lowerCAmelCase     )

  _lowercase							= processor(images=prepare_img()			,							return_tensors='pt'     )
  _lowercase							= encoding['pixel_values']

  _lowercase							= detr(__lowerCAmelCase     )
  _lowercase							= model(__lowerCAmelCase     )

  assert torch.allclose(outputs.logits			,							original_outputs['pred_logits']			,							atol=1E-3     )
  assert torch.allclose(outputs.pred_boxes			,							original_outputs['pred_boxes']			,							atol=1E-3     )
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks			,							original_outputs['pred_masks']			,							atol=1E-4     )
  print('Looks ok!'     )

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""     )
    Path(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
    model.save_pretrained(__lowerCAmelCase     )
    processor.save_pretrained(__lowerCAmelCase     )

  if push_to_hub:
    # Upload model and image processor to the hub
    logger.info('Uploading PyTorch model and image processor to the hub...'     )
    model.push_to_hub(F"""nielsr/{model_name}"""     )
    processor.push_to_hub(F"""nielsr/{model_name}"""     )


if __name__ == "__main__":
      snake_case 				=       argparse.ArgumentParser()

      parser.add_argument(
          """--model_name""",
          default="""detr-resnet-50""",
          type=str,
          choices=["""detr-resnet-50""", """detr-resnet-101"""],
          help="""Name of the DETR model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
      )
      parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
      snake_case 				=       parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





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







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         _lowercase							= self.get_config()

         return config, pixel_values







       def    __UpperCAmelCase (    self	:	Any  )						->					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						,image_size=self.image_size						,)







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Any						,__A	:	Union[str, Any]  )						->					List[Any]:
         _lowercase							= FlaxRegNetModel(config=__A  )
         _lowercase							= model(__A  )

         # Output shape (b, c, h, w)
         self.parent.assertEqual(
             result.last_hidden_state.shape						,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,)







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Optional[int]						,__A	:	Dict  )						->					Any:
         _lowercase							= self.num_labels
         _lowercase							= FlaxRegNetForImageClassification(config=__A  )
         _lowercase							= model(__A  )
         self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase							= config_and_inputs
         _lowercase							= {'pixel_values': pixel_values}
         return config, inputs_dict





@require_flax
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Tuple      =  (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  False
       SCREAMING_SNAKE_CASE_      : Any      =  False
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  False







       def    __UpperCAmelCase (    self	:	Dict  )						->					None:
         _lowercase							= FlaxRegNetModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=__A						,has_text_modality=__A  )







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







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__A  )







       @unittest.skip(reason='RegNet does not use inputs_embeds'  )
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:
         pass







       @unittest.skip(reason='RegNet does not support input and output embeddings'  )
       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         pass







       def    __UpperCAmelCase (    self	:	Dict  )						->					List[str]:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= model_class(__A  )
           _lowercase							= inspect.signature(model.__call__  )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowercase							= [*signature.parameters.keys()]

           _lowercase							= ['pixel_values']
           self.assertListEqual(arg_names[:1]						,__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[str]:
         def check_hidden_states_output(__A	:	Optional[Any]						,__A	:	Optional[int]						,__A	:	str  ):
           _lowercase							= model_class(__A  )

           _lowercase							= model(**self._prepare_for_class(__A						,__A  )  )

           _lowercase							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase							= self.model_tester.num_stages
           self.assertEqual(len(__A  )						,expected_num_stages + 1  )

         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= True
           check_hidden_states_output(__A						,__A						,__A  )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase							= True

           check_hidden_states_output(__A						,__A						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					int:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
             _lowercase							= self._prepare_for_class(__A						,__A  )
             _lowercase							= model_class(__A  )

             @jax.jit
             def model_jitted(__A	:	Any						,**__A	:	Union[str, Any]  ):
               return model(pixel_values=__A						,**__A  )

             with self.subTest('JIT Enabled'  ):
               _lowercase							= model_jitted(**__A  ).to_tuple()

             with self.subTest('JIT Disabled'  ):
               with jax.disable_jit():
                 _lowercase							= model_jitted(**__A  ).to_tuple()

             self.assertEqual(len(__A  )						,len(__A  )  )
             for jitted_output, output in zip(__A						,__A  ):
               self.assertEqual(jitted_output.shape						,output.shape  )

def 							SCREAMING_SNAKE_CASE__       (  )      ->						str:
  _lowercase							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
  return image







@require_flax
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @cached_property
       def    __UpperCAmelCase (    self	:	str  )						->					Any:
         return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'  ) if is_vision_available() else None







       @slow
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'  )

         _lowercase							= self.default_image_processor
         _lowercase							= prepare_img()
         _lowercase							= image_processor(images=__A						,return_tensors='np'  )

         _lowercase							= model(**__A  )

         # verify the logits
         _lowercase							= (1, 1000)
         self.assertEqual(outputs.logits.shape						,__A  )

         _lowercase							= jnp.array([-0.4180, -1.5051, -3.4836]  )

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