
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__      :

						def __init__(			self       ,							a       ,							a = 13       ,							a = 64       ,							a = 2       ,							a = 3       ,							a = 3       ,							a = True       ,							a = True       ,							a = 1_28       ,							a=[16, 32, 64, 1_28]       ,							a = 7       ,							a = 4       ,							a = 37       ,							a = "gelu"       ,							a = 0.1       ,							a = 0.1       ,							a = 10       ,							a = 0.02       ,							a = 2       ,							a = 1       ,							a = 1_28       ,							a = [2, 2, 2, 2]       ,							a = 2       ,							a = 2       ,							)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	parent
								_UpperCamelCase						    =	batch_size
								_UpperCamelCase						    =	image_size
								_UpperCamelCase						    =	patch_size
								_UpperCamelCase						    =	num_channels
								_UpperCamelCase						    =	is_training
								_UpperCamelCase						    =	use_labels
								_UpperCamelCase						    =	hidden_size
								_UpperCamelCase						    =	num_hidden_layers
								_UpperCamelCase						    =	num_attention_heads
								_UpperCamelCase						    =	intermediate_size
								_UpperCamelCase						    =	hidden_act
								_UpperCamelCase						    =	hidden_dropout_prob
								_UpperCamelCase						    =	attention_probs_dropout_prob
								_UpperCamelCase						    =	type_sequence_label_size
								_UpperCamelCase						    =	initializer_range
								_UpperCamelCase						    =	encoder_stride
								_UpperCamelCase						    =	num_attention_outputs
								_UpperCamelCase						    =	embed_dim
								_UpperCamelCase						    =	embed_dim + 1
								_UpperCamelCase						    =	resolution
								_UpperCamelCase						    =	depths
								_UpperCamelCase						    =	hidden_sizes
								_UpperCamelCase						    =	dim
								_UpperCamelCase						    =	mlp_expansion_ratio

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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

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

								_UpperCamelCase						    =	self.get_config()

								return config, pixel_values, labels

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								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  A_				(			self       ,							a       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	TFEfficientFormerModel(config=A_				)
								_UpperCamelCase						    =	model(A_       ,							training=A_				)
								self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.type_sequence_label_size
								_UpperCamelCase						    =	TFEfficientFormerForImageClassification(A_				)
								_UpperCamelCase						    =	model(A_       ,							labels=A_       ,							training=A_				)
								self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)				)

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

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







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


								'''simple docstring'''

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







@require_tf
class        lowerCAmelCase__      (   _lowercase		,	_lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		(
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCamelCase_							: Union[str, Any]	=		(
						    {
						        '''feature-extraction''': TFEfficientFormerModel,
						        '''image-classification''': (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCamelCase_							: int	=		False

						UpperCamelCase_							: Tuple	=		False
						UpperCamelCase_							: Dict	=		False
						UpperCamelCase_							: Dict	=		False
						UpperCamelCase_							: Any	=		False

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


								'''simple docstring'''

								_UpperCamelCase						    =	TFEfficientFormerModelTester(self				)
								_UpperCamelCase						    =	ConfigTester(
								    self       ,							config_class=A_       ,							has_text_modality=A_       ,							hidden_size=37				)

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


								'''simple docstring'''

								self.config_tester.run_common_tests()

						@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""				)
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								pass

						@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""				)
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								pass

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


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

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

										_UpperCamelCase						    =	["""pixel_values"""]
										self.assertListEqual(arg_names[:1]       ,							A_				)

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


								'''simple docstring'''

								def check_hidden_states_output(a       ,							a       ,							a				):
										_UpperCamelCase						    =	model_class(A_				)

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

										_UpperCamelCase						    =	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"""				):
												_UpperCamelCase						    =	self.model_tester.encoder_seq_length
												if hasattr(self.model_tester       ,							"""chunk_length"""				) and self.model_tester.chunk_length > 1:
														_UpperCamelCase						    =	seq_length * self.model_tester.chunk_length
										else:
												_UpperCamelCase						    =	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:
												_UpperCamelCase						    =	outputs.decoder_hidden_states

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

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

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

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

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

										check_hidden_states_output(A_       ,							A_       ,							A_				)

						def  A_				(			self       ,							a       ,							a       ,							a=False				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	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  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	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  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*A_				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A_				)

						@slow
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_UpperCamelCase						    =	TFEfficientFormerModel.from_pretrained(A_				)
										self.assertIsNotNone(A_				)

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()
								_UpperCamelCase						    =	True

								_UpperCamelCase						    =	getattr(self.model_tester       ,							"""seq_length"""       ,							A_				)
								_UpperCamelCase						    =	getattr(self.model_tester       ,							"""encoder_seq_length"""       ,							A_				)
								_UpperCamelCase						    =	getattr(self.model_tester       ,							"""key_length"""       ,							A_				)
								_UpperCamelCase						    =	getattr(self.model_tester       ,							"""chunk_length"""       ,							A_				)

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

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

										_UpperCamelCase						    =	model(**self._prepare_for_class(A_       ,							A_				)       ,							training=A_				)
										_UpperCamelCase						    =	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"]
										_UpperCamelCase						    =	True
										_UpperCamelCase						    =	model_class(A_				)
										_UpperCamelCase						    =	model(**self._prepare_for_class(A_       ,							A_				)       ,							training=A_				)

										_UpperCamelCase						    =	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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										# Prepare our model
										_UpperCamelCase						    =	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
										_UpperCamelCase						    =	{
										    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
										}
										_UpperCamelCase						    =	model(A_				)
										self.assertTrue(outputs_dict is not None				)




def 							__A()		->				str:

		"""simple docstring"""






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







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

						@cached_property
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								return (
								    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""				)
								    if is_vision_available()
								    else None
								)

						@slow
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""				)
								_UpperCamelCase						    =	self.default_image_processor
								_UpperCamelCase						    =	prepare_img()
								_UpperCamelCase						    =	image_processor(images=A_       ,							return_tensors="""tf"""				)
								# forward pass
								_UpperCamelCase						    =	model(**A_       ,							training=A_				)
								# verify the logits
								_UpperCamelCase						    =	tf.TensorShape((1, 10_00)				)
								self.assertEqual(outputs.logits.shape       ,							A_				)
								_UpperCamelCase						    =	tf.constant([-0.0555, 0.4825, -0.0852]				)
								self.assertTrue(np.allclose(outputs.logits[0, :3]       ,							A_       ,							atol=1e-4				)				)







						@slow
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
								    """snap-research/efficientformer-l1-300"""				)
								_UpperCamelCase						    =	self.default_image_processor
								_UpperCamelCase						    =	prepare_img()
								_UpperCamelCase						    =	image_processor(images=A_       ,							return_tensors="""tf"""				)
								# forward pass
								_UpperCamelCase						    =	model(**A_       ,							training=A_				)
								# verify the logits
								_UpperCamelCase						    =	tf.TensorShape((1, 10_00)				)
								self.assertEqual(outputs.logits.shape       ,							A_				)
								_UpperCamelCase						    =	tf.constant([-0.1312, 0.4353, -1.0499]				)
								self.assertTrue(np.allclose(outputs.logits[0, :3]       ,							A_       ,							atol=1e-4				)				)







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = "x"						,  lowerCAmelCase = 1_0**-1_0						,  lowerCAmelCase = 1						,  )		->				complex:

		"""simple docstring"""






		_UpperCamelCase						    =	symbols(lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  diff(lowerCAmelCase						,  lowerCAmelCase ) )

		_UpperCamelCase						    =	starting_point

		while True:
				if diff_function(lowerCAmelCase ) != 0:
						_UpperCamelCase						    =	prev_guess - multiplicity * func(lowerCAmelCase ) / diff_function(
						    lowerCAmelCase )
				else:
						raise ZeroDivisionError("""Could not find root""" ) from None

				# Precision is checked by comparing the difference of consecutive guesses
				if abs(next_guess - prev_guess ) < precision:
						return next_guess

				_UpperCamelCase						    =	next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"""{newton_raphson("exp(x) - 1", 10, precision=0.0_05)}""",
		)

		# Find root of cos(x)
		print(F"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")



