





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       ):



					'''simple docstring'''

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




											'''simple docstring'''


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






					def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Tuple:




											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE		=						self.get_config()

											return config, pixel_values






					def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Optional[int]:




											'''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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Union[str, Any]	)					->		Any:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						FlaxRegNetModel(config=lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Optional[Any]	)					->		Tuple:




											'''simple docstring'''


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






					def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		int:




											'''simple docstring'''


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

@require_flax
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



					'''simple docstring'''

					__snake_case   :     Union[str, Any]		     =      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

					__snake_case   :     Tuple		     =      False
					__snake_case   :     int		     =      False
					__snake_case   :     Tuple		     =      False






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




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						FlaxRegNetModelTester(self	)
											SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,has_text_modality=lowerCamelCase__	)






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




											'''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     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[str]:




											'''simple docstring'''


											return






					def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowerCamelCase__	)






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




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




											'''simple docstring'''


											pass






					@unittest.skip(reason="""RegNet does not support input and output embeddings"""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




											'''simple docstring'''


											pass






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




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

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

																	SCREAMING_SNAKE_CASE		=						["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]						,lowerCamelCase__	)






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




											'''simple docstring'''


											def check_hidden_states_output(lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]	):
																	SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

																	SCREAMING_SNAKE_CASE		=						model(**self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)	)

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

																	SCREAMING_SNAKE_CASE		=						self.model_tester.num_stages
																	self.assertEqual(len(lowerCamelCase__	)						,expected_num_stages + 1	)

											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE		=						True
																	check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)

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

																	check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__	):
																							SCREAMING_SNAKE_CASE		=						self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)
																							SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

																							@jax.jit
																							def model_jitted(lowerCamelCase__						:   Dict						,**lowerCamelCase__						:   Optional[Any]	):
																													return model(pixel_values=lowerCamelCase__						,**lowerCamelCase__	)

																							with self.subTest("""JIT Enabled"""	):
																													SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

																							with self.subTest("""JIT Disabled"""	):
																													with jax.disable_jit():
																																			SCREAMING_SNAKE_CASE		=						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        __lowercase       (				)			->       Union[str, Any]:


						'''simple docstring'''


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



@require_flax
class 		UpperCamelCase__			(				unittest.TestCase       ):



					'''simple docstring'''

					@cached_property
					def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		str:




											'''simple docstring'''


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






					@slow
					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[str]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""	)

											SCREAMING_SNAKE_CASE		=						self.default_image_processor
											SCREAMING_SNAKE_CASE		=						prepare_img()
											SCREAMING_SNAKE_CASE		=						image_processor(images=lowerCamelCase__						,return_tensors="""np"""	)

											SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

											# verify the logits
											SCREAMING_SNAKE_CASE		=						(1, 1000)
											self.assertEqual(outputs.logits.shape						,lowerCamelCase__	)

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

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MegatronBertForCausalLM""",
          """MegatronBertForMaskedLM""",
          """MegatronBertForMultipleChoice""",
          """MegatronBertForNextSentencePrediction""",
          """MegatronBertForPreTraining""",
          """MegatronBertForQuestionAnswering""",
          """MegatronBertForSequenceClassification""",
          """MegatronBertForTokenClassification""",
          """MegatronBertModel""",
          """MegatronBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_megatron_bert import (
                MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegatronBertForCausalLM,
                MegatronBertForMaskedLM,
                MegatronBertForMultipleChoice,
                MegatronBertForNextSentencePrediction,
                MegatronBertForPreTraining,
                MegatronBertForQuestionAnswering,
                MegatronBertForSequenceClassification,
                MegatronBertForTokenClassification,
                MegatronBertModel,
                MegatronBertPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


