




'''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       lowerCAmelCase				(	UpperCamelCase_):


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					self.config_class(**self.inputs_dict					)
            self.parent.assertTrue(hasattr(lowercase_      ,					'''hidden_sizes'''					)					)
            self.parent.assertTrue(hasattr(lowercase_      ,					'''neck_hidden_sizes'''					)					)
            self.parent.assertTrue(hasattr(lowercase_      ,					'''num_attention_heads'''					)					)





class       lowerCAmelCase				:


       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      ,					) ->					int:



            '''simple docstring'''




            __snake_case         =					parent
            __snake_case         =					batch_size
            __snake_case         =					image_size
            __snake_case         =					patch_size
            __snake_case         =					num_channels
            __snake_case         =					last_hidden_size
            __snake_case         =					num_attention_heads
            __snake_case         =					hidden_act
            __snake_case         =					conv_kernel_size
            __snake_case         =					output_stride
            __snake_case         =					hidden_dropout_prob
            __snake_case         =					attention_probs_dropout_prob
            __snake_case         =					classifier_dropout_prob
            __snake_case         =					use_labels
            __snake_case         =					is_training
            __snake_case         =					num_labels
            __snake_case         =					initializer_range
            __snake_case         =					scope


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            __snake_case         =					None
            __snake_case         =					None
            if self.use_labels:
                 __snake_case         =					ids_tensor([self.batch_size]      ,					self.num_labels					)
                 __snake_case         =					ids_tensor([self.batch_size, self.image_size, self.image_size]      ,					self.num_labels					)

            __snake_case         =					self.get_config()

            return config, pixel_values, labels, pixel_labels


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




            __snake_case         =					TFMobileViTModel(config=lowercase_					)
            __snake_case         =					model(lowercase_      ,					training=lowercase_					)
            __snake_case         =					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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					self.num_labels
            __snake_case         =					TFMobileViTForImageClassification(lowercase_					)
            __snake_case         =					model(lowercase_      ,					labels=lowercase_      ,					training=lowercase_					)
            self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels)					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					List[Any]:



            '''simple docstring'''




            __snake_case         =					self.num_labels
            __snake_case         =					TFMobileViTForSemanticSegmentation(lowercase_					)
            __snake_case         =					self.image_size // self.output_stride

            __snake_case         =					model(lowercase_      ,					training=lowercase_					)
            self.parent.assertEqual(
                result.logits.shape      ,					(self.batch_size, self.num_labels, expected_height, expected_width)					)

            __snake_case         =					model(lowercase_      ,					labels=lowercase_      ,					training=lowercase_					)
            self.parent.assertEqual(
                result.logits.shape      ,					(self.batch_size, self.num_labels, expected_height, expected_width)					)




       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					self.prepare_config_and_inputs()
            __snake_case         =					config_and_inputs
            __snake_case         =					{'pixel_values': pixel_values}
            return config, inputs_dict





@require_tf
class       lowerCAmelCase				(	UpperCamelCase_			,				UpperCamelCase_			,				unittest.TestCase):
       __lowercase					: Union[str, Any]	    =     (
           (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
           if is_tf_available()
           else ()
       )
       __lowercase					: List[str]	    =     (
           {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
           if is_tf_available()
           else {}
       )

       __lowercase					: Any	    =     False
       __lowercase					: Optional[Any]	    =     False
       __lowercase					: Tuple	    =     False
       __lowercase					: Optional[int]	    =     False
       __lowercase					: Optional[int]	    =     False


       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					TFMobileViTModelTester(self					)
            __snake_case         =					TFMobileViTConfigTester(self      ,					config_class=lowercase_      ,					has_text_modality=lowercase_					)


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



            '''simple docstring'''




            self.config_tester.run_common_tests()


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



            '''simple docstring'''




            pass


       @unittest.skip(reason='''MobileViT does not support input and output embeddings'''					)
       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            pass


       @unittest.skip(reason='''MobileViT does not output attentions'''					)
       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            pass


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

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

                 __snake_case         =					['pixel_values']
                 self.assertListEqual(arg_names[:1]      ,					lowercase_					)


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase_					)


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            def check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					model_class(lowercase_					)

                 __snake_case         =					model(**self._prepare_for_class(lowercase_      ,					lowercase_					)					)

                 __snake_case         =					outputs.hidden_states

                 __snake_case         =					5
                 self.assertEqual(len(lowercase_					)      ,					lowercase_					)

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

            __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case         =					True
                 check_hidden_states_output(lowercase_      ,					lowercase_      ,					lowercase_					)

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

                 check_hidden_states_output(lowercase_      ,					lowercase_      ,					lowercase_					)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowercase_					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_					)


       @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  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            super().test_dataset_conversion()


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=2E-1      ,					__SCREAMING_SNAKE_CASE=2E-1					) ->					List[Any]:



            '''simple docstring'''




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


       @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  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					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":
                      __snake_case         =					model_class(lowercase_					)
                      if getattr(lowercase_      ,					'''hf_compute_loss'''      ,					lowercase_					):
                           super().test_keras_fit()


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					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":
                      __snake_case         =					5

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

                      # Test that model correctly compute the loss with kwargs
                      __snake_case         =					self._prepare_for_class(inputs_dict.copy()      ,					lowercase_      ,					return_labels=lowercase_					)
                      __snake_case         =					{'input_ids', 'pixel_values', 'input_features'}
                      __snake_case         =					possible_input_names.intersection(set(lowercase_					)					).pop()
                      __snake_case         =					prepared_for_class.pop(lowercase_					)

                      __snake_case         =					model(lowercase_      ,					**lowercase_					)[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
                      __snake_case         =					self._prepare_for_class(inputs_dict.copy()      ,					lowercase_      ,					return_labels=lowercase_					)
                      __snake_case         =					{'input_ids', 'pixel_values', 'input_features'}
                      __snake_case         =					possible_input_names.intersection(set(lowercase_					)					).pop()
                      __snake_case         =					prepared_for_class.pop(lowercase_					)
                      if "labels" in prepared_for_class:
                           __snake_case         =					prepared_for_class['labels'].numpy()
                           if len(labels.shape					) > 1 and labels.shape[1] != 1:
                                # labels[0] = -100
                                __snake_case         =					tf.convert_to_tensor(lowercase_					)
                                __snake_case         =					model(lowercase_      ,					**lowercase_					)[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
                      __snake_case         =					self._prepare_for_class(inputs_dict.copy()      ,					lowercase_      ,					return_labels=lowercase_					)
                      __snake_case         =					model(lowercase_					)[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
                      __snake_case         =					self._prepare_for_class(inputs_dict.copy()      ,					lowercase_      ,					return_labels=lowercase_					)

                      # Get keys that were added with the _prepare_for_class function
                      __snake_case         =					prepared_for_class.keys() - inputs_dict.keys()
                      __snake_case         =					inspect.signature(model.call					).parameters
                      __snake_case         =					list(signature.keys()					)

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

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

                      for index, value in sorted_tuple_index_mapping:
                           __snake_case         =					prepared_for_class[value]

                      __snake_case         =					tuple(lowercase_					)

                      # Send to model
                      __snake_case         =					model(tuple_input[:-1]					)[0]

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




       @slow
       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case         =					TFMobileViTModel.from_pretrained(lowercase_					)
                 self.assertIsNotNone(lowercase_					)







def      _UpperCamelCase	()->				Dict:
     __snake_case         =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
     return image







@require_tf
class       lowerCAmelCase				(	unittest.TestCase):


       @slow
       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					TFMobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''					)

            __snake_case         =					MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''					)
            __snake_case         =					prepare_img()
            __snake_case         =					image_processor(images=lowercase_      ,					return_tensors='''tf'''					)

            # forward pass
            __snake_case         =					model(**lowercase_      ,					training=lowercase_					)

            # verify the logits
            __snake_case         =					tf.TensorShape((1, 1000)					)
            self.assertEqual(outputs.logits.shape      ,					lowercase_					)

            __snake_case         =					tf.constant([-1.9_364, -1.2_327, -0.4_653]					)

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




       @slow
       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case         =					TFMobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''					)

            __snake_case         =					MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''					)

            __snake_case         =					prepare_img()
            __snake_case         =					image_processor(images=lowercase_      ,					return_tensors='''tf'''					)

            # forward pass
            __snake_case         =					model(inputs.pixel_values      ,					training=lowercase_					)
            __snake_case         =					outputs.logits

            # verify the logits
            __snake_case         =					tf.TensorShape((1, 21, 32, 32)					)
            self.assertEqual(logits.shape      ,					lowercase_					)

            __snake_case         =					tf.constant(
                [
                    [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
                    [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
                    [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
                ]					)

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












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

     __snake_case         =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_encoder_rel_embedding

     # Assigning
     __snake_case         =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __snake_case         =					tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

     # Decoder Normalization
     __snake_case         =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __snake_case         =					txa_decoder_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_decoder_rel_embedding

     # Token Embeddings
     __snake_case         =					tax_model['''target''']['''token_embedder''']['''embedding''']
     __snake_case         =					txa_token_embeddings

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

     flax_model.save_pretrained(_lowerCamelCase		)
     print('''T5X Model was sucessfully converted!'''		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[str]					      =						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.'''
     )
     UpperCAmelCase_    :					List[Any]					      =						parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






