



import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase__     =	"""src/transformers"""

# Matches is_xxx_available()
lowerCamelCase__     =	re.compile(r"""is\_([a-z_]*)_available()""")
# Matches from xxx import bla
lowerCamelCase__     =	re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
lowerCamelCase__     =	re.compile(r"""^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)""")


lowerCamelCase__     =	"""
{0} = None
"""

lowerCamelCase__     =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowerCamelCase__     =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[Any]      ):
				"""simple docstring"""







				if _re_test_backend.search(_SCREAMING_SNAKE_CASE      ) is None:
								return None
				__a					       =						[b[0] for b in _re_backend.findall(_SCREAMING_SNAKE_CASE      )]
				backends.sort()
				return "_and_".join(_SCREAMING_SNAKE_CASE      )

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				with open(os.path.join(_SCREAMING_SNAKE_CASE			,       """__init__.py"""      )			,       """r"""			,       encoding="""utf-8"""			,       newline="""\n"""      ) as f:
								__a					       =						f.readlines()

				# Get to the point we do the actual imports for type checking
				__a					       =						0
				while not lines[line_index].startswith("""if TYPE_CHECKING"""      ):
								line_index += 1

				__a					       =						{}
				# Go through the end of the file
				while line_index < len(_SCREAMING_SNAKE_CASE      ):
								# If the line is an if is_backend_available, we grab all objects associated.
								__a					       =						find_backend(lines[line_index]      )
								if backend is not None:
												while not lines[line_index].startswith("""    else:"""      ):
																line_index += 1
												line_index += 1

												__a					       =						[]
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(""" """ * 8      ):
																__a					       =						lines[line_index]
																__a					       =						_re_single_line_import.search(_SCREAMING_SNAKE_CASE      )
																if single_line_import_search is not None:
																				objects.extend(single_line_import_search.groups()[0].split(""", """      )      )
																elif line.startswith(""" """ * 12      ):
																				objects.append(line[12:-2]      )
																line_index += 1

												__a					       =						objects
								else:
												line_index += 1

				return backend_specific_objects

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     int      ):
				"""simple docstring"""







				if name.isupper():
								return DUMMY_CONSTANT.format(_SCREAMING_SNAKE_CASE      )
				elif name.islower():
								return DUMMY_FUNCTION.format(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
				else:
								return DUMMY_CLASS.format(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[str]=None      ):
				"""simple docstring"""







				if backend_specific_objects is None:
								__a					       =						read_init()
				# For special correspondence backend to module name as used in the function requires_modulename
				__a					       =						{}

				for backend, objects in backend_specific_objects.items():
								__a					       =						"""[""" + """, """.join(f"\"{b}\"" for b in backend.split("""_and_"""      )      ) + """]"""
								__a					       =						"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
								dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
								dummy_file += "\n".join([create_dummy_object(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      ) for o in objects]      )
								__a					       =						dummy_file

				return dummy_files

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]=False      ):
				"""simple docstring"""







				__a					       =						create_dummy_files()
				# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
				__a					       =						{"""torch""": """pt"""}

				# Locate actual dummy modules and read their content.
				__a					       =						os.path.join(_SCREAMING_SNAKE_CASE			,       """utils"""      )
				__a					       =						{
				    backend: os.path.join(_SCREAMING_SNAKE_CASE			,       f"dummy_{short_names.get(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )}_objects.py"      )
				    for backend in dummy_files.keys()
				}

				__a					       =						{}
				for backend, file_path in dummy_file_paths.items():
								if os.path.isfile(_SCREAMING_SNAKE_CASE      ):
												with open(_SCREAMING_SNAKE_CASE			,       """r"""			,       encoding="""utf-8"""			,       newline="""\n"""      ) as f:
																__a					       =						f.read()
								else:
												__a					       =						""""""

				for backend in dummy_files.keys():
								if dummy_files[backend] != actual_dummies[backend]:
												if overwrite:
																print(
																    f"Updating transformers.utils.dummy_{short_names.get(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )}_objects.py as the main "
																    """__init__ has new objects."""      )
																with open(dummy_file_paths[backend]			,       """w"""			,       encoding="""utf-8"""			,       newline="""\n"""      ) as f:
																				f.write(dummy_files[backend]      )
												else:
																raise ValueError(
																    """The main __init__ has objects that are not present in """
																    f"transformers.utils.dummy_{short_names.get(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )}_objects.py. Run `make fix-copies` "
																    """to fix this."""      )


if __name__ == "__main__":
						lowerCamelCase__     =	argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						lowerCamelCase__     =	parser.parse_args()

						check_dummies(args.fix_and_overwrite)





import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





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

											'''simple docstring'''
											__a					       =						parent
											__a					       =						batch_size
											__a					       =						image_size
											__a					       =						num_channels
											__a					       =						embeddings_size
											__a					       =						hidden_sizes
											__a					       =						depths
											__a					       =						is_training
											__a					       =						use_labels
											__a					       =						hidden_act
											__a					       =						num_labels
											__a					       =						scope
											__a					       =						len(__lowercase		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

											__a					       =						self.get_config()

											return config, pixel_values





							def   UpperCamelCase_   (				self						:						Optional[int]		):

											'''simple docstring'''
											return ResNetConfig(
											    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						:						List[str] ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						FlaxResNetModel(config=__lowercase		)
											__a					       =						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						:						List[str] ,			__lowercase						:						Dict ,			__lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a					       =						self.num_labels
											__a					       =						FlaxResNetForImageClassification(config=__lowercase		)
											__a					       =						model(__lowercase		)
											self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)		)







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.prepare_config_and_inputs()
											__a       ,   __a					       =						config_and_inputs
											__a					       =						{"""pixel_values""": pixel_values}
											return config, inputs_dict






@require_flax
class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__     , unittest.TestCase   ):
							__lowerCamelCase		:		int      =(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							__lowerCamelCase		:		Union[str, Any]      =False
							__lowerCamelCase		:		Optional[Any]      =False
							__lowerCamelCase		:		str      =False





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						FlaxResNetModelTester(self		)
											__a					       =						ConfigTester(self ,			config_class=__lowercase ,			has_text_modality=__lowercase		)





							def   UpperCamelCase_   (				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   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											return





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__lowercase		)





							@unittest.skip(reason="""ResNet does not use inputs_embeds"""		)
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											pass





							@unittest.skip(reason="""ResNet does not support input and output embeddings"""		)
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											pass





							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a       ,   __a					       =						self.model_tester.prepare_config_and_inputs_for_common()

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

															__a					       =						["""pixel_values"""]
															self.assertListEqual(arg_names[:1] ,			__lowercase		)





							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											def check_hidden_states_output(__lowercase						:						Tuple ,			__lowercase						:						List[str] ,			__lowercase						:						str		):
															__a					       =						model_class(__lowercase		)

															__a					       =						model(**self._prepare_for_class(__lowercase ,			__lowercase		)		)

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

															__a					       =						self.model_tester.num_stages
															self.assertEqual(len(__lowercase		) ,			expected_num_stages + 1		)





							@unittest.skip(reason="""ResNet does not use feedforward chunking"""		)
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											pass







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a       ,   __a					       =						self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__		):
																			__a					       =						self._prepare_for_class(__lowercase ,			__lowercase		)
																			__a					       =						model_class(__lowercase		)

																			@jax.jit
																			def model_jitted(__lowercase						:						List[str] ,			**__lowercase						:						Optional[int]		):
																							return model(pixel_values=__lowercase ,			**__lowercase		)

																			with self.subTest("""JIT Enabled"""		):
																							__a					       =						model_jitted(**__lowercase		).to_tuple()

																			with self.subTest("""JIT Disabled"""		):
																							with jax.disable_jit():
																											__a					       =						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 					lowerCAmelCase__  (							):
				"""simple docstring"""







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






@require_flax
class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							@cached_property
							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""		) if is_vision_available() else None







							@slow
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a					       =						FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""		)

											__a					       =						self.default_image_processor
											__a					       =						prepare_img()
											__a					       =						image_processor(images=__lowercase ,			return_tensors="""np"""		)

											__a					       =						model(**__lowercase		)

											# verify the logits
											__a					       =						(1, 1000)
											self.assertEqual(outputs.logits.shape ,			__lowercase		)

											__a					       =						jnp.array([-11.1069, -9.7877, -8.3777]		)

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