"""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      _A	:




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     str	,      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_							:     int=[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	,      )    ->       Tuple:
         __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  lowercase					(		self							:     Tuple    )    ->       Union[str, Any]:
         __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  lowercase					(		self							:     Any    )    ->       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=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  lowercase					(		self							:     Tuple	,      A_							:     int	,      A_							:     int	,      A_							:     int    )    ->       Dict:
         __snake_case				=							TFEfficientFormerModel(config=A_    )
         __snake_case				=							model(A_	,      training=A_    )
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     List[str]	,      A_							:     int	,      A_							:     List[Any]	,      A_							:     List[str]    )    ->       Any:
         __snake_case				=							self.type_sequence_label_size
         __snake_case				=							TFEfficientFormerForImageClassification(A_    )
         __snake_case				=							model(A_	,      labels=A_	,      training=A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )

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

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







    def  lowercase					(		self							:     List[Any]    )    ->       str:
         __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      _A	(						_UpperCAmelCase							, _UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




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

    UpperCamelCase_	:					Union[str, Any]							=					False

    UpperCamelCase_	:					Optional[Any]							=					False
    UpperCamelCase_	:					Tuple							=					False
    UpperCamelCase_	:					Union[str, Any]							=					False
    UpperCamelCase_	:					Optional[Any]							=					False
    def  lowercase					(		self							:     List[str]    )    ->       Union[str, Any]:
         __snake_case				=							TFEfficientFormerModelTester(self    )
         __snake_case				=							ConfigTester(
             self	,      config_class=A_	,      has_text_modality=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     List[Any]    )    ->       Tuple:
         self.config_tester.run_common_tests()
    @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''    )
    def  lowercase					(		self							:     int    )    ->       str:
         pass
    @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''    )
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         pass
    def  lowercase					(		self							:     Tuple    )    ->       List[str]:
         __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(A_    )
              __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]	,      A_    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         def check_hidden_states_output(A_							:     Dict	,      A_							:     int	,      A_							:     List[str]    ):
              __snake_case				=							model_class(A_    )

              __snake_case				=							model(**self._prepare_for_class(A_	,      A_    )	,      training=A_    )
              __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(A_    )	,      A_    )

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

                   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(A_	,      A_	,      A_    )

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

              check_hidden_states_output(A_	,      A_	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Tuple	,      A_							:     Optional[int]	,      A_							:     Dict=False    )    ->       Any:
         __snake_case				=							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  lowercase					(		self							:     Any    )    ->       int:
         __snake_case				=							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  lowercase					(		self							:     Any    )    ->       Optional[Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*A_    )
    def  lowercase					(		self							:     Any    )    ->       Tuple:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )
    @slow
    def  lowercase					(		self							:     Dict    )    ->       Dict:
         for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __snake_case				=							TFEfficientFormerModel.from_pretrained(A_    )
              self.assertIsNotNone(A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Union[str, Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case				=							True

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

         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(A_    )

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

              __snake_case				=							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  lowercase					(		self							:     Dict    )    ->       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
         __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(A_    )
              # 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=A_    )
                  for key, val in model.input_signature.items()
                  if key in model.dummy_inputs
              }
              __snake_case				=							model(A_    )
              self.assertTrue(outputs_dict is not None    )






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




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




    """simple docstring"""




    @cached_property
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         return (
             EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''    )
             if is_vision_available()
             else None
         )
    @slow
    def  lowercase					(		self							:     Tuple    )    ->       Tuple:
         __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=A_	,      return_tensors='''tf'''    )
         # forward pass
         __snake_case				=							model(**A_	,      training=A_    )
         # verify the logits
         __snake_case				=							tf.TensorShape((1, 1_000)    )
         self.assertEqual(outputs.logits.shape	,      A_    )
         __snake_case				=							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  lowercase					(		self							:     Any    )    ->       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=A_	,      return_tensors='''tf'''    )
         # forward pass
         __snake_case				=							model(**A_	,      training=A_    )
         # verify the logits
         __snake_case				=							tf.TensorShape((1, 1_000)    )
         self.assertEqual(outputs.logits.shape	,      A_    )
         __snake_case				=							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"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __snake_case				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __snake_case				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __snake_case				=							True
          else:
               __snake_case				=							next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(snake_case),      snake_case)
          plt.xlabel('''Iterations''')
          plt.ylabel('''Function values''')
          plt.show()
     return best_state


if __name__ == "__main__":





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Union[str, Any]														=							simulated_annealing(
           prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							simulated_annealing(
           prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

       __lowercase		:		List[str]														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Dict														=							simulated_annealing(prob, find_max=False, visualization=True)
       print(
           "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )

       __lowercase		:		Any														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Tuple														=							simulated_annealing(prob, find_max=True, visualization=True)




       print(
           "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )