





'''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 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





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

            '''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 snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''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 snake_case__		(				self  :						str	)       ->	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 snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Tuple	)       ->	Optional[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 snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						List[str]	)       ->	Tuple:

            '''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 snake_case__		(				self  :						int	)       ->	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 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[str]									=       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       _snake_case      :	Any									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	Optional[int]									=       False





       def snake_case__		(				self  :						str	)       ->	None:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__	)





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

            '''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 snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            return





       def snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''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 snake_case__		(				self  :						Dict	)       ->	Dict:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						Dict	)       ->	Optional[int]:

            '''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 snake_case__		(				self  :						Tuple	)       ->	Union[str, Any]:

            '''simple docstring'''




            def check_hidden_states_output(lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Optional[Any]	):
                 _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 snake_case__		(				self  :						Dict	)       ->	Optional[int]:

            '''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__  :						List[str]			,					**lowerCAmelCase__  :						Optional[int]	):
                           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    a__							(							)						->						Union[str, Any]:





     """simple docstring"""

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




@require_flax
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						Tuple	)       ->	Dict:

            '''simple docstring'''




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







       @slow
       def snake_case__		(				self  :						Tuple	)       ->	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	)	)









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_      :  str				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  Any				     =  {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       str   							=					'''transfo-xl'''
      __magic_name__  :       List[str]   							=					['''mems''']
      __magic_name__  :       Dict   							=					{
          '''n_token''': '''vocab_size''',
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }






      def __init__( self   :  Union[str, Any]						,						lowercase__   :  List[Any]=26_7735						,						lowercase__   :  Optional[Any]=[2_0000, 4_0000, 20_0000]						,						lowercase__   :  Optional[Any]=1024						,						lowercase__   :  str=1024						,						lowercase__   :  Any=16						,						lowercase__   :  int=64						,						lowercase__   :  str=4096						,						lowercase__   :  Union[str, Any]=4						,						lowercase__   :  List[Any]=False						,						lowercase__   :  List[Any]=18						,						lowercase__   :  str=1600						,						lowercase__   :  str=1000						,						lowercase__   :  Any=True						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  Union[str, Any]=0						,						lowercase__   :  str=-1						,						lowercase__   :  int=True						,						lowercase__   :  str=0.1						,						lowercase__   :  Optional[Any]=0.0						,						lowercase__   :  Tuple=True						,						lowercase__   :  Optional[int]="normal"						,						lowercase__   :  str=0.01						,						lowercase__   :  List[str]=0.01						,						lowercase__   :  Union[str, Any]=0.02						,						lowercase__   :  str=1e-5						,						lowercase__   :  Any=0						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  vocab_size
           a_     :							Optional[int]     						=  []
           self.cutoffs.extend(lowercase__       )
           if proj_share_all_but_first:
                a_     :							Any     						=  [False] + [True] * len(self.cutoffs       )
           else:
                a_     :							Tuple     						=  [False] + [False] * len(self.cutoffs       )
           a_     :							Tuple     						=  d_model
           a_     :							Optional[int]     						=  d_embed
           a_     :							List[Any]     						=  d_head
           a_     :							List[str]     						=  d_inner
           a_     :							Tuple     						=  div_val
           a_     :							Dict     						=  pre_lnorm
           a_     :							Optional[Any]     						=  n_layer
           a_     :							Dict     						=  n_head
           a_     :							Any     						=  mem_len
           a_     :							Union[str, Any]     						=  same_length
           a_     :							Dict     						=  attn_type
           a_     :							List[str]     						=  clamp_len
           a_     :							str     						=  sample_softmax
           a_     :							Any     						=  adaptive
           a_     :							List[Any]     						=  dropout
           a_     :							str     						=  dropatt
           a_     :							Dict     						=  untie_r
           a_     :							Tuple     						=  init
           a_     :							Optional[int]     						=  init_range
           a_     :							List[Any]     						=  proj_init_std
           a_     :							Optional[int]     						=  init_std
           a_     :							int     						=  layer_norm_epsilon
           super().__init__(eos_token_id=lowercase__						,						**lowercase__       )






      @property
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."       )
           return -1






      @max_position_embeddings.setter
      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"The model {self.model_type} is one of the few models that has no sequence length limit."       )
