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 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(_UpperCamelCase     ,	"""hidden_sizes"""))
          self.parent.assertTrue(hasattr(_UpperCamelCase     ,	"""neck_hidden_sizes"""))
          self.parent.assertTrue(hasattr(_UpperCamelCase     ,	"""num_attention_heads"""))





class 						__snake_case						:





      '''simple docstring'''






      def __init__(       self  :					str     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					List[str]=13     ,	_UpperCamelCase  :					int=32     ,	_UpperCamelCase  :					str=2     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Any=640     ,	_UpperCamelCase  :					Union[str, Any]=4     ,	_UpperCamelCase  :					List[str]="silu"     ,	_UpperCamelCase  :					List[str]=3     ,	_UpperCamelCase  :					Union[str, Any]=32     ,	_UpperCamelCase  :					Optional[int]=0.1     ,	_UpperCamelCase  :					int=0.1     ,	_UpperCamelCase  :					Tuple=0.1     ,	_UpperCamelCase  :					Union[str, Any]=0.0_2     ,	_UpperCamelCase  :					int=True     ,	_UpperCamelCase  :					Tuple=True     ,	_UpperCamelCase  :					Any=10     ,	_UpperCamelCase  :					List[str]=None     ,	)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    parent
          _lowerCamelCase  :		Any			    =    batch_size
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		List[Any]			    =    patch_size
          _lowerCamelCase  :		Dict			    =    num_channels
          _lowerCamelCase  :		Optional[int]			    =    last_hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_attention_heads
          _lowerCamelCase  :		Tuple			    =    hidden_act
          _lowerCamelCase  :		Optional[Any]			    =    conv_kernel_size
          _lowerCamelCase  :		Union[str, Any]			    =    output_stride
          _lowerCamelCase  :		Union[str, Any]			    =    hidden_dropout_prob
          _lowerCamelCase  :		Dict			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		List[str]			    =    classifier_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    use_labels
          _lowerCamelCase  :		int			    =    is_training
          _lowerCamelCase  :		List[str]			    =    num_labels
          _lowerCamelCase  :		Optional[Any]			    =    initializer_range
          _lowerCamelCase  :		Optional[Any]			    =    scope



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          _lowerCamelCase  :		Any			    =    None
          _lowerCamelCase  :		List[str]			    =    None
          if self.use_labels:
              _lowerCamelCase  :		Tuple			    =    ids_tensor([self.batch_size]     ,	self.num_labels)
              _lowerCamelCase  :		str			    =    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,	self.num_labels)

          _lowerCamelCase  :		Tuple			    =    self.get_config()

          return config, pixel_values, labels, pixel_labels



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Tuple:


          """simple docstring"""


          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 						_SCREAMING_SNAKE_CASE     (       self  :					Dict     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					int)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    TFMobileViTModel(config=_UpperCamelCase)
          _lowerCamelCase  :		str			    =    model(_UpperCamelCase     ,	training=_UpperCamelCase)
          _lowerCamelCase  :		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 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Optional[Any])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    self.num_labels
          _lowerCamelCase  :		List[str]			    =    TFMobileViTForImageClassification(_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase     ,	labels=_UpperCamelCase     ,	training=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Optional[Any])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    self.num_labels
          _lowerCamelCase  :		Optional[int]			    =    TFMobileViTForSemanticSegmentation(_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    self.image_size // self.output_stride

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

          _lowerCamelCase  :		Optional[int]			    =    model(_UpperCamelCase     ,	labels=_UpperCamelCase     ,	training=_UpperCamelCase)
          self.parent.assertEqual(
              result.logits.shape     ,	(self.batch_size, self.num_labels, expected_height, expected_width))




      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->str:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    self.prepare_config_and_inputs()
          _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    config_and_inputs
          _lowerCamelCase  :		Tuple			    =    {"""pixel_values""": pixel_values}
          return config, inputs_dict





@require_tf
class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






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

      _snake_case     		=							False
      _snake_case     		=							False
      _snake_case     		=							False
      _snake_case     		=							False
      _snake_case     		=							False



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->str:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    TFMobileViTModelTester(self)
          _lowerCamelCase  :		List[Any]			    =    TFMobileViTConfigTester(self     ,	config_class=_UpperCamelCase     ,	has_text_modality=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Union[str, Any]:


          """simple docstring"""


          self.config_tester.run_common_tests()



      @unittest.skip(reason="""MobileViT does not use inputs_embeds""")
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Any:


          """simple docstring"""


          pass



      @unittest.skip(reason="""MobileViT does not support input and output embeddings""")
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->List[Any]:


          """simple docstring"""


          pass



      @unittest.skip(reason="""MobileViT does not output attentions""")
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Dict:


          """simple docstring"""


          pass



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase    ,				_lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              _lowerCamelCase  :		Dict			    =    model_class(_UpperCamelCase)
              _lowerCamelCase  :		Union[str, Any]			    =    inspect.signature(model.call)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _lowerCamelCase  :		Union[str, Any]			    =    [*signature.parameters.keys()]

              _lowerCamelCase  :		Tuple			    =    ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]     ,	_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->int:


          """simple docstring"""


          def check_hidden_states_output(_UpperCamelCase  :					str     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Union[str, Any]):
              _lowerCamelCase  :		Union[str, Any]			    =    model_class(_UpperCamelCase)

              _lowerCamelCase  :		Optional[int]			    =    model(**self._prepare_for_class(_UpperCamelCase     ,	_UpperCamelCase))

              _lowerCamelCase  :		Dict			    =    outputs.hidden_states

              _lowerCamelCase  :		Tuple			    =    5
              self.assertEqual(len(_UpperCamelCase)     ,	_UpperCamelCase)

              # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
              # with the width and height being successively divided by 2.
              _lowerCamelCase  :		Any			    =    2
              for i in range(len(_UpperCamelCase)):
                  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)

          _lowerCamelCase    ,				_lowerCamelCase  :		Optional[Any]			    =    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              _lowerCamelCase  :		Optional[Any]			    =    True
              check_hidden_states_output(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _lowerCamelCase  :		Dict			    =    True

              check_hidden_states_output(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase)



      @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 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Dict:


          """simple docstring"""


          super().test_dataset_conversion()



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Any=2E-1     ,	_UpperCamelCase  :					Optional[Any]=2E-1)					->Union[str, Any]:


          """simple docstring"""


          self.assertTrue(np.allclose(_UpperCamelCase     ,	_UpperCamelCase     ,	atol=_UpperCamelCase     ,	rtol=_UpperCamelCase))



      @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 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->str:


          """simple docstring"""


          _lowerCamelCase    ,				_lowerCamelCase  :		Union[str, Any]			    =    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":
                  _lowerCamelCase  :		Tuple			    =    model_class(_UpperCamelCase)
                  if getattr(_UpperCamelCase     ,	"""hf_compute_loss"""     ,	_UpperCamelCase):
                      super().test_keras_fit()



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Tuple:


          """simple docstring"""


          _lowerCamelCase    ,				_lowerCamelCase  :		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":
                  _lowerCamelCase  :		Dict			    =    5

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

                  # Test that model correctly compute the loss with kwargs
                  _lowerCamelCase  :		Tuple			    =    self._prepare_for_class(inputs_dict.copy()     ,	_UpperCamelCase     ,	return_labels=_UpperCamelCase)
                  _lowerCamelCase  :		int			    =    {"""input_ids""", """pixel_values""", """input_features"""}
                  _lowerCamelCase  :		List[Any]			    =    possible_input_names.intersection(set(_UpperCamelCase)).pop()
                  _lowerCamelCase  :		int			    =    prepared_for_class.pop(_UpperCamelCase)

                  _lowerCamelCase  :		List[Any]			    =    model(_UpperCamelCase     ,	**_UpperCamelCase)[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
                  _lowerCamelCase  :		Union[str, Any]			    =    self._prepare_for_class(inputs_dict.copy()     ,	_UpperCamelCase     ,	return_labels=_UpperCamelCase)
                  _lowerCamelCase  :		Dict			    =    {"""input_ids""", """pixel_values""", """input_features"""}
                  _lowerCamelCase  :		Any			    =    possible_input_names.intersection(set(_UpperCamelCase)).pop()
                  _lowerCamelCase  :		str			    =    prepared_for_class.pop(_UpperCamelCase)
                  if "labels" in prepared_for_class:
                      _lowerCamelCase  :		Optional[Any]			    =    prepared_for_class["""labels"""].numpy()
                      if len(labels.shape) > 1 and labels.shape[1] != 1:
                          # labels[0] = -100
                          _lowerCamelCase  :		List[Any]			    =    tf.convert_to_tensor(_UpperCamelCase)
                          _lowerCamelCase  :		Optional[int]			    =    model(_UpperCamelCase     ,	**_UpperCamelCase)[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
                  _lowerCamelCase  :		Any			    =    self._prepare_for_class(inputs_dict.copy()     ,	_UpperCamelCase     ,	return_labels=_UpperCamelCase)
                  _lowerCamelCase  :		List[str]			    =    model(_UpperCamelCase)[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
                  _lowerCamelCase  :		Dict			    =    self._prepare_for_class(inputs_dict.copy()     ,	_UpperCamelCase     ,	return_labels=_UpperCamelCase)

                  # Get keys that were added with the _prepare_for_class function
                  _lowerCamelCase  :		Dict			    =    prepared_for_class.keys() - inputs_dict.keys()
                  _lowerCamelCase  :		Optional[int]			    =    inspect.signature(model.call).parameters
                  _lowerCamelCase  :		Optional[int]			    =    list(signature.keys())

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

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

                  for index, value in sorted_tuple_index_mapping:
                      _lowerCamelCase  :		Dict			    =    prepared_for_class[value]

                  _lowerCamelCase  :		List[Any]			    =    tuple(_UpperCamelCase)

                  # Send to model
                  _lowerCamelCase  :		Optional[int]			    =    model(tuple_input[:-1])[0]

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




      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCamelCase  :		Optional[int]			    =    TFMobileViTModel.from_pretrained(_UpperCamelCase)
              self.assertIsNotNone(_UpperCamelCase)







def     A__	(       ):







    '''simple docstring'''
    _lowerCamelCase  :		Union[str, Any]			    =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    return image




@require_tf
class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""")

          _lowerCamelCase  :		int			    =    MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""")
          _lowerCamelCase  :		Any			    =    prepare_img()
          _lowerCamelCase  :		Dict			    =    image_processor(images=_UpperCamelCase     ,	return_tensors="""tf""")

          # forward pass
          _lowerCamelCase  :		str			    =    model(**_UpperCamelCase     ,	training=_UpperCamelCase)

          # verify the logits
          _lowerCamelCase  :		Optional[Any]			    =    tf.TensorShape((1, 1000))
          self.assertEqual(outputs.logits.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3])

          tf.debugging.assert_near(outputs.logits[0, :3]     ,	_UpperCamelCase     ,	atol=1E-4     ,	rtol=1E-0_4)




      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->str:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""")

          _lowerCamelCase  :		List[Any]			    =    MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""")

          _lowerCamelCase  :		List[str]			    =    prepare_img()
          _lowerCamelCase  :		int			    =    image_processor(images=_UpperCamelCase     ,	return_tensors="""tf""")

          # forward pass
          _lowerCamelCase  :		Optional[Any]			    =    model(inputs.pixel_values     ,	training=_UpperCamelCase)
          _lowerCamelCase  :		Any			    =    outputs.logits

          # verify the logits
          _lowerCamelCase  :		int			    =    tf.TensorShape((1, 21, 32, 32))
          self.assertEqual(logits.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		Tuple			    =    tf.constant(
              [
                  [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
                  [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
                  [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
              ])

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





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    TaConfig.from_pretrained(__A						)
    _lowerCamelCase  :		List[Any]			    =    FlaxTaForConditionalGeneration(config=__A						)
    _lowerCamelCase  :		Tuple			    =    checkpoints.load_tax_checkpoint(__A						)

    _lowerCamelCase  :		Union[str, Any]			    =    """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

    # Encoder
    for layer_index in range(config.num_layers						):
        _lowerCamelCase  :		Optional[int]			    =    F"""layers_{str(__A						)}"""

        # Self-Attention
        _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
        _lowerCamelCase  :		int			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
        _lowerCamelCase  :		int			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

        if split_mlp_wi:
            _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        _lowerCamelCase  :		int			    =    tax_attention_key
        _lowerCamelCase  :		Tuple			    =    tax_attention_out
        _lowerCamelCase  :		List[str]			    =    tax_attention_query
        _lowerCamelCase  :		Tuple			    =    tax_attention_value

        _lowerCamelCase  :		Dict			    =    tax_attention_layer_norm

        if split_mlp_wi:
            _lowerCamelCase  :		Dict			    =    tax_mlp_wi_a
            _lowerCamelCase  :		Optional[int]			    =    tax_mlp_wi_a
        else:
            _lowerCamelCase  :		List[str]			    =    tax_mlp_wi

        _lowerCamelCase  :		Tuple			    =    tax_mlp_wo
        _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_layer_norm

    # Only for layer 0:
    _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    _lowerCamelCase  :		List[Any]			    =    tax_encoder_rel_embedding

    # Assigning
    _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
    _lowerCamelCase  :		int			    =    tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers						):
        _lowerCamelCase  :		Union[str, Any]			    =    F"""layers_{str(__A						)}"""

        # Self-Attention
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
        _lowerCamelCase  :		Any			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
            """scale"""
        ]

        # Encoder-Decoder-Attention
        _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
            """kernel"""
        ]

        # Layer Normalization
        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

        # MLP
        if split_mlp_wi:
            _lowerCamelCase  :		str			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_key
        _lowerCamelCase  :		Tuple			    =    tax_attention_out
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_query
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_value

        _lowerCamelCase  :		Optional[Any]			    =    tax_pre_attention_layer_norm

        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_key
        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_out
        _lowerCamelCase  :		Tuple			    =    tax_enc_dec_attention_query
        _lowerCamelCase  :		Dict			    =    tax_enc_dec_attention_value

        _lowerCamelCase  :		List[Any]			    =    tax_cross_layer_norm

        if split_mlp_wi:
            _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_wi_a
            _lowerCamelCase  :		Tuple			    =    tax_mlp_wi_a
        else:
            _lowerCamelCase  :		Any			    =    tax_mlp_wi

        _lowerCamelCase  :		Optional[int]			    =    tax_mlp_wo

        _lowerCamelCase  :		List[str]			    =    txa_mlp_layer_norm

    # Decoder Normalization
    _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
    _lowerCamelCase  :		Union[str, Any]			    =    txa_decoder_norm

    # Only for layer 0:
    _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    _lowerCamelCase  :		Tuple			    =    tax_decoder_rel_embedding

    # Token Embeddings
    _lowerCamelCase  :		List[str]			    =    tax_model["""target"""]["""token_embedder"""]["""embedding"""]
    _lowerCamelCase  :		List[str]			    =    txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

    flax_model.save_pretrained(__A						)
    print("""T5X Model was sucessfully converted!"""						)


if __name__ == "__main__":
    lowerCAmelCase      :				Any     			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
    )
    parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
    parser.add_argument(
        "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
    )
    lowerCAmelCase      :				Any     			=parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




