
'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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 numpy as np
   import tensorflow as tf

   from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
   from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.config_class(**self.inputs_dict      )
    self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,'hidden_sizes'      )      )
    self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,'neck_hidden_sizes'      )      )
    self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,'num_attention_heads'      )      )



class 		_a     :







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=13    ,__SCREAMING_SNAKE_CASE=32    ,__SCREAMING_SNAKE_CASE=2    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=640    ,__SCREAMING_SNAKE_CASE=4    ,__SCREAMING_SNAKE_CASE="silu"    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=32    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.02    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=10    ,__SCREAMING_SNAKE_CASE=None    ,):
    SCREAMING_SNAKE_CASE  :  Tuple								=		parent
    SCREAMING_SNAKE_CASE  :  Tuple								=		batch_size
    SCREAMING_SNAKE_CASE  :  List[str]								=		image_size
    SCREAMING_SNAKE_CASE  :  Dict								=		patch_size
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		num_channels
    SCREAMING_SNAKE_CASE  :  str								=		last_hidden_size
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		num_attention_heads
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		hidden_act
    SCREAMING_SNAKE_CASE  :  List[Any]								=		conv_kernel_size
    SCREAMING_SNAKE_CASE  :  Any								=		output_stride
    SCREAMING_SNAKE_CASE  :  str								=		hidden_dropout_prob
    SCREAMING_SNAKE_CASE  :  str								=		attention_probs_dropout_prob
    SCREAMING_SNAKE_CASE  :  Dict								=		classifier_dropout_prob
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		use_labels
    SCREAMING_SNAKE_CASE  :  str								=		is_training
    SCREAMING_SNAKE_CASE  :  str								=		num_labels
    SCREAMING_SNAKE_CASE  :  List[str]								=		initializer_range
    SCREAMING_SNAKE_CASE  :  Any								=		scope






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		None
    SCREAMING_SNAKE_CASE  :  int								=		None
    if self.use_labels:
     SCREAMING_SNAKE_CASE  :  str								=		ids_tensor([self.batch_size]    ,self.num_labels      )
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		ids_tensor([self.batch_size, self.image_size, self.image_size]    ,self.num_labels      )

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.get_config()

    return config, pixel_values, labels, pixel_labels






   def 					__a			(       self      ):
    return MobileViTConfig(
        image_size=self.image_size    ,patch_size=self.patch_size    ,num_channels=self.num_channels    ,num_attention_heads=self.num_attention_heads    ,hidden_act=self.hidden_act    ,conv_kernel_size=self.conv_kernel_size    ,output_stride=self.output_stride    ,hidden_dropout_prob=self.hidden_dropout_prob    ,attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,classifier_dropout_prob=self.classifier_dropout_prob    ,initializer_range=self.initializer_range    ,)






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		TFMobileViTModel(config=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		model(__SCREAMING_SNAKE_CASE    ,training=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.image_size // self.output_stride
    self.parent.assertEqual(
        result.last_hidden_state.shape    ,(self.batch_size, self.last_hidden_size, expected_height, expected_width)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.num_labels
    SCREAMING_SNAKE_CASE  :  str								=		TFMobileViTForImageClassification(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		model(__SCREAMING_SNAKE_CASE    ,labels=__SCREAMING_SNAKE_CASE    ,training=__SCREAMING_SNAKE_CASE      )
    self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.num_labels
    SCREAMING_SNAKE_CASE  :  List[Any]								=		TFMobileViTForSemanticSegmentation(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.image_size // self.output_stride

    SCREAMING_SNAKE_CASE  :  str								=		model(__SCREAMING_SNAKE_CASE    ,training=__SCREAMING_SNAKE_CASE      )
    self.parent.assertEqual(
        result.logits.shape    ,(self.batch_size, self.num_labels, expected_height, expected_width)      )

    SCREAMING_SNAKE_CASE  :  Any								=		model(__SCREAMING_SNAKE_CASE    ,labels=__SCREAMING_SNAKE_CASE    ,training=__SCREAMING_SNAKE_CASE      )
    self.parent.assertEqual(
        result.logits.shape    ,(self.batch_size, self.num_labels, expected_height, expected_width)      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.prepare_config_and_inputs()
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[str]								=		config_and_inputs
    SCREAMING_SNAKE_CASE  :  int								=		{'pixel_values': pixel_values}
    return config, inputs_dict



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







   """simple docstring"""




   A		= (
       (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
       if is_tf_available()
       else ()
   )
   A		= (
       {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
       if is_tf_available()
       else {}
   )

   A		= False
   A		= False
   A		= False
   A		= False
   A		= False






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		TFMobileViTModelTester(self      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		TFMobileViTConfigTester(self    ,config_class=__SCREAMING_SNAKE_CASE    ,has_text_modality=__SCREAMING_SNAKE_CASE      )






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






   @unittest.skip(reason='MobileViT does not use inputs_embeds'      )
   def 					__a			(       self      ):
    pass






   @unittest.skip(reason='MobileViT does not support input and output embeddings'      )
   def 					__a			(       self      ):
    pass






   @unittest.skip(reason='MobileViT does not output attentions'      )
   def 					__a			(       self      ):
    pass






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Dict								=		self.model_tester.prepare_config_and_inputs_for_common()

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

     SCREAMING_SNAKE_CASE  :  Any								=		['pixel_values']
     self.assertListEqual(arg_names[:1]    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    def check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Any								=		model_class(__SCREAMING_SNAKE_CASE      )

     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )      )

     SCREAMING_SNAKE_CASE  :  Dict								=		outputs.hidden_states

     SCREAMING_SNAKE_CASE  :  Any								=		5
     self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,__SCREAMING_SNAKE_CASE      )

     # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
     # with the width and height being successively divided by 2.
     SCREAMING_SNAKE_CASE  :  str								=		2
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
      self.assertListEqual(
          list(hidden_states[i].shape[-2:]      )    ,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,)
      divisor *= 2

     self.assertEqual(self.model_tester.output_stride    ,divisor // 2      )

    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		True
     check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     SCREAMING_SNAKE_CASE  :  List[str]								=		True

     check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE      )






   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU'      )      ) == 0    ,reason='TF does not support backprop for grouped convolutions on CPU.'    ,)
   def 					__a			(       self      ):
    super().test_dataset_conversion()






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=2e-1    ,__SCREAMING_SNAKE_CASE=2e-1      ):
    self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,atol=__SCREAMING_SNAKE_CASE    ,rtol=__SCREAMING_SNAKE_CASE      )      )






   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU'      )      ) == 0    ,reason='TF does not support backprop for grouped convolutions on CPU.'    ,)
   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[str]								=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
     if model_class.__name__ != "TFMobileViTModel":
      SCREAMING_SNAKE_CASE  :  Any								=		model_class(__SCREAMING_SNAKE_CASE      )
      if getattr(__SCREAMING_SNAKE_CASE    ,'hf_compute_loss'    ,__SCREAMING_SNAKE_CASE      ):
       super().test_keras_fit()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
     # set an ignore index to correctly test the masked loss used in
     # `TFMobileViTForSemanticSegmentation`.
     if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
      SCREAMING_SNAKE_CASE  :  int								=		5

     SCREAMING_SNAKE_CASE  :  Any								=		model_class(__SCREAMING_SNAKE_CASE      )
     if getattr(__SCREAMING_SNAKE_CASE    ,'hf_compute_loss'    ,__SCREAMING_SNAKE_CASE      ):
      # The number of elements in the loss should be the same as the number of elements in the label
      SCREAMING_SNAKE_CASE  :  List[Any]								=		self._prepare_for_class(inputs_dict.copy()    ,__SCREAMING_SNAKE_CASE    ,return_labels=__SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		prepared_for_class[
          sorted(prepared_for_class.keys() - inputs_dict.keys()    ,reverse=__SCREAMING_SNAKE_CASE      )[0]
      ]
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		added_label.shape.as_list()[:1]

      # Test that model correctly compute the loss with kwargs
      SCREAMING_SNAKE_CASE  :  Tuple								=		self._prepare_for_class(inputs_dict.copy()    ,__SCREAMING_SNAKE_CASE    ,return_labels=__SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'input_ids', 'pixel_values', 'input_features'}
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		possible_input_names.intersection(set(__SCREAMING_SNAKE_CASE      )      ).pop()
      SCREAMING_SNAKE_CASE  :  str								=		prepared_for_class.pop(__SCREAMING_SNAKE_CASE      )

      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )[0]
      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

      # Test that model correctly compute the loss when we mask some positions
      SCREAMING_SNAKE_CASE  :  List[str]								=		self._prepare_for_class(inputs_dict.copy()    ,__SCREAMING_SNAKE_CASE    ,return_labels=__SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE  :  str								=		{'input_ids', 'pixel_values', 'input_features'}
      SCREAMING_SNAKE_CASE  :  Optional[int]								=		possible_input_names.intersection(set(__SCREAMING_SNAKE_CASE      )      ).pop()
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		prepared_for_class.pop(__SCREAMING_SNAKE_CASE      )
      if "labels" in prepared_for_class:
       SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		prepared_for_class['labels'].numpy()
       if len(labels.shape      ) > 1 and labels.shape[1] != 1:
        # labels[0] = -100
        SCREAMING_SNAKE_CASE  :  List[Any]								=		tf.convert_to_tensor(__SCREAMING_SNAKE_CASE      )
        SCREAMING_SNAKE_CASE  :  List[str]								=		model(__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
        self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
      SCREAMING_SNAKE_CASE  :  Any								=		self._prepare_for_class(inputs_dict.copy()    ,__SCREAMING_SNAKE_CASE    ,return_labels=__SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE  :  Dict								=		model(__SCREAMING_SNAKE_CASE      )[0]
      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

      # Test that model correctly compute the loss with a tuple
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self._prepare_for_class(inputs_dict.copy()    ,__SCREAMING_SNAKE_CASE    ,return_labels=__SCREAMING_SNAKE_CASE      )

      # Get keys that were added with the _prepare_for_class function
      SCREAMING_SNAKE_CASE  :  List[str]								=		prepared_for_class.keys() - inputs_dict.keys()
      SCREAMING_SNAKE_CASE  :  List[Any]								=		inspect.signature(model.call      ).parameters
      SCREAMING_SNAKE_CASE  :  Tuple								=		list(signature.keys()      )

      # Create a dictionary holding the location of the tensors in the tuple
      SCREAMING_SNAKE_CASE  :  int								=		{0: input_name}
      for label_key in label_keys:
       SCREAMING_SNAKE_CASE  :  Optional[Any]								=		signature_names.index(__SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE  :  List[Any]								=		label_key
      SCREAMING_SNAKE_CASE  :  Dict								=		sorted(tuple_index_mapping.items()      )
      # Initialize a list with their default values, update the values and convert to a tuple
      SCREAMING_SNAKE_CASE  :  str								=		[]

      for name in signature_names:
       if name != "kwargs":
        list_input.append(signature[name].default      )

      for index, value in sorted_tuple_index_mapping:
       SCREAMING_SNAKE_CASE  :  List[Any]								=		prepared_for_class[value]

      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tuple(__SCREAMING_SNAKE_CASE      )

      # Send to model
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		model(tuple_input[:-1]      )[0]

      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )






   @slow
   def 					__a			(       self      ):
    for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     SCREAMING_SNAKE_CASE  :  int								=		TFMobileViTModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
     self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )



def        SCREAMING_SNAKE_CASE_  (	)       ->	Any:
 SCREAMING_SNAKE_CASE  :  Any								=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
 return image





@require_tf
class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'      )

    SCREAMING_SNAKE_CASE  :  int								=		MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		prepare_img()
    SCREAMING_SNAKE_CASE  :  Dict								=		image_processor(images=__SCREAMING_SNAKE_CASE    ,return_tensors='tf'      )

    # forward pass
    SCREAMING_SNAKE_CASE  :  Tuple								=		model(**__SCREAMING_SNAKE_CASE    ,training=__SCREAMING_SNAKE_CASE      )

    # verify the logits
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tf.TensorShape((1, 1000)      )
    self.assertEqual(outputs.logits.shape    ,__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tf.constant([-1.9364, -1.2327, -0.4653]      )

    tf.debugging.assert_near(outputs.logits[0, :3]    ,__SCREAMING_SNAKE_CASE    ,atol=1e-4    ,rtol=1e-04      )






   @slow
   def 					__a			(       self      ):
    # `from_pt` will be removed
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'      )

    SCREAMING_SNAKE_CASE  :  Any								=		prepare_img()
    SCREAMING_SNAKE_CASE  :  Any								=		image_processor(images=__SCREAMING_SNAKE_CASE    ,return_tensors='tf'      )

    # forward pass
    SCREAMING_SNAKE_CASE  :  List[str]								=		model(inputs.pixel_values    ,training=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		outputs.logits

    # verify the logits
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tf.TensorShape((1, 21, 32, 32)      )
    self.assertEqual(logits.shape    ,__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		tf.constant(
        [
            [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
            [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
            [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
        ]      )

    tf.debugging.assert_near(logits[0, :3, :3, :3]    ,__SCREAMING_SNAKE_CASE    ,rtol=1e-4    ,atol=1e-4      )




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str = "mytext.txt"   ,     snake_case_				:	bool = False							)       ->	None:
 import socket

 SCREAMING_SNAKE_CASE  :  Optional[int]								=		1_2312  # Reserve a port for your service.
 SCREAMING_SNAKE_CASE  :  Any								=		socket.socket()  # Create a socket object
 SCREAMING_SNAKE_CASE  :  int								=		socket.gethostname()  # Get local machine name
 sock.bind((host, port)							)  # Bind to the port
 sock.listen(5							)  # Now wait for client connection.

 print('Server listening....'							)

 while True:
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[Any]								=		sock.accept()  # Establish connection with client.
  print(f"""Got connection from {addr}"""							)
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		conn.recv(1024							)
  print(f"""Server received: {data = }"""							)

  with open(snake_case_   ,     'rb'							) as in_file:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		in_file.read(1024							)
   while data:
    conn.send(snake_case_							)
    print(f"""Sent {data!r}"""							)
    SCREAMING_SNAKE_CASE  :  List[Any]								=		in_file.read(1024							)

  print('Done sending'							)
  conn.close()
  if testing:  # Allow the test to complete
   break

 sock.shutdown(1							)
 sock.close()


if __name__ == "__main__":
   send_file()


