






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   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Optional[int]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      int = 13       ,	UpperCamelCase:      int = 64       ,	UpperCamelCase:      int = 2       ,	UpperCamelCase:      int = 3       ,	UpperCamelCase:      int = 3       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      bool = True       ,	UpperCamelCase:      int = 1_28       ,	UpperCamelCase:      Optional[Any]=[16, 32, 64, 1_28]       ,	UpperCamelCase:      int = 7       ,	UpperCamelCase:      int = 4       ,	UpperCamelCase:      int = 37       ,	UpperCamelCase:      str = "gelu"       ,	UpperCamelCase:      float = 0.1       ,	UpperCamelCase:      float = 0.1       ,	UpperCamelCase:      int = 10       ,	UpperCamelCase:      float = 0.02       ,	UpperCamelCase:      int = 2       ,	UpperCamelCase:      int = 1       ,	UpperCamelCase:      int = 1_28       ,	UpperCamelCase:      List[int] = [2, 2, 2, 2]       ,	UpperCamelCase:      int = 2       ,	UpperCamelCase:      int = 2       ,	)		->		Dict:
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     image_size
											snake_case__									=     patch_size
											snake_case__									=     num_channels
											snake_case__									=     is_training
											snake_case__									=     use_labels
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_act
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     type_sequence_label_size
											snake_case__									=     initializer_range
											snake_case__									=     encoder_stride
											snake_case__									=     num_attention_outputs
											snake_case__									=     embed_dim
											snake_case__									=     embed_dim + 1
											snake_case__									=     resolution
											snake_case__									=     depths
											snake_case__									=     hidden_sizes
											snake_case__									=     dim
											snake_case__									=     mlp_expansion_ratio
							def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											snake_case__									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

											snake_case__									=     self.get_config()

											return config, pixel_values, labels
							def lowerCAmelCase_ (     self:      int						)		->		Optional[int]:
											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=UpperCamelCase       ,	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 lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Union[str, Any]						)		->		int:
											snake_case__									=     TFEfficientFormerModel(config=UpperCamelCase						)
											snake_case__									=     model(UpperCamelCase       ,	training=UpperCamelCase						)
											self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)						)
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Tuple						)		->		List[Any]:
											snake_case__									=     self.type_sequence_label_size
											snake_case__									=     TFEfficientFormerForImageClassification(UpperCamelCase						)
											snake_case__									=     model(UpperCamelCase       ,	labels=UpperCamelCase       ,	training=UpperCamelCase						)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)						)

											# test greyscale images
											snake_case__									=     1
											snake_case__									=     TFEfficientFormerForImageClassification(UpperCamelCase						)

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


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


@require_tf
class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (
							    (
							        TFEfficientFormerModel,
							        TFEfficientFormerForImageClassificationWithTeacher,
							        TFEfficientFormerForImageClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							_UpperCAmelCase     =       (
							    {
							        "feature-extraction": TFEfficientFormerModel,
							        "image-classification": (
							            TFEfficientFormerForImageClassification,
							            TFEfficientFormerForImageClassificationWithTeacher,
							        ),
							    }
							    if is_tf_available()
							    else {}
							)

							_UpperCAmelCase     =       False

							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											snake_case__									=     TFEfficientFormerModelTester(self						)
											snake_case__									=     ConfigTester(
											    self       ,	config_class=UpperCamelCase       ,	has_text_modality=UpperCamelCase       ,	hidden_size=37						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		List[Any]:
											self.config_tester.run_common_tests()
							@unittest.skip(reason='EfficientFormer does not use inputs_embeds'						)
							def lowerCAmelCase_ (     self:      int						)		->		str:
											pass
							@unittest.skip(reason='EfficientFormer does not support input and output embeddings'						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[int]:
											pass
							def lowerCAmelCase_ (     self:      Tuple						)		->		Optional[int]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case__									=     ['pixel_values']
															self.assertListEqual(arg_names[:1]       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											def check_hidden_states_output(UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Optional[int]						):
															snake_case__									=     model_class(UpperCamelCase						)

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

															snake_case__									=     getattr(
															    self.model_tester       ,	'expected_num_hidden_layers'       ,	self.model_tester.num_hidden_layers + 1						)
															self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

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

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

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

											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     True
															check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

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

															check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      List[Any]=False						)		->		List[str]:
											snake_case__									=     super()._prepare_for_class(UpperCamelCase       ,	UpperCamelCase       ,	return_labels=UpperCamelCase						)

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

											return inputs_dict
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Union[str, Any]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase						)
							@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[Any]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Dict:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      Dict						)		->		Dict:
											for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     TFEfficientFormerModel.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__									=     True

											snake_case__									=     getattr(self.model_tester       ,	'seq_length'       ,	UpperCamelCase						)
											snake_case__									=     getattr(self.model_tester       ,	'encoder_seq_length'       ,	UpperCamelCase						)
											snake_case__									=     getattr(self.model_tester       ,	'key_length'       ,	UpperCamelCase						)
											snake_case__									=     getattr(self.model_tester       ,	'chunk_length'       ,	UpperCamelCase						)

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

											for model_class in self.all_model_classes:
															snake_case__									=     True
															snake_case__									=     False
															snake_case__									=     True
															snake_case__									=     model_class(UpperCamelCase						)

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

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

															snake_case__									=     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
															self.assertEqual(len(UpperCamelCase						)       ,	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 lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[Any]:
											# 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
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

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


def      a_ (      )			->			Dict:







				"""simple docstring"""
				snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
				return image


@require_tf
@require_vision
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@cached_property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
											return (
											    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'						)
											    if is_vision_available()
											    else None
											)
							@slow
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'						)
											snake_case__									=     self.default_image_processor
											snake_case__									=     prepare_img()
											snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='tf'						)
											# forward pass
											snake_case__									=     model(**UpperCamelCase       ,	training=UpperCamelCase						)
											# verify the logits
											snake_case__									=     tf.TensorShape((1, 10_00)						)
											self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)
											snake_case__									=     tf.constant([-0.0_555, 0.4_825, -0.0_852]						)
											self.assertTrue(np.allclose(outputs.logits[0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)


							@slow
							def lowerCAmelCase_ (     self:      Tuple						)		->		List[Any]:
											snake_case__									=     TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
											    'snap-research/efficientformer-l1-300'						)
											snake_case__									=     self.default_image_processor
											snake_case__									=     prepare_img()
											snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='tf'						)
											# forward pass
											snake_case__									=     model(**UpperCamelCase       ,	training=UpperCamelCase						)
											# verify the logits
											snake_case__									=     tf.TensorShape((1, 10_00)						)
											self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)
											snake_case__									=     tf.constant([-0.1_312, 0.4_353, -1.0_499]						)
											self.assertTrue(np.allclose(outputs.logits[0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




