
"""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 A_	(unittest.TestCase      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=3				,   lowercase_=32				,   lowercase_=3				,   lowercase_=10				,   lowercase_=[10, 20, 30, 40]				,   lowercase_=[1, 1, 2, 1]				,   lowercase_=True				,   lowercase_=True				,   lowercase_="relu"				,   lowercase_=3				,   lowercase_=None				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    parent
     UpperCAmelCase_					:					Tuple			     =    batch_size
     UpperCAmelCase_					:					List[Any]			     =    image_size
     UpperCAmelCase_					:					Optional[int]			     =    num_channels
     UpperCAmelCase_					:					Union[str, Any]			     =    embeddings_size
     UpperCAmelCase_					:					str			     =    hidden_sizes
     UpperCAmelCase_					:					List[Any]			     =    depths
     UpperCAmelCase_					:					List[str]			     =    is_training
     UpperCAmelCase_					:					Optional[int]			     =    use_labels
     UpperCAmelCase_					:					List[str]			     =    hidden_act
     UpperCAmelCase_					:					Tuple			     =    num_labels
     UpperCAmelCase_					:					Dict			     =    scope
     UpperCAmelCase_					:					Tuple			     =    len(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     UpperCAmelCase_					:					List[str]			     =    self.get_config()

     return config, pixel_values







  def        UpperCamelCase__					(		self  ):

     """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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    FlaxRegNetModel(config=lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    model(lowercase_  )

     # 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				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.num_labels
     UpperCAmelCase_					:					str			     =    FlaxRegNetForImageClassification(config=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.prepare_config_and_inputs()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    config_and_inputs
     UpperCAmelCase_					:					List[Any]			     =    {"pixel_values": pixel_values}
     return config, inputs_dict




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




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

  SCREAMING_SNAKE_CASE__			:						Any       =		False
  SCREAMING_SNAKE_CASE__			:						int       =		False
  SCREAMING_SNAKE_CASE__			:						Tuple       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    FlaxRegNetModelTester(self  )
     UpperCAmelCase_					:					str			     =    ConfigTester(self				,   config_class=lowercase_				,   has_text_modality=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_  )







  @unittest.skip(reason="RegNet does not use inputs_embeds"  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  @unittest.skip(reason="RegNet does not support input and output embeddings"  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					Union[str, Any]			     =    model_class(lowercase_  )
        UpperCAmelCase_					:					str			     =    inspect.signature(model.__call__  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCAmelCase_					:					Tuple			     =    [*signature.parameters.keys()]

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     def check_hidden_states_output(lowercase_				,   lowercase_				,   lowercase_  ):
        UpperCAmelCase_					:					Optional[int]			     =    model_class(lowercase_  )

        UpperCAmelCase_					:					List[str]			     =    model(**self._prepare_for_class(lowercase_				,   lowercase_  )  )

        UpperCAmelCase_					:					Tuple			     =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        UpperCAmelCase_					:					int			     =    self.model_tester.num_stages
        self.assertEqual(len(lowercase_  )				,   expected_num_stages + 1  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					int			     =    True
        check_hidden_states_output(lowercase_				,   lowercase_				,   lowercase_  )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCAmelCase_					:					Optional[Any]			     =    True

        check_hidden_states_output(lowercase_				,   lowercase_				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					Optional[int]			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
           UpperCAmelCase_					:					Dict			     =    model_class(lowercase_  )

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

           with self.subTest("JIT Enabled"  ):
              UpperCAmelCase_					:					Dict			     =    model_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					int			     =    model_jitted(**lowercase_  ).to_tuple()

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







def 	__a     (		):
   UpperCAmelCase_					:					Optional[int]			     =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   return image


@require_flax
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  @cached_property
  def        UpperCamelCase__					(		self  ):

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







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"  )

     UpperCAmelCase_					:					str			     =    self.default_image_processor
     UpperCAmelCase_					:					Optional[Any]			     =    prepare_img()
     UpperCAmelCase_					:					Union[str, Any]			     =    image_processor(images=lowercase_				,   return_tensors="np"  )

     UpperCAmelCase_					:					str			     =    model(**lowercase_  )

     # verify the logits
     UpperCAmelCase_					:					Tuple			     =    (1, 1000)
     self.assertEqual(outputs.logits.shape				,   lowercase_  )

     UpperCAmelCase_					:					List[str]			     =    jnp.array([-0.41_80, -1.50_51, -3.48_36]  )

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






"""simple docstring"""






import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


UpperCAmelCase    :     str   						=		{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}





class      lowerCamelCase__   (     nn.Module				):







					"""simple docstring"""


					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase   :						Union[str, Any]      							=					torchvision.models.resnetaaa(pretrained=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					list(model.children()  )[:-2]
												__UpperCAmelCase   :						Tuple      							=					nn.Sequential(*UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					self.pool(self.model(UpperCamelCase  )  )
												__UpperCAmelCase   :						str      							=					torch.flatten(UpperCamelCase ,       start_dim=2  )
												__UpperCAmelCase   :						Union[str, Any]      							=					out.transpose(1 ,       2  ).contiguous()
												return out  # BxNx2048






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Any ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					[json.loads(UpperCamelCase  ) for l in open(UpperCamelCase  )]
												__UpperCAmelCase   :						Optional[Any]      							=					os.path.dirname(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					tokenizer
												__UpperCAmelCase   :						List[str]      							=					labels
												__UpperCAmelCase   :						Optional[int]      							=					len(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					max_seq_length

												__UpperCAmelCase   :						Tuple      							=					transforms






					def __len__(       self    : Tuple  ):
												'''simple docstring'''






												return len(self.data  )






					def __getitem__(       self    : int ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,       add_special_tokens=UpperCamelCase  )  )
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					sentence[0], sentence[1:-1], sentence[-1]
												__UpperCAmelCase   :						Union[str, Any]      							=					sentence[: self.max_seq_length]

												__UpperCAmelCase   :						Tuple      							=					torch.zeros(self.n_classes  )
												__UpperCAmelCase   :						str      							=					1

												__UpperCAmelCase   :						List[str]      							=					Image.open(os.path.join(self.data_dir ,       self.data[index]["""img"""]  )  ).convert("""RGB"""  )
												__UpperCAmelCase   :						Any      							=					self.transforms(UpperCamelCase  )

												return {
												    "image_start_token": start_token,
												    "image_end_token": end_token,
												    "sentence": sentence,
												    "image": image,
												    "label": label,
												}






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					Counter()
												for row in self.data:
																			label_freqs.update(row["""label"""]  )
												return label_freqs






def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					[len(row["""sentence"""]							) for row in batch]
							__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					len(_UpperCamelCase							), max(_UpperCamelCase							)

							__UpperCAmelCase   :						int      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)
							__UpperCAmelCase   :						Any      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)

							for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase       ,			_UpperCamelCase							)							):
														__UpperCAmelCase   :						List[str]      							=					input_row["""sentence"""]
														__UpperCAmelCase   :						Optional[Any]      							=					1

							__UpperCAmelCase   :						int      							=					torch.stack([row["""image"""] for row in batch]							)
							__UpperCAmelCase   :						Optional[int]      							=					torch.stack([row["""label"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_start_token"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_end_token"""] for row in batch]							)

							return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def      lowerCamelCase       (     )		->       List[str]:

							'''simple docstring'''



							return [
							    "Crime",
							    "Drama",
							    "Thriller",
							    "Action",
							    "Comedy",
							    "Romance",
							    "Documentary",
							    "Short",
							    "Mystery",
							    "History",
							    "Family",
							    "Adventure",
							    "Fantasy",
							    "Sci-Fi",
							    "Western",
							    "Horror",
							    "Sport",
							    "War",
							    "Music",
							    "Musical",
							    "Animation",
							    "Biography",
							    "Film-Noir",
							]
def      lowerCamelCase       (     )		->       Dict:

							'''simple docstring'''



							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6							),
							        transforms.CenterCrop(2_2_4							),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]       ,			std=[0.12_221_994, 0.12_145_835, 0.14_380_469]       ,			),
							    ]							)
