
from __future__ import annotations

import os
from typing import Any

import requests

lowercase        =  '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase        =  BASE_URL + '''/user'''

# https://github.com/settings/tokens
lowercase        =  os.environ.get('''USER_TOKEN''', '''''')



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str       )  ->    dict[Any, Any]:
							lowerCamelCase_							  =					{
							    """Authorization""": F'''token {auth_token}''',
							    """Accept""": """application/vnd.github.v3+json""",
							}
							return requests.get(UpperCAmelCase__			,				headers=UpperCAmelCase__       ).json()


if __name__ == "__main__":  # pragma: no cover
		if USER_TOKEN:
				for key, value in fetch_github_info(USER_TOKEN).items():
						print(F"""{key}: {value}""")
		else:
				raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')







from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class       __A:

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     int=3_2   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     List[str]=1_0   ,					__UpperCamelCase					:     int=[1_0, 2_0, 3_0, 4_0]   ,					__UpperCamelCase					:     List[str]=[1, 1, 2, 1]   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict="relu"   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					embeddings_size
												lowerCamelCase_							  =					hidden_sizes
												lowerCamelCase_							  =					depths
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					scope
												lowerCamelCase_							  =					len(__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

												lowerCamelCase_							  =					None
												if self.use_labels:
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.num_labels   )

												lowerCamelCase_							  =					self.get_config()
												return config, pixel_values, labels

					def    lowercase__      (	self					:     str   ):
												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   ,					)

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFRegNetModel(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					training=__UpperCamelCase   )
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,					)

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					TFRegNetForImageClassification(__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   ,					training=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (
					    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					TFRegNetModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												return

					@unittest.skip(reason="""RegNet does not use inputs_embeds"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0   ,					reason="""TF does not support backprop for grouped convolutions on CPU."""   ,					)
					@slow
					def    lowercase__      (	self					:     Tuple   ):
												super().test_keras_fit()

					@unittest.skip(reason="""RegNet does not support input and output embeddings"""   )
					def    lowercase__      (	self					:     Optional[Any]   ):
												pass

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												def check_hidden_states_output(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Any   ):
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   ,					training=__UpperCamelCase   )

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

																			lowerCamelCase_							  =					self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase   )   ,					expected_num_stages + 1   )

																			# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]   )   ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,					)

												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_							  =					["""basic""", """bottleneck"""]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										lowerCamelCase_							  =					layer_type
																										lowerCamelCase_							  =					True
																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

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

																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[Any]={}   ):
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   )
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   ).to_tuple()

																			def recursive_check(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
																										if isinstance(__UpperCamelCase   ,					(List, Tuple)   ):
																																	for tuple_iterable_value, dict_iterable_value in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																																								recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )
																										elif tuple_object is None:
																																	return
																										else:
																																	self.assertTrue(
																																	    all(tf.equal(__UpperCamelCase   ,					__UpperCamelCase   )   )   ,					msg=(
																																	        """Tuple and dict output are not equal. Difference:"""
																																	        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'''
																																	    )   ,					)

																										recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     List[Any]   ):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFRegNetModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



def      __lowerCAmelCase						(							)  ->    Optional[int]:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





@require_tf
@require_vision
class       __A(				unittest.TestCase       ):

					@cached_property
					def    lowercase__      (	self					:     Union[str, Any]   ):
												return (
												    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
												    if is_vision_available()
												    else None
												)



					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

												lowerCamelCase_							  =					self.default_image_processor
												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""tf"""   )

												# forward pass
												lowerCamelCase_							  =					model(**__UpperCamelCase   ,					training=__UpperCamelCase   )

												# verify the logits
												lowerCamelCase_							  =					tf.TensorShape((1, 1_0_0_0)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					tf.constant([-0.4180, -1.5051, -3.4836]   )

												tf.debugging.assert_near(outputs.logits[0, :3]   ,					__UpperCamelCase   ,					atol=1E-4   )





