from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, 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 tensorflow as tf

       from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class 			UpperCAmelCase__    :



  """simple docstring"""






  def __init__(				self:			str				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Optional[Any]=13				,				__lowerCAmelCase:			Tuple=30				,				__lowerCAmelCase:			Tuple=2				,				__lowerCAmelCase:			Any=3				,				__lowerCAmelCase:			List[str]=True				,				__lowerCAmelCase:			Dict=True				,				__lowerCAmelCase:			List[Any]=32				,				__lowerCAmelCase:			Any=2				,				__lowerCAmelCase:			Any=4				,				__lowerCAmelCase:			Union[str, Any]=37				,				__lowerCAmelCase:			str="gelu"				,				__lowerCAmelCase:			Optional[int]=0.1				,				__lowerCAmelCase:			str=0.1				,				__lowerCAmelCase:			Union[str, Any]=10				,				__lowerCAmelCase:			Union[str, Any]=0.02				,				__lowerCAmelCase:			Optional[Any]=3				,				__lowerCAmelCase:			int=0.6				,				__lowerCAmelCase:			Optional[int]=None				,				) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       parent
    __UpperCAmelCase						    =       batch_size
    __UpperCAmelCase						    =       image_size
    __UpperCAmelCase						    =       patch_size
    __UpperCAmelCase						    =       num_channels
    __UpperCAmelCase						    =       is_training
    __UpperCAmelCase						    =       use_labels
    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       num_hidden_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       intermediate_size
    __UpperCAmelCase						    =       hidden_act
    __UpperCAmelCase						    =       hidden_dropout_prob
    __UpperCAmelCase						    =       attention_probs_dropout_prob
    __UpperCAmelCase						    =       type_sequence_label_size
    __UpperCAmelCase						    =       initializer_range
    __UpperCAmelCase						    =       mask_ratio
    __UpperCAmelCase						    =       scope

    # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
    # (we add 1 for the [CLS] token)
    __UpperCAmelCase						    =       (image_size // patch_size) ** 2
    __UpperCAmelCase						    =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Optional[int]:




    '''simple docstring'''




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

    __UpperCAmelCase						    =       None
    if self.use_labels:
      __UpperCAmelCase						    =       ids_tensor([self.batch_size]				,				self.type_sequence_label_size   )

    __UpperCAmelCase						    =       self.get_config()

    return config, pixel_values, labels






  def 	_UpperCAmelCase				(				self:			str   ) -> Tuple:




    '''simple docstring'''




    return ViTMAEConfig(
        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				,				decoder_hidden_size=self.hidden_size				,				decoder_num_hidden_layers=self.num_hidden_layers				,				decoder_num_attention_heads=self.num_attention_heads				,				decoder_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				,				mask_ratio=self.mask_ratio				,				)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Union[str, Any]				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			str   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       TFViTMAEModel(config=__lowerCAmelCase   )
    __UpperCAmelCase						    =       model(__lowerCAmelCase				,				training=__lowerCAmelCase   )
    self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			str   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       TFViTMAEForPreTraining(__lowerCAmelCase   )
    __UpperCAmelCase						    =       model(__lowerCAmelCase				,				training=__lowerCAmelCase   )
    # expected sequence length = num_patches
    __UpperCAmelCase						    =       (self.image_size // self.patch_size) ** 2
    __UpperCAmelCase						    =       self.patch_size**2 * self.num_channels
    self.parent.assertEqual(result.logits.shape				,				(self.batch_size, num_patches, expected_num_channels)   )

    # test greyscale images
    __UpperCAmelCase						    =       1
    __UpperCAmelCase						    =       TFViTMAEForPreTraining(__lowerCAmelCase   )

    __UpperCAmelCase						    =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
    __UpperCAmelCase						    =       model(__lowerCAmelCase				,				training=__lowerCAmelCase   )
    __UpperCAmelCase						    =       self.patch_size**2
    self.parent.assertEqual(result.logits.shape				,				(self.batch_size, num_patches, expected_num_channels)   )






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




    '''simple docstring'''




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






@require_tf
class 			UpperCAmelCase__    (		snake_case		,	snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[Any]    		= (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
  lowerCAmelCase__		:	Any    		= {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

  lowerCAmelCase__		:	Optional[int]    		= False
  lowerCAmelCase__		:	Any    		= False
  lowerCAmelCase__		:	Union[str, Any]    		= False
  lowerCAmelCase__		:	int    		= False






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       TFViTMAEModelTester(self   )
    __UpperCAmelCase						    =       ConfigTester(self				,				config_class=__lowerCAmelCase				,				has_text_modality=__lowerCAmelCase				,				hidden_size=37   )






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''simple docstring'''




    self.config_tester.run_common_tests()






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




    '''simple docstring'''




    pass






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




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __UpperCAmelCase						    =       model_class(__lowerCAmelCase   )
      self.assertIsInstance(model.get_input_embeddings()				,				(tf.keras.layers.Layer)   )
      __UpperCAmelCase						    =       model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(__lowerCAmelCase				,				tf.keras.layers.Layer   )   )






  def 	_UpperCAmelCase				(				self:			str   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

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

      __UpperCAmelCase						    =       ["pixel_values"]
      self.assertListEqual(arg_names[:1]				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Any   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			int   ) -> int:




    '''simple docstring'''




    np.random.seed(2   )

    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase						    =       int((config.image_size // config.patch_size) ** 2   )
    __UpperCAmelCase						    =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

    for model_class in self.all_model_classes:
      __UpperCAmelCase						    =       model_class(__lowerCAmelCase   )
      __UpperCAmelCase						    =       self._prepare_for_class(__lowerCAmelCase				,				__lowerCAmelCase   )

      __UpperCAmelCase						    =       model(__lowerCAmelCase				,				noise=__lowerCAmelCase   )

      __UpperCAmelCase						    =       copy.deepcopy(self._prepare_for_class(__lowerCAmelCase				,				__lowerCAmelCase   )   )
      __UpperCAmelCase						    =       model(**__lowerCAmelCase				,				noise=__lowerCAmelCase   )
      __UpperCAmelCase						    =       outputs_dict[0].numpy()
      __UpperCAmelCase						    =       outputs_keywords[0].numpy()

      self.assertLess(np.sum(np.abs(output_dict - output_keywords   )   )				,				1E-6   )






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




    '''simple docstring'''




    np.random.seed(2   )

    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase						    =       int((config.image_size // config.patch_size) ** 2   )
    __UpperCAmelCase						    =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

    def prepare_numpy_arrays(__lowerCAmelCase:			Optional[Any]   ):
      __UpperCAmelCase						    =       {}
      for k, v in inputs_dict.items():
        if tf.is_tensor(__lowerCAmelCase   ):
          __UpperCAmelCase						    =       v.numpy()
        else:
          __UpperCAmelCase						    =       np.array(__lowerCAmelCase   )

      return inputs_np_dict

    for model_class in self.all_model_classes:
      __UpperCAmelCase						    =       model_class(__lowerCAmelCase   )

      __UpperCAmelCase						    =       self._prepare_for_class(__lowerCAmelCase				,				__lowerCAmelCase   )
      __UpperCAmelCase						    =       prepare_numpy_arrays(__lowerCAmelCase   )

      __UpperCAmelCase						    =       model(__lowerCAmelCase				,				noise=__lowerCAmelCase   )
      __UpperCAmelCase						    =       model(**__lowerCAmelCase				,				noise=__lowerCAmelCase   )
      self.assert_outputs_same(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[int]				,				__lowerCAmelCase:			Union[str, Any]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			str   ) -> Tuple:




    '''simple docstring'''




    np.random.seed(2   )

    __UpperCAmelCase						    =       int((tf_model.config.image_size // tf_model.config.patch_size) ** 2   )
    __UpperCAmelCase						    =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
    __UpperCAmelCase						    =       tf.constant(__lowerCAmelCase   )

    # Add `noise` argument.
    # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
    __UpperCAmelCase						    =       tf_noise

    super().check_pt_tf_models(__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase   )






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




    '''simple docstring'''




    np.random.seed(2   )

    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase						    =       {
        module_member
        for model_class in self.all_model_classes
        for module in (import_module(model_class.__module__   ),)
        for module_member_name in dir(__lowerCAmelCase   )
        if module_member_name.endswith("MainLayer"   )
        # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
        and module_member_name[: -len("MainLayer"   )] == model_class.__name__[: -len("Model"   )]
        for module_member in (getattr(__lowerCAmelCase				,				__lowerCAmelCase   ),)
        if isinstance(__lowerCAmelCase				,				__lowerCAmelCase   )
        and tf.keras.layers.Layer in module_member.__bases__
        and getattr(__lowerCAmelCase				,				"_keras_serializable"				,				__lowerCAmelCase   )
    }

    __UpperCAmelCase						    =       int((config.image_size // config.patch_size) ** 2   )
    __UpperCAmelCase						    =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
    __UpperCAmelCase						    =       tf.convert_to_tensor(__lowerCAmelCase   )
    inputs_dict.update({"noise": noise}   )

    for main_layer_class in tf_main_layer_classes:
      __UpperCAmelCase						    =       main_layer_class(__lowerCAmelCase   )

      __UpperCAmelCase						    =       {
          name: tf.keras.Input(tensor.shape[1:]				,				dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
      }

      __UpperCAmelCase						    =       tf.keras.Model(__lowerCAmelCase				,				outputs=main_layer(__lowerCAmelCase   )   )
      __UpperCAmelCase						    =       model(__lowerCAmelCase   )

      with tempfile.TemporaryDirectory() as tmpdirname:
        __UpperCAmelCase						    =       os.path.join(__lowerCAmelCase				,				"keras_model.h5"   )
        model.save(__lowerCAmelCase   )
        __UpperCAmelCase						    =       tf.keras.models.load_model(
            __lowerCAmelCase				,				custom_objects={main_layer_class.__name__: main_layer_class}   )
        assert isinstance(__lowerCAmelCase				,				tf.keras.Model   )
        __UpperCAmelCase						    =       model(__lowerCAmelCase   )
        self.assert_outputs_same(__lowerCAmelCase				,				__lowerCAmelCase   )






  @slow
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Dict:




    '''simple docstring'''




    np.random.seed(2   )

    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase						    =       int((config.image_size // config.patch_size) ** 2   )
    __UpperCAmelCase						    =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

    for model_class in self.all_model_classes:
      __UpperCAmelCase						    =       model_class(__lowerCAmelCase   )
      __UpperCAmelCase						    =       self._prepare_for_class(__lowerCAmelCase				,				__lowerCAmelCase   )
      __UpperCAmelCase						    =       model(__lowerCAmelCase				,				noise=__lowerCAmelCase   )

      if model_class.__name__ == "TFViTMAEModel":
        __UpperCAmelCase						    =       outputs.last_hidden_state.numpy()
        __UpperCAmelCase						    =       0
      else:
        __UpperCAmelCase						    =       outputs.logits.numpy()
        __UpperCAmelCase						    =       0

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(__lowerCAmelCase				,				saved_model=__lowerCAmelCase   )
        __UpperCAmelCase						    =       model_class.from_pretrained(__lowerCAmelCase   )
        __UpperCAmelCase						    =       model(__lowerCAmelCase				,				noise=__lowerCAmelCase   )

        if model_class.__name__ == "TFViTMAEModel":
          __UpperCAmelCase						    =       after_outputs["last_hidden_state"].numpy()
          __UpperCAmelCase						    =       0
        else:
          __UpperCAmelCase						    =       after_outputs["logits"].numpy()
          __UpperCAmelCase						    =       0

        __UpperCAmelCase						    =       np.amax(np.abs(out_a - out_a   )   )
        self.assertLessEqual(__lowerCAmelCase				,				1E-5   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> str:




    '''simple docstring'''




    np.random.seed(2   )

    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase						    =       int((config.image_size // config.patch_size) ** 2   )
    __UpperCAmelCase						    =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

    for model_class in self.all_model_classes:
      __UpperCAmelCase						    =       model_class(__lowerCAmelCase   )
      __UpperCAmelCase						    =       self._prepare_for_class(__lowerCAmelCase				,				__lowerCAmelCase   )

      __UpperCAmelCase						    =       model(__lowerCAmelCase				,				noise=__lowerCAmelCase   )
      __UpperCAmelCase						    =       model.get_config()
      # make sure that returned config is jsonifiable, which is required by keras
      json.dumps(__lowerCAmelCase   )
      __UpperCAmelCase						    =       model_class.from_config(model.get_config()   )
      # make sure it also accepts a normal config
      __UpperCAmelCase						    =       model_class.from_config(model.config   )
      __UpperCAmelCase						    =       new_model(__lowerCAmelCase   )  # Build model
      new_model.set_weights(model.get_weights()   )
      __UpperCAmelCase						    =       new_model(__lowerCAmelCase				,				noise=__lowerCAmelCase   )

      self.assert_outputs_same(__lowerCAmelCase				,				__lowerCAmelCase   )






  @unittest.skip(
      reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."   )
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Tuple:




    '''simple docstring'''




    pass






  @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"   )
  def 	_UpperCAmelCase				(				self:			Any   ) -> List[Any]:




    '''simple docstring'''




    pass






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




    '''simple docstring'''




    __UpperCAmelCase						    =       TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"   )
    self.assertIsNotNone(__lowerCAmelCase   )






def       __lowerCAmelCase      (						)    ->  str:
  __UpperCAmelCase						    =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  return image





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



  """simple docstring"""






  @cached_property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[str]:




    '''simple docstring'''




    return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"   ) if is_vision_available() else None






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




    '''simple docstring'''




    np.random.seed(2   )

    __UpperCAmelCase						    =       TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"   )

    __UpperCAmelCase						    =       self.default_image_processor
    __UpperCAmelCase						    =       prepare_img()
    __UpperCAmelCase						    =       image_processor(images=__lowerCAmelCase				,				return_tensors="tf"   )

    # prepare a noise vector that will be also used for testing the TF model
    # (this way we can ensure that the PT and TF models operate on the same inputs)
    __UpperCAmelCase						    =       ViTMAEConfig()
    __UpperCAmelCase						    =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
    __UpperCAmelCase						    =       np.random.uniform(size=(1, num_patches)   )

    # forward pass
    __UpperCAmelCase						    =       model(**__lowerCAmelCase				,				noise=__lowerCAmelCase   )

    # verify the logits
    __UpperCAmelCase						    =       tf.convert_to_tensor([1, 196, 768]   )
    self.assertEqual(outputs.logits.shape				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       tf.convert_to_tensor(
        [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]   )

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





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def       __lowerCAmelCase      (						A_   : int						,	A_   : Optional[Any]						,	A_   : Tuple					)    ->  List[str]:
  return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : Dict						,	A_   : Dict						,	A_   : Optional[int]="attention"					)    ->  Optional[int]:
  __UpperCAmelCase						    =       __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       k_tmp.reshape(k_tmp.shape[0]						,	k_tmp.shape[1] * k_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,	o_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       q_tmp.reshape(q_tmp.shape[0]						,	q_tmp.shape[1] * q_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       v_tmp.reshape(v_tmp.shape[0]						,	v_tmp.shape[1] * v_tmp.shape[2]					)
  return k, o, q, v




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Union[str, Any]						,	A_   : Optional[Any]						,	A_   : int=False					)    ->  List[Any]:
  if split_mlp_wi:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    __UpperCAmelCase						    =       (wi_a, wi_a)
  else:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo




def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : int						,	A_   : Optional[Any]						,	A_   : Optional[Any]					)    ->  Tuple:
  return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def       __lowerCAmelCase      (						A_   : dict						,	*, A_   : int						,	A_   : bool						,	A_   : bool = False					)    ->  List[str]:
  __UpperCAmelCase						    =       traverse_util.flatten_dict(variables["target"]					)
  __UpperCAmelCase						    =       {"/".join(A_					): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  __UpperCAmelCase						    =       "encoder/encoder/mlp/wi_0/kernel" in old
  print("Split MLP:"						,	A_					)

  __UpperCAmelCase						    =       collections.OrderedDict()

  # Shared embeddings.
  __UpperCAmelCase						    =       old["token_embedder/embedding"]

  # Encoder.
  for i in range(A_					):
    # Block i, layer 0 (Self Attention).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_attention_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"encoder"						,	"attention"					)
    __UpperCAmelCase						    =       layer_norm
    __UpperCAmelCase						    =       k.T
    __UpperCAmelCase						    =       o.T
    __UpperCAmelCase						    =       q.T
    __UpperCAmelCase						    =       v.T

    # Block i, layer 1 (MLP).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_mlp_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"encoder"						,	A_					)
    __UpperCAmelCase						    =       layer_norm
    if split_mlp_wi:
      __UpperCAmelCase						    =       wi[0].T
      __UpperCAmelCase						    =       wi[1].T
    else:
      __UpperCAmelCase						    =       wi.T
    __UpperCAmelCase						    =       wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      __UpperCAmelCase						    =       tax_relpos_bias_lookup(
          A_						,	A_						,	"encoder"					).T

  __UpperCAmelCase						    =       old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"encoder"					).T
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"decoder"					).T

  if not is_encoder_only:
    # Decoder.
    for i in range(A_					):
      # Block i, layer 0 (Self Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_self_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"self_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 1 (Cross Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_cross_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"encoder_decoder_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 2 (MLP).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_mlp_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"decoder"						,	A_					)
      __UpperCAmelCase						    =       layer_norm
      if split_mlp_wi:
        __UpperCAmelCase						    =       wi[0].T
        __UpperCAmelCase						    =       wi[1].T
      else:
        __UpperCAmelCase						    =       wi.T
      __UpperCAmelCase						    =       wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        __UpperCAmelCase						    =       tax_relpos_bias_lookup(A_						,	A_						,	"decoder"					).T

    __UpperCAmelCase						    =       old["decoder/decoder_norm/scale"]

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      __UpperCAmelCase						    =       old["decoder/logits_dense/kernel"].T

  return new




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : bool					)    ->  List[Any]:
  __UpperCAmelCase						    =       collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    __UpperCAmelCase						    =       state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __UpperCAmelCase						    =       state_dict["shared.weight"]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("Using shared word embeddings as lm_head."					)
      __UpperCAmelCase						    =       state_dict["shared.weight"]

  return state_dict




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : int						,	A_   : List[Any]						,	A_   : List[Any]						,	A_   : List[Any]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       checkpoints.load_tax_checkpoint(A_					)
  __UpperCAmelCase						    =       convert_tax_to_pytorch(
      A_						,	num_layers=config.num_layers						,	is_encoder_only=A_						,	scalable_attention=A_					)
  __UpperCAmelCase						    =       make_state_dict(A_						,	A_					)
  model.load_state_dict(A_						,	strict=A_					)




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : List[Any]						,	A_   : List[str]						,	A_   : bool = False						,	A_   : bool = False						,	)    ->  Optional[int]:
  __UpperCAmelCase						    =       MTaConfig.from_json_file(A_					)
  print(F'''Building PyTorch model from configuration: {config}'''					)
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    __UpperCAmelCase						    =       UMTaEncoderModel(A_					)
  else:
    __UpperCAmelCase						    =       UMTaForConditionalGeneration(A_					)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(A_						,	A_						,	A_						,	A_						,	A_					)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
  model.save_pretrained(A_					)

  # Verify that we can load the checkpoint.
  model.from_pretrained(A_					)
  print("Done"					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       parser.add_argument(
           """--scalable_attention""",
           action="""store_true""",
           help="""Whether the model uses scaled attention (umt5 model)""",
           default=False,
       )
       a_							=      parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )



