


"""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			):







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

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Any         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       snake_case					:   List[Any]         =					self.get_config()

       return config, pixel_values







   def 				lowerCamelCase	(			self		)			->	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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       snake_case					:   str         =					FlaxRegNetModel(config=UpperCamelCase__		)
       snake_case					:   str         =					model(UpperCamelCase__		)

       # 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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.num_labels
       snake_case					:   str         =					FlaxRegNetForImageClassification(config=UpperCamelCase__		)
       snake_case					:   str         =					model(UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)		)







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

       '''simple docstring'''




       snake_case					:   int         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case					:   Dict         =					config_and_inputs
       snake_case					:   Dict         =					{"pixel_values": pixel_values}
       return config, inputs_dict







@require_flax
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: Optional[int]													=						(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

   __UpperCAmelCase				: Any													=						False
   __UpperCAmelCase				: Union[str, Any]													=						False
   __UpperCAmelCase				: Tuple													=						False







   def 				lowerCamelCase	(			self		)			->	None:

       '''simple docstring'''




       snake_case					:   Any         =					FlaxRegNetModelTester(self		)
       snake_case					:   Tuple         =					ConfigTester(self		,			config_class=UpperCamelCase__		,			has_text_modality=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	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 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       return







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

       '''simple docstring'''




       snake_case					:   str         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Dict         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__		)







   @unittest.skip(reason="RegNet does not use inputs_embeds"		)
   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       pass







   @unittest.skip(reason="RegNet does not support input and output embeddings"		)
   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       pass







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case      ,snake_case					:   List[str]         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case					:   List[str]         =					model_class(UpperCamelCase__		)
           snake_case					:   List[str]         =					inspect.signature(model.__call__		)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           snake_case					:   Optional[Any]         =					[*signature.parameters.keys()]

           snake_case					:   Any         =					["pixel_values"]
           self.assertListEqual(arg_names[:1]		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       def check_hidden_states_output(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   str         =					model_class(UpperCamelCase__		)

           snake_case					:   Optional[int]         =					model(**self._prepare_for_class(UpperCamelCase__		,			UpperCamelCase__		)		)

           snake_case					:   int         =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           snake_case					:   Optional[Any]         =					self.model_tester.num_stages
           self.assertEqual(len(UpperCamelCase__		)		,			expected_num_stages + 1		)

       snake_case      ,snake_case					:   Any         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case					:   Tuple         =					True
           check_hidden_states_output(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)

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

           check_hidden_states_output(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case      ,snake_case					:   Union[str, Any]         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__		):
               snake_case					:   str         =					self._prepare_for_class(UpperCamelCase__		,			UpperCamelCase__		)
               snake_case					:   str         =					model_class(UpperCamelCase__		)

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

               with self.subTest("JIT Enabled"		):
                   snake_case					:   Dict         =					model_jitted(**UpperCamelCase__		).to_tuple()

               with self.subTest("JIT Disabled"		):
                   with jax.disable_jit():
                       snake_case					:   Union[str, Any]         =					model_jitted(**UpperCamelCase__		).to_tuple()

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







def 						__lowerCAmelCase		(		)			->		Tuple:


    """simple docstring"""





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







@require_flax
class _lowerCAmelCase       (   unittest.TestCase			):







   @cached_property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




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







   @slow
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

       snake_case					:   Optional[Any]         =					self.default_image_processor
       snake_case					:   Tuple         =					prepare_img()
       snake_case					:   Dict         =					image_processor(images=UpperCamelCase__		,			return_tensors="np"		)

       snake_case					:   Tuple         =					model(**UpperCamelCase__		)

       # verify the logits
       snake_case					:   int         =					(1, 1000)
       self.assertEqual(outputs.logits.shape		,			UpperCamelCase__		)

       snake_case					:   Dict         =					jnp.array([-0.4180, -1.5051, -3.4836]		)

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









"""simple docstring"""



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizer
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizerFast
   __UpperCAmelCase				: List[Any]													=						True
   __UpperCAmelCase				: Dict													=						True







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       super().setUp()

       snake_case					:   Dict         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   List[str]         =					os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["vocab_file"]		)
       with open(self.vocab_file		,			"w"		,			encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)







   def 				lowerCamelCase	(			self		,			**UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   str         =					"UNwant\u00E9d,running"
       snake_case					:   Optional[int]         =					"unwanted, running"
       return input_text, output_text







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.tokenizer_class(self.vocab_file		)

       snake_case					:   Optional[Any]         =					tokenizer.tokenize("UNwant\u00E9d,running"		)
       self.assertListEqual(UpperCamelCase__		,			["un", "##want", "##ed", ",", "runn", "##ing"]		)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)		,			[7, 4, 5, 10, 8, 9]		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass





