
"""simple docstring"""


import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
		    TFEfficientFormerForImageClassification,
		    TFEfficientFormerForImageClassificationWithTeacher,
		    TFEfficientFormerModel,
		)
		from transformers.models.efficientformer.modeling_tf_efficientformer import (
		    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


if is_vision_available():
		from PIL import Image

		from transformers import EfficientFormerImageProcessor







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Tuple  ,A_     :      Dict  ,A_     :      int = 13  ,A_     :      int = 64  ,A_     :      int = 2  ,A_     :      int = 3  ,A_     :      int = 3  ,A_     :      bool = True  ,A_     :      bool = True  ,A_     :      int = 128  ,A_     :      Dict=[16, 32, 64, 128]  ,A_     :      int = 7  ,A_     :      int = 4  ,A_     :      int = 37  ,A_     :      str = "gelu"  ,A_     :      float = 0.1  ,A_     :      float = 0.1  ,A_     :      int = 10  ,A_     :      float = 0.02  ,A_     :      int = 2  ,A_     :      int = 1  ,A_     :      int = 128  ,A_     :      List[int] = [2, 2, 2, 2]  ,A_     :      int = 2  ,A_     :      int = 2  ,)     ->   str:
							A						   =   parent
							A						   =   batch_size
							A						   =   image_size
							A						   =   patch_size
							A						   =   num_channels
							A						   =   is_training
							A						   =   use_labels
							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   encoder_stride
							A						   =   num_attention_outputs
							A						   =   embed_dim
							A						   =   embed_dim + 1
							A						   =   resolution
							A						   =   depths
							A						   =   hidden_sizes
							A						   =   dim
							A						   =   mlp_expansion_ratio


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Tuple:
							A						   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

							A						   =   None
							if self.use_labels:
								A						   =   ids_tensor([self.batch_size]  ,self.type_sequence_label_size      )

							A						   =   self.get_config()

							return config, pixel_values, labels


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							return EfficientFormerConfig(
							    image_size=self.image_size  ,patch_size=self.patch_size  ,num_channels=self.num_channels  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,intermediate_size=self.intermediate_size  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,is_decoder=A_  ,initializer_range=self.initializer_range  ,encoder_stride=self.encoder_stride  ,resolution=self.resolution  ,depths=self.depths  ,hidden_sizes=self.hidden_sizes  ,dim=self.dim  ,mlp_expansion_ratio=self.mlp_expansion_ratio  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      int  ,A_     :      List[Any]  ,A_     :      Union[str, Any]      )     ->   List[Any]:
							A						   =   TFEfficientFormerModel(config=A_      )
							A						   =   model(A_  ,training=A_      )
							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      Union[str, Any]  ,A_     :      int  ,A_     :      Union[str, Any]      )     ->   Union[str, Any]:
							A						   =   self.type_sequence_label_size
							A						   =   TFEfficientFormerForImageClassification(A_      )
							A						   =   model(A_  ,labels=A_  ,training=A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)      )

							# test greyscale images
							A						   =   1
							A						   =   TFEfficientFormerForImageClassification(A_      )

							A						   =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
							A						   =   model(A_  ,labels=A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[Any]:
							A						   =   self.prepare_config_and_inputs()
							A   ,      A   ,      A						   =   config_and_inputs
							A						   =   {'pixel_values': pixel_values}
							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    (
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCamelCase:    Union[str, Any]      =    (
						    {
						        '''feature-extraction''': TFEfficientFormerModel,
						        '''image-classification''': (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						_lowerCamelCase:    int      =    False

						_lowerCamelCase:    Tuple      =    False
						_lowerCamelCase:    Dict      =    False
						_lowerCamelCase:    Dict      =    False
						_lowerCamelCase:    Any      =    False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[Any]:
							A						   =   TFEfficientFormerModelTester(self      )
							A						   =   ConfigTester(
							    self  ,config_class=A_  ,has_text_modality=A_  ,hidden_size=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							self.config_tester.run_common_tests()


						@unittest.skip(reason='EfficientFormer does not use inputs_embeds'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Any:
							pass


						@unittest.skip(reason='EfficientFormer does not support input and output embeddings'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							pass


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[Any]:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A						   =   model_class(A_      )
								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]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							def check_hidden_states_output(A_     :      Tuple  ,A_     :      Tuple  ,A_     :      str      ):
								A						   =   model_class(A_      )

								A						   =   model(**self._prepare_for_class(A_  ,A_      )  ,training=A_      )
								A						   =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								A						   =   getattr(
								    self.model_tester  ,'expected_num_hidden_layers'  ,self.model_tester.num_hidden_layers + 1      )
								self.assertEqual(len(A_      )  ,A_      )

								if hasattr(self.model_tester  ,'encoder_seq_length'      ):
									A						   =   self.model_tester.encoder_seq_length
									if hasattr(self.model_tester  ,'chunk_length'      ) and self.model_tester.chunk_length > 1:
										A						   =   seq_length * self.model_tester.chunk_length
								else:
									A						   =   self.model_tester.seq_length

								self.assertListEqual(
								    list(hidden_states[-1].shape[-2:]      )  ,[seq_length, self.model_tester.hidden_size]  ,)

								if config.is_encoder_decoder:
									A						   =   outputs.decoder_hidden_states

									self.asseretIsInstance(A_  ,(list, tuple)      )
									self.assertEqual(len(A_      )  ,A_      )
									A						   =   getattr(self.model_tester  ,'seq_length'  ,A_      )
									A						   =   getattr(self.model_tester  ,'decoder_seq_length'  ,A_      )

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:]      )  ,[decoder_seq_length, self.model_tester.hidden_size]  ,)

							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A						   =   True
								check_hidden_states_output(A_  ,A_  ,A_      )

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

								check_hidden_states_output(A_  ,A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      List[str]=False      )     ->   List[Any]:
							A						   =   super()._prepare_for_class(A_  ,A_  ,return_labels=A_      )

							if return_labels:
								if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
									del inputs_dict["labels"]

							return inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[str]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
							for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   TFEfficientFormerModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Any:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()
							A						   =   True

							A						   =   getattr(self.model_tester  ,'seq_length'  ,A_      )
							A						   =   getattr(self.model_tester  ,'encoder_seq_length'  ,A_      )
							A						   =   getattr(self.model_tester  ,'key_length'  ,A_      )
							A						   =   getattr(self.model_tester  ,'chunk_length'  ,A_      )

							if chunk_length is not None and hasattr(self.model_tester  ,'num_hashes'      ):
								A						   =   encoder_seq_length * self.model_tester.num_hashes

							for model_class in self.all_model_classes:
								A						   =   True
								A						   =   False
								A						   =   True
								A						   =   model_class(A_      )

								A						   =   model(**self._prepare_for_class(A_  ,A_      )  ,training=A_      )
								A						   =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
								self.assertEqual(len(A_      )  ,self.model_tester.num_attention_outputs      )

								# check that output_attentions also work using config
								del inputs_dict["output_attentions"]
								A						   =   True
								A						   =   model_class(A_      )
								A						   =   model(**self._prepare_for_class(A_  ,A_      )  ,training=A_      )

								A						   =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
								self.assertEqual(len(A_      )  ,self.model_tester.num_attention_outputs      )

								if chunk_length is not None:
									self.assertListEqual(
									    list(attentions[0].shape[-4:]      )  ,[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]  ,)
								else:
									self.assertListEqual(
									    list(attentions[0].shape[-3:]      )  ,[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,)




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[int]:
							# We use a simplified version of this test for EfficientFormer because it requires training=False
							# and Keras refuses to let us force that during functional construction
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								# Prepare our model
								A						   =   model_class(A_      )
								# These are maximally general inputs for the model, with multiple None dimensions
								# Hopefully this will catch any conditionals that fail for flexible shapes
								A						   =   {
								    key: tf.keras.Input(shape=val.shape[1:]  ,dtype=val.dtype  ,name=A_      )
								    for key, val in model.input_signature.items()
								    if key in model.dummy_inputs
								}
								A						   =   model(A_      )
								self.assertTrue(outputs_dict is not None      )






def       _snake_case				(		):
	A						   =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
	return image







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



						'''simple docstring'''


						@cached_property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[Any]:
							return (
							    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'      )
							    if is_vision_available()
							    else None
							)


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   List[Any]:
							A						   =   TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'      )
							A						   =   self.default_image_processor
							A						   =   prepare_img()
							A						   =   image_processor(images=A_  ,return_tensors='tf'      )
							# forward pass
							A						   =   model(**A_  ,training=A_      )
							# verify the logits
							A						   =   tf.TensorShape((1, 1000)      )
							self.assertEqual(outputs.logits.shape  ,A_      )
							A						   =   tf.constant([-0.05_55, 0.48_25, -0.08_52]      )
							self.assertTrue(np.allclose(outputs.logits[0, :3]  ,A_  ,atol=1e-4      )      )




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
							    'snap-research/efficientformer-l1-300'      )
							A						   =   self.default_image_processor
							A						   =   prepare_img()
							A						   =   image_processor(images=A_  ,return_tensors='tf'      )
							# forward pass
							A						   =   model(**A_  ,training=A_      )
							# verify the logits
							A						   =   tf.TensorShape((1, 1000)      )
							self.assertEqual(outputs.logits.shape  ,A_      )
							A						   =   tf.constant([-0.13_12, 0.43_53, -1.04_99]      )
							self.assertTrue(np.allclose(outputs.logits[0, :3]  ,A_  ,atol=1e-4      )      )

"""simple docstring"""


from math import pi, sqrt






def       _snake_case				(		snake_case__   :     float     ):
	if num <= 0:
		raise ValueError('math domain error'     )
	if num > 171.5:
		raise OverflowError('math range error'     )
	elif num - int(snake_case__     ) not in (0, 0.5):
		raise NotImplementedError('num must be an integer or a half-integer'     )
	elif num == 0.5:
		return sqrt(snake_case__     )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )






def       _snake_case				(		):
	assert gamma(0.5     ) == sqrt(snake_case__     )
	assert gamma(1     ) == 1.0
	assert gamma(2     ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_lowercase							   =				1.0
		while num:
				_lowercase							   =				float(input('''Gamma of: '''))
				print(F"""gamma({num}) = {gamma(num)}""")
				print('''\nEnter 0 to exit...''')