"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BlipImageProcessor




class   __snake_case			(	unittest.TestCase							):



      def __init__(		self       ,     lowercase       ,     lowercase=7       ,     lowercase=3       ,     lowercase=18       ,     lowercase=30       ,     lowercase=4_00       ,     lowercase=True       ,     lowercase=None       ,     lowercase=True       ,     lowercase=False       ,     lowercase=[0.48145466, 0.4578275, 0.40821073]       ,     lowercase=[0.26862954, 0.26130258, 0.27577711]       ,     lowercase=True       ,     )      ->     Optional[int]:





        '''simple docstring'''





        a__:	Dict     				=      size if size is not None else {'height': 20, 'width': 20}
        a__:	Dict     				=      parent
        a__:	Union[str, Any]     				=      batch_size
        a__:	Dict     				=      num_channels
        a__:	List[Any]     				=      image_size
        a__:	Optional[int]     				=      min_resolution
        a__:	List[str]     				=      max_resolution
        a__:	Optional[Any]     				=      do_resize
        a__:	Optional[Any]     				=      size
        a__:	Any     				=      do_normalize
        a__:	Union[str, Any]     				=      image_mean
        a__:	Dict     				=      image_std
        a__:	int     				=      do_pad
        a__:	int     				=      do_convert_rgb



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





        '''simple docstring'''





        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }



      def     lowerCamelCase_  (		self       ,     lowercase=False       ,     lowercase=False       ,     lowercase=False)      ->     str:





        '''simple docstring'''





        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          a__:	List[str]     				=      []
          for i in range(self.batch_size):
            image_inputs.append(
                np.random.randint(
                    2_55       ,     size=(self.num_channels, self.max_resolution, self.max_resolution)       ,     dtype=np.uinta))
        else:
          a__:	List[str]     				=      []
          for i in range(self.batch_size):
            a__				,       a__:	Tuple     				=      np.random.choice(np.arange(self.min_resolution       ,     self.max_resolution)       ,     2)
            image_inputs.append(np.random.randint(2_55       ,     size=(self.num_channels, width, height)       ,     dtype=np.uinta))

        if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          a__:	Union[str, Any]     				=      [Image.fromarray(np.moveaxis(lowercase       ,     0       ,     -1)) for x in image_inputs]

        if torchify:
          a__:	int     				=      [torch.from_numpy(lowercase) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						BlipImageProcessor if is_vision_available() else None



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





        '''simple docstring'''





        a__:	int     				=      BlipImageProcessingTester(self)



      @property
      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase       ,     'do_resize'))
        self.assertTrue(hasattr(lowercase       ,     'size'))
        self.assertTrue(hasattr(lowercase       ,     'do_normalize'))
        self.assertTrue(hasattr(lowercase       ,     'image_mean'))
        self.assertTrue(hasattr(lowercase       ,     'image_std'))
        self.assertTrue(hasattr(lowercase       ,     'do_convert_rgb'))



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        a__:	Dict     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:	Any     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     Image.Image)

        # Test not batched input
        a__:	Tuple     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Tuple     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	int     				=      self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        a__:	Optional[Any]     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase       ,     numpify=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     np.ndarray)

        # Test not batched input
        a__:	Dict     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	int     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	int     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        a__:	List[str]     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase       ,     torchify=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     torch.Tensor)

        # Test not batched input
        a__:	Optional[Any]     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Tuple     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )







@require_torch
@require_vision
class   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						BlipImageProcessor if is_vision_available() else None



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





        '''simple docstring'''





        a__:	Optional[Any]     				=      BlipImageProcessingTester(self       ,     num_channels=4)
        a__:	Union[str, Any]     				=      3



      @property
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	Dict     				=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase       ,     'do_resize'))
        self.assertTrue(hasattr(lowercase       ,     'size'))
        self.assertTrue(hasattr(lowercase       ,     'do_normalize'))
        self.assertTrue(hasattr(lowercase       ,     'image_mean'))
        self.assertTrue(hasattr(lowercase       ,     'image_std'))
        self.assertTrue(hasattr(lowercase       ,     'do_convert_rgb'))



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        a__:	Optional[int]     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:	Dict     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     Image.Image)

        # Test not batched input
        a__:	Union[str, Any]     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Dict     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )



"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

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

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

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

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

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

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

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

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


if __name__ == "__main__":
 lowerCAmelCase_:       List[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_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




