"""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 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase = 13  ,						lowerCamelCase = 64  ,						lowerCamelCase = 2  ,						lowerCamelCase = 3  ,						lowerCamelCase = 3  ,						lowerCamelCase = True  ,						lowerCamelCase = True  ,						lowerCamelCase = 128  ,						lowerCamelCase=[16, 32, 64, 128]  ,						lowerCamelCase = 7  ,						lowerCamelCase = 4  ,						lowerCamelCase = 37  ,						lowerCamelCase = "gelu"  ,						lowerCamelCase = 0.1  ,						lowerCamelCase = 0.1  ,						lowerCamelCase = 10  ,						lowerCamelCase = 0.02  ,						lowerCamelCase = 2  ,						lowerCamelCase = 1  ,						lowerCamelCase = 128  ,						lowerCamelCase = [2, 2, 2, 2]  ,						lowerCamelCase = 2  ,						lowerCamelCase = 2  ,						):
        __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 		a__ (				self		):
        __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 		a__ (				self		):
        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=lowerCamelCase  ,						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  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  TFEfficientFormerModel(config=lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						training=lowerCamelCase		)
        self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.type_sequence_label_size
        __a     				=  TFEfficientFormerForImageClassification(lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						labels=lowerCamelCase  ,						training=lowerCamelCase		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        __a     				=  1
        __a     				=  TFEfficientFormerForImageClassification(lowerCamelCase		)

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






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






@require_tf
class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			str           =		(
     (
         TFEfficientFormerModel,
         TFEfficientFormerForImageClassificationWithTeacher,
         TFEfficientFormerForImageClassification,
     )
     if is_tf_available()
     else ()
 )
 _snake_case   :			str           =		(
     {
         """feature-extraction""": TFEfficientFormerModel,
         """image-classification""": (
             TFEfficientFormerForImageClassification,
             TFEfficientFormerForImageClassificationWithTeacher,
         ),
     }
     if is_tf_available()
     else {}
 )

 _snake_case   :			str           =		False

 _snake_case   :			Optional[int]           =		False
 _snake_case   :			Optional[Any]           =		False
 _snake_case   :			Dict           =		False
 _snake_case   :			Dict           =		False


 def 		a__ (				self		):
        __a     				=  TFEfficientFormerModelTester(self		)
        __a     				=  ConfigTester(
            self  ,						config_class=lowerCamelCase  ,						has_text_modality=lowerCamelCase  ,						hidden_size=37		)


 def 		a__ (				self		):
        self.config_tester.run_common_tests()


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


 @unittest.skip(reason="EfficientFormer does not support input and output embeddings"		)
 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a     				=  model_class(lowerCamelCase		)
               __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]  ,						lowerCamelCase		)


 def 		a__ (				self		):
        def check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  model_class(lowerCamelCase		)

               __a     				=  model(**self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)  ,						training=lowerCamelCase		)
               __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(lowerCamelCase		)  ,						lowerCamelCase		)

               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(lowerCamelCase  ,						(list, tuple)		)
                      self.assertEqual(len(lowerCamelCase		)  ,						lowerCamelCase		)
                      __a     				=  getattr(self.model_tester  ,						"seq_length"  ,						lowerCamelCase		)
                      __a     				=  getattr(self.model_tester  ,						"decoder_seq_length"  ,						lowerCamelCase		)

                      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(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

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

               check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase=False		):
        __a     				=  super()._prepare_for_class(lowerCamelCase  ,						lowerCamelCase  ,						return_labels=lowerCamelCase		)

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

        return inputs_dict


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase		)


 @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"		)
 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


 @slow
 def 		a__ (				self		):
        for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a     				=  TFEfficientFormerModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()
        __a     				=  True

        __a     				=  getattr(self.model_tester  ,						"seq_length"  ,						lowerCamelCase		)
        __a     				=  getattr(self.model_tester  ,						"encoder_seq_length"  ,						lowerCamelCase		)
        __a     				=  getattr(self.model_tester  ,						"key_length"  ,						lowerCamelCase		)
        __a     				=  getattr(self.model_tester  ,						"chunk_length"  ,						lowerCamelCase		)

        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(lowerCamelCase		)

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

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

               __a     				=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
               self.assertEqual(len(lowerCamelCase		)  ,						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		):
        # 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(lowerCamelCase		)
               # 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=lowerCamelCase		)
                   for key, val in model.input_signature.items()
                   if key in model.dummy_inputs
               }
               __a     				=  model(lowerCamelCase		)
               self.assertTrue(outputs_dict is not None		)



def 					_lowerCamelCase(					):
       __a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
       return image






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


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


 @slow
 def 		a__ (				self		):
        __a     				=  TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"		)
        __a     				=  self.default_image_processor
        __a     				=  prepare_img()
        __a     				=  image_processor(images=lowerCamelCase  ,						return_tensors="tf"		)
        # forward pass
        __a     				=  model(**lowerCamelCase  ,						training=lowerCamelCase		)
        # verify the logits
        __a     				=  tf.TensorShape((1, 1000)		)
        self.assertEqual(outputs.logits.shape  ,						lowerCamelCase		)
        __a     				=  tf.constant([-0.0555, 0.4825, -0.0852]		)
        self.assertTrue(np.allclose(outputs.logits[0, :3]  ,						lowerCamelCase  ,						atol=1E-4		)		)






 @slow
 def 		a__ (				self		):
        __a     				=  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
            "snap-research/efficientformer-l1-300"		)
        __a     				=  self.default_image_processor
        __a     				=  prepare_img()
        __a     				=  image_processor(images=lowerCamelCase  ,						return_tensors="tf"		)
        # forward pass
        __a     				=  model(**lowerCamelCase  ,						training=lowerCamelCase		)
        # verify the logits
        __a     				=  tf.TensorShape((1, 1000)		)
        self.assertEqual(outputs.logits.shape  ,						lowerCamelCase		)
        __a     				=  tf.constant([-0.1312, 0.4353, -1.0499]		)
        self.assertTrue(np.allclose(outputs.logits[0, :3]  ,						lowerCamelCase  ,						atol=1E-4		)		)

"""simple docstring"""



SCREAMING_SNAKE_CASE__:Any			=	{
    """A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
    """H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
    """O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
    """V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
    """2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
    """8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
    """:""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
    """?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
    """(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE__:Any			=	{value: key for key, value in MORSE_CODE_DICT.items()}



def 					_lowerCamelCase(					a							):
       return " ".join(MORSE_CODE_DICT[char] for char in message.upper()							)



def 					_lowerCamelCase(					a							):
       return "".join(REVERSE_DICT[char] for char in message.split()							)



def 					_lowerCamelCase(					):
       __a     				=  "Morse code here!"
       print(a							)
       __a     				=  encrypt(a							)
       print(a							)
       __a     				=  decrypt(a							)
       print(a							)


if __name__ == "__main__":
   main()
