


"""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				:    List[str]				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    int = 13				, __UpperCAmelCase				:    int = 64				, __UpperCAmelCase				:    int = 2				, __UpperCAmelCase				:    int = 3				, __UpperCAmelCase				:    int = 3				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    int = 128				, __UpperCAmelCase				:    Dict=[16, 32, 64, 128]				, __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 = 128				, __UpperCAmelCase				:    List[int] = [2, 2, 2, 2]				, __UpperCAmelCase				:    int = 2				, __UpperCAmelCase				:    int = 2				, ):
      a						:		int      							=			parent
      a						:		List[str]      							=			batch_size
      a						:		List[Any]      							=			image_size
      a						:		Union[str, Any]      							=			patch_size
      a						:		Any      							=			num_channels
      a						:		Union[str, Any]      							=			is_training
      a						:		Union[str, Any]      							=			use_labels
      a						:		Tuple      							=			hidden_size
      a						:		Tuple      							=			num_hidden_layers
      a						:		int      							=			num_attention_heads
      a						:		Union[str, Any]      							=			intermediate_size
      a						:		int      							=			hidden_act
      a						:		Tuple      							=			hidden_dropout_prob
      a						:		Optional[int]      							=			attention_probs_dropout_prob
      a						:		List[str]      							=			type_sequence_label_size
      a						:		str      							=			initializer_range
      a						:		Optional[int]      							=			encoder_stride
      a						:		Optional[int]      							=			num_attention_outputs
      a						:		Any      							=			embed_dim
      a						:		Dict      							=			embed_dim + 1
      a						:		Union[str, Any]      							=			resolution
      a						:		Optional[int]      							=			depths
      a						:		Union[str, Any]      							=			hidden_sizes
      a						:		Dict      							=			dim
      a						:		List[str]      							=			mlp_expansion_ratio






    def 	__snake_case							(					self				:    Dict):
      a						:		int      							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      a						:		str      							=			None
      if self.use_labels:
        a						:		Tuple      							=			ids_tensor([self.batch_size]				, self.type_sequence_label_size)

      a						:		List[str]      							=			self.get_config()

      return config, pixel_values, labels






    def 	__snake_case							(					self				:    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 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    Optional[int]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    Dict):
      a						:		Optional[Any]      							=			TFEfficientFormerModel(config=__UpperCAmelCase)
      a						:		int      							=			model(__UpperCAmelCase				, training=__UpperCAmelCase)
      self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size))






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Tuple):
      a						:		Optional[int]      							=			self.type_sequence_label_size
      a						:		Union[str, Any]      							=			TFEfficientFormerForImageClassification(__UpperCAmelCase)
      a						:		Dict      							=			model(__UpperCAmelCase				, labels=__UpperCAmelCase				, training=__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      a						:		Union[str, Any]      							=			1
      a						:		Any      							=			TFEfficientFormerForImageClassification(__UpperCAmelCase)

      a						:		List[Any]      							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      a						:		List[str]      							=			model(__UpperCAmelCase				, labels=__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size))






    def 	__snake_case							(					self				:    Tuple):
      a						:		Union[str, Any]      							=			self.prepare_config_and_inputs()
      a							,		a							,		a						:		List[str]      							=			config_and_inputs
      a						:		int      							=			{"pixel_values": pixel_values}
      return config, inputs_dict






@require_tf
class 						_A  (  _a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       (
        (
            TFEfficientFormerModel,
            TFEfficientFormerForImageClassificationWithTeacher,
            TFEfficientFormerForImageClassification,
        )
        if is_tf_available()
        else ()
    )
    UpperCAmelCase       :							Optional[Any]	=       (
        {
            """feature-extraction""": TFEfficientFormerModel,
            """image-classification""": (
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
            ),
        }
        if is_tf_available()
        else {}
    )

    UpperCAmelCase       :							Tuple	=       False

    UpperCAmelCase       :							Optional[Any]	=       False
    UpperCAmelCase       :							List[Any]	=       False
    UpperCAmelCase       :							Dict	=       False
    UpperCAmelCase       :							Union[str, Any]	=       False






    def 	__snake_case							(					self				:    List[Any]):
      a						:		Any      							=			TFEfficientFormerModelTester(self)
      a						:		Optional[Any]      							=			ConfigTester(
          self				, config_class=__UpperCAmelCase				, has_text_modality=__UpperCAmelCase				, hidden_size=37)






    def 	__snake_case							(					self				:    str):
      self.config_tester.run_common_tests()






    @unittest.skip(reason="EfficientFormer does not use inputs_embeds")
    def 	__snake_case							(					self				:    int):
      pass






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






    def 	__snake_case							(					self				:    Optional[Any]):
      a							,		a						:		Dict      							=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						:		Dict      							=			model_class(__UpperCAmelCase)
        a						:		Optional[Any]      							=			inspect.signature(model.call)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a						:		List[str]      							=			[*signature.parameters.keys()]

        a						:		List[Any]      							=			["pixel_values"]
        self.assertListEqual(arg_names[:1]				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    str):
      def check_hidden_states_output(__UpperCAmelCase				:    List[Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[Any]):
        a						:		int      							=			model_class(__UpperCAmelCase)

        a						:		Optional[int]      							=			model(**self._prepare_for_class(__UpperCAmelCase				, __UpperCAmelCase)				, training=__UpperCAmelCase)
        a						:		List[str]      							=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        a						:		Optional[Any]      							=			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"):
          a						:		Optional[Any]      							=			self.model_tester.encoder_seq_length
          if hasattr(self.model_tester				, "chunk_length") and self.model_tester.chunk_length > 1:
            a						:		Any      							=			seq_length * self.model_tester.chunk_length
        else:
          a						:		str      							=			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						:		Union[str, Any]      							=			outputs.decoder_hidden_states

          self.asseretIsInstance(__UpperCAmelCase				, (list, tuple))
          self.assertEqual(len(__UpperCAmelCase)				, __UpperCAmelCase)
          a						:		int      							=			getattr(self.model_tester				, "seq_length"				, __UpperCAmelCase)
          a						:		Any      							=			getattr(self.model_tester				, "decoder_seq_length"				, __UpperCAmelCase)

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

      a							,		a						:		Any      							=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						:		int      							=			True
        check_hidden_states_output(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a						:		Optional[Any]      							=			True

        check_hidden_states_output(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    Union[str, Any]=False):
      a						:		str      							=			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 	__snake_case							(					self				:    str):
      a						:		Dict      							=			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 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Any      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    str):
      a						:		Any      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase)






    @slow
    def 	__snake_case							(					self				:    int):
      for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						:		Optional[int]      							=			TFEfficientFormerModel.from_pretrained(__UpperCAmelCase)
        self.assertIsNotNone(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]):
      a							,		a						:		List[str]      							=			self.model_tester.prepare_config_and_inputs_for_common()
      a						:		Dict      							=			True

      a						:		Dict      							=			getattr(self.model_tester				, "seq_length"				, __UpperCAmelCase)
      a						:		List[str]      							=			getattr(self.model_tester				, "encoder_seq_length"				, __UpperCAmelCase)
      a						:		List[Any]      							=			getattr(self.model_tester				, "key_length"				, __UpperCAmelCase)
      a						:		List[str]      							=			getattr(self.model_tester				, "chunk_length"				, __UpperCAmelCase)

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

      for model_class in self.all_model_classes:
        a						:		Optional[int]      							=			True
        a						:		Dict      							=			False
        a						:		Union[str, Any]      							=			True
        a						:		Any      							=			model_class(__UpperCAmelCase)

        a						:		Union[str, Any]      							=			model(**self._prepare_for_class(__UpperCAmelCase				, __UpperCAmelCase)				, training=__UpperCAmelCase)
        a						:		Union[str, Any]      							=			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"]
        a						:		str      							=			True
        a						:		Dict      							=			model_class(__UpperCAmelCase)
        a						:		int      							=			model(**self._prepare_for_class(__UpperCAmelCase				, __UpperCAmelCase)				, training=__UpperCAmelCase)

        a						:		Any      							=			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 	__snake_case							(					self				:    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						:		int      							=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        # Prepare our model
        a						:		Any      							=			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
        a						:		str      							=			{
            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
        }
        a						:		Optional[int]      							=			model(__UpperCAmelCase)
        self.assertTrue(outputs_dict is not None)






def      lowercase			(				)->      Dict:
  '''simple docstring'''







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







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







    """simple docstring"""




    @cached_property
    def 	__snake_case							(					self				:    Tuple):
      return (
          EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
          if is_vision_available()
          else None
      )






    @slow
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Any      							=			TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300")
      a						:		Optional[int]      							=			self.default_image_processor
      a						:		int      							=			prepare_img()
      a						:		Optional[Any]      							=			image_processor(images=__UpperCAmelCase				, return_tensors="tf")
      # forward pass
      a						:		Tuple      							=			model(**__UpperCAmelCase				, training=__UpperCAmelCase)
      # verify the logits
      a						:		str      							=			tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape				, __UpperCAmelCase)
      a						:		Any      							=			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 	__snake_case							(					self				:    Optional[Any]):
      a						:		List[Any]      							=			TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
          "snap-research/efficientformer-l1-300")
      a						:		int      							=			self.default_image_processor
      a						:		int      							=			prepare_img()
      a						:		str      							=			image_processor(images=__UpperCAmelCase				, return_tensors="tf")
      # forward pass
      a						:		Union[str, Any]      							=			model(**__UpperCAmelCase				, training=__UpperCAmelCase)
      # verify the logits
      a						:		str      							=			tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape				, __UpperCAmelCase)
      a						:		Any      							=			tf.constant([-0.1_312, 0.4_353, -1.0_499])
      self.assertTrue(np.allclose(outputs.logits[0, :3]				, __UpperCAmelCase				, atol=1e-4))








import os






def 							_lowercase							(     )		->		List[str]:
					'''simple docstring'''


					with open(os.path.dirname(UpperCamelCase_		) + '/p022_names.txt'		) as file:
										SCREAMING_SNAKE_CASE__													= str(file.readlines()[0]		)
										SCREAMING_SNAKE_CASE__													= names.replace('"'	,	''		).split(','		)

					names.sort()

					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0

					for i, name in enumerate(UpperCamelCase_		):
										for letter in name:
															name_score += ord(UpperCamelCase_		) - 64

										total_score += (i + 1) * name_score
										SCREAMING_SNAKE_CASE__													= 0
					return total_score


if __name__ == "__main__":
				print(solution())





