


'''simple docstring'''



import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowercase    :    Dict			     =  []
for i in range(6):
 # 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}.cross_attn.out_proj.weight""",
         F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
     )
 )
 rename_keys.append(
     (
         F"""transformer.decoder.layers.{i}.cross_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"""))

 # q, k, v projections in self/cross-attention in decoder for conditional DETR
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
 )
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
 )
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
 )
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
 )
 rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
 )
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
 )
 rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
 )

 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
 )
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
 )
 rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
 rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
 rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
 )
 rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
 rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
 rename_keys.append(
     (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
 )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)


def    lowerCamelCase__   ( A     :    Any   ,     A     :    Optional[Any]   ,     A     :    List[Any]  ):




      '''simple docstring'''






      UpperCAmelCase         =			state_dict.pop(A  )
      UpperCAmelCase         =			val


def    lowerCamelCase__   ( A     :    int  ):




      '''simple docstring'''






      UpperCAmelCase         =			OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  UpperCAmelCase         =			key.replace('''backbone.0.body'''   ,     '''backbone.conv_encoder.model'''  )
                  UpperCAmelCase         =			value
            else:
                  UpperCAmelCase         =			value

      return new_state_dict


def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    List[str]=False  ):




      '''simple docstring'''






      UpperCAmelCase         =			''''''
      if is_panoptic:
            UpperCAmelCase         =			'''conditional_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)
            UpperCAmelCase         =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""  )
            UpperCAmelCase         =			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
            UpperCAmelCase         =			in_proj_weight[:2_56, :]
            UpperCAmelCase         =			in_proj_bias[:2_56]
            UpperCAmelCase         =			in_proj_weight[2_56:5_12, :]
            UpperCAmelCase         =			in_proj_bias[2_56:5_12]
            UpperCAmelCase         =			in_proj_weight[-2_56:, :]
            UpperCAmelCase         =			in_proj_bias[-2_56:]


def    lowerCamelCase__   ( ):




      '''simple docstring'''






      UpperCAmelCase         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCAmelCase         =			Image.open(requests.get(A   ,     stream=A  ).raw  )

      return im


@torch.no_grad()
def    lowerCamelCase__   ( A     :    int   ,     A     :    Any  ):




      '''simple docstring'''






      UpperCAmelCase         =			ConditionalDetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            UpperCAmelCase         =			'''resnet101'''
      if "dc5" in model_name:
            UpperCAmelCase         =			True
      UpperCAmelCase         =			'''panoptic''' in model_name
      if is_panoptic:
            UpperCAmelCase         =			2_50
      else:
            UpperCAmelCase         =			91
            UpperCAmelCase         =			'''huggingface/label-files'''
            UpperCAmelCase         =			'''coco-detection-id2label.json'''
            UpperCAmelCase         =			json.load(open(hf_hub_download(A   ,     A   ,     repo_type='''dataset'''  )   ,     '''r'''  )  )
            UpperCAmelCase         =			{int(A  ): v for k, v in idalabel.items()}
            UpperCAmelCase         =			idalabel
            UpperCAmelCase         =			{v: k for k, v in idalabel.items()}

      # load image processor
      UpperCAmelCase         =			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
      UpperCAmelCase         =			ConditionalDetrImageProcessor(format=A  )

      # prepare image
      UpperCAmelCase         =			prepare_img()
      UpperCAmelCase         =			image_processor(images=A   ,     return_tensors='''pt'''  )
      UpperCAmelCase         =			encoding['''pixel_values''']

      logger.info(f"""Converting model {model_name}..."""  )

      # load original model from torch hub
      UpperCAmelCase         =			torch.hub.load('''DeppMeng/ConditionalDETR'''   ,     A   ,     pretrained=A  ).eval()
      UpperCAmelCase         =			conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  UpperCAmelCase         =			'''conditional_detr.''' + src
            rename_key(A   ,     A   ,     A  )
      UpperCAmelCase         =			rename_backbone_keys(A  )
      # query, key and value matrices need special treatment
      read_in_q_k_v(A   ,     is_panoptic=A  )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      UpperCAmelCase         =			'''conditional_detr.model.''' if is_panoptic else '''model.'''
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith('''conditional_detr'''  )
                      and not key.startswith('''class_labels_classifier'''  )
                      and not key.startswith('''bbox_predictor'''  )
                  ):
                        UpperCAmelCase         =			state_dict.pop(A  )
                        UpperCAmelCase         =			val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        UpperCAmelCase         =			state_dict.pop(A  )
                        UpperCAmelCase         =			val
                  elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
                        continue
                  else:
                        UpperCAmelCase         =			state_dict.pop(A  )
                        UpperCAmelCase         =			val
            else:
                  if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
                        UpperCAmelCase         =			state_dict.pop(A  )
                        UpperCAmelCase         =			val
                        # finally, create HuggingFace model and load state dict
      UpperCAmelCase         =			ConditionalDetrForSegmentation(A  ) if is_panoptic else ConditionalDetrForObjectDetection(A  )
      model.load_state_dict(A  )
      model.eval()
      model.push_to_hub(repo_id=A   ,     organization='''DepuMeng'''   ,     commit_message='''Add model'''  )
      # verify our conversion
      UpperCAmelCase         =			conditional_detr(A  )
      UpperCAmelCase         =			model(A  )
      assert torch.allclose(outputs.logits   ,     original_outputs['''pred_logits''']   ,     atol=1E-4  )
      assert torch.allclose(outputs.pred_boxes   ,     original_outputs['''pred_boxes''']   ,     atol=1E-4  )
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks   ,     original_outputs['''pred_masks''']   ,     atol=1E-4  )

      # Save model and image processor
      logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""  )
      Path(A  ).mkdir(exist_ok=A  )
      model.save_pretrained(A  )
      image_processor.save_pretrained(A  )


if __name__ == "__main__":
 _lowercase    :    Optional[int]			     =  argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     default="""conditional_detr_resnet50""",
     type=str,
     help="""Name of the CONDITIONAL_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."""
 )
 _lowercase    :    Tuple			     =  parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''



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  UpperCamelCase__(     unittest.TestCase ):




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				List[Any]=3   ,					lowerCAmelCase    :				Tuple=32   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				Optional[Any]=10   ,					lowerCAmelCase    :				Optional[Any]=[10, 20, 30, 40]   ,					lowerCAmelCase    :				Optional[int]=[1, 1, 2, 1]   ,					lowerCAmelCase    :				Union[str, Any]=True   ,					lowerCAmelCase    :				Tuple=True   ,					lowerCAmelCase    :				List[Any]="relu"   ,					lowerCAmelCase    :				List[Any]=3   ,					lowerCAmelCase    :				Union[str, Any]=None   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			embeddings_size
										UpperCAmelCase         =			hidden_sizes
										UpperCAmelCase         =			depths
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			num_labels
										UpperCAmelCase         =			scope
										UpperCAmelCase         =			len(lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values




				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										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   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Any   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetModel(config=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )

										# 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   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Optional[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.num_labels
										UpperCAmelCase         =			FlaxRegNetForImageClassification(config=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )





				def   a__(	self    :				List[str]   )->  Any:

										"""simple docstring"""




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







@require_flax
class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Optional[int]							= (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				__magic_name__       :   Optional[int]							= False
				__magic_name__       :   List[str]							= False
				__magic_name__       :   Dict							= False




				def   a__(	self    :				Union[str, Any]   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  List[str]:

										"""simple docstring"""




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										return




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )




				@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
				def   a__(	self    :				Any   )->  List[str]:

										"""simple docstring"""




										pass




				def   a__(	self    :				Any   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

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

																UpperCAmelCase         =			['''pixel_values''']
																self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase   )




				def   a__(	self    :				Tuple   )->  int:

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				int   ):
																UpperCAmelCase         =			model_class(lowerCAmelCase   )

																UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )

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

																UpperCAmelCase         =			self.model_tester.num_stages
																self.assertEqual(len(lowerCAmelCase   )   ,					expected_num_stages + 1   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

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

																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )





				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__   ):
																						UpperCAmelCase         =			self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )
																						UpperCAmelCase         =			model_class(lowerCAmelCase   )

																						@jax.jit
																						def model_jitted(lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				Tuple   ):
																												return model(pixel_values=lowerCAmelCase   ,					**lowerCAmelCase   )

																						with self.subTest('''JIT Enabled'''   ):
																												UpperCAmelCase         =			model_jitted(**lowerCAmelCase   ).to_tuple()

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

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







def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
						return image






@require_flax
class  UpperCamelCase__(     unittest.TestCase ):




				@cached_property
				def   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''   ) if is_vision_available() else None





				@slow
				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''   )

										UpperCAmelCase         =			self.default_image_processor
										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''np'''   )

										UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			(1, 1000)
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )

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

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





