

'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			List[Any]:
   _lowercase    :   str      =     TaConfig.from_pretrained(snake_case  )
   _lowercase    :   List[str]      =     FlaxTaForConditionalGeneration(config=snake_case  )
   _lowercase    :   str      =     checkpoints.load_tax_checkpoint(snake_case  )

   _lowercase    :   str      =     "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

   # Encoder
   for layer_index in range(config.num_layers  ):
      _lowercase    :   Union[str, Any]      =     F'''layers_{str(snake_case  )}'''

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

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

      if split_mlp_wi:
         _lowercase    :   Optional[Any]      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         _lowercase    :   int      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         _lowercase    :   List[Any]      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

      _lowercase    :   Union[str, Any]      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      _lowercase    :   int      =     tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      _lowercase    :   List[str]      =     tax_attention_key
      _lowercase    :   str      =     tax_attention_out
      _lowercase    :   Tuple      =     tax_attention_query
      _lowercase    :   Union[str, Any]      =     tax_attention_value

      _lowercase    :   Any      =     tax_attention_layer_norm

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

      _lowercase    :   str      =     tax_mlp_wo
      _lowercase    :   Any      =     tax_mlp_layer_norm

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

   # Assigning
   _lowercase    :   Optional[int]      =     tax_model["target"]["encoder"]["encoder_norm"]["scale"]
   _lowercase    :   List[str]      =     tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_decoder_layers  ):
      _lowercase    :   str      =     F'''layers_{str(snake_case  )}'''

      # Self-Attention
      _lowercase    :   Optional[int]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      _lowercase    :   Union[str, Any]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      _lowercase    :   List[Any]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      _lowercase    :   Union[str, Any]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

      # Layer Normalization
      _lowercase    :   List[str]      =     tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]

      # Encoder-Decoder-Attention
      _lowercase    :   Tuple      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
          "kernel"
      ]
      _lowercase    :   Tuple      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
          "kernel"
      ]
      _lowercase    :   List[Any]      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
          "kernel"
      ]
      _lowercase    :   List[str]      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
          "kernel"
      ]

      # Layer Normalization
      _lowercase    :   Optional[Any]      =     tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

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

      # Layer Normalization
      _lowercase    :   Optional[Any]      =     tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      _lowercase    :   List[Any]      =     tax_attention_key
      _lowercase    :   int      =     tax_attention_out
      _lowercase    :   Tuple      =     tax_attention_query
      _lowercase    :   Tuple      =     tax_attention_value

      _lowercase    :   Tuple      =     tax_pre_attention_layer_norm

      _lowercase    :   Optional[int]      =     tax_enc_dec_attention_key
      _lowercase    :   Tuple      =     tax_enc_dec_attention_out
      _lowercase    :   str      =     tax_enc_dec_attention_query
      _lowercase    :   List[str]      =     tax_enc_dec_attention_value

      _lowercase    :   str      =     tax_cross_layer_norm

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

      _lowercase    :   Optional[int]      =     tax_mlp_wo

      _lowercase    :   Optional[int]      =     txa_mlp_layer_norm

   # Decoder Normalization
   _lowercase    :   Any      =     tax_model["target"]["decoder"]["decoder_norm"]["scale"]
   _lowercase    :   List[str]      =     txa_decoder_norm

   # Only for layer 0:
   _lowercase    :   str      =     tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
   _lowercase    :   Optional[Any]      =     tax_decoder_rel_embedding

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

   # LM Head (only in v1.1 checkpoints)
   if "logits_dense" in tax_model["target"]["decoder"]:
      _lowercase    :   Union[str, Any]      =     tax_model["target"]["decoder"]["logits_dense"]["kernel"]

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


if __name__ == "__main__":
    _snake_case     						=						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.'
    )
    _snake_case     						=						parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









'''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 TvltImageProcessor







def       _A	(			snake_case	,							snake_case=10	,							snake_case=10	,							snake_case=False	,							snake_case=False  )    ->			List[Any]:
   _lowercase    :   Dict      =     []
   for i in range(image_processor_tester.num_frames  ):
      video.append(np.random.randint(2_55	,							size=(image_processor_tester.num_channels, width, height)	,							dtype=np.uinta  )  )

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

   if torchify:
      _lowercase    :   Any      =     [torch.from_numpy(snake_case  ) for frame in video]

   return video







def       _A	(			snake_case	,							snake_case=False	,							snake_case=False	,							snake_case=False  )    ->			Tuple:
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   _lowercase    :   Optional[Any]      =     []
   for i in range(image_processor_tester.batch_size  ):
      if equal_resolution:
         _lowercase    :   List[Any]      =     image_processor_tester.max_resolution
      else:
         _lowercase  ,   _lowercase    :   Dict      =     np.random.choice(
             np.arange(image_processor_tester.min_resolution	,							image_processor_tester.max_resolution  )	,							2  )
         _lowercase    :   List[Any]      =     prepare_video(
             image_processor_tester=snake_case	,							width=snake_case	,							height=snake_case	,							numpify=snake_case	,							torchify=snake_case	,							)
      video_inputs.append(snake_case  )

   return video_inputs


class      a__						(							unittest.TestCase						):


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=7    ,       _UpperCamelCase=3    ,       _UpperCamelCase=4    ,       _UpperCamelCase=18    ,       _UpperCamelCase=30    ,       _UpperCamelCase=400    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       _UpperCamelCase=True    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     size if size is not None else {"shortest_edge": 18}
      _lowercase    :   Any      =     crop_size if crop_size is not None else {"height": 18, "width": 18}

      _lowercase    :   Dict      =     parent
      _lowercase    :   Optional[Any]      =     batch_size
      _lowercase    :   List[Any]      =     num_channels
      _lowercase    :   Optional[Any]      =     num_frames
      _lowercase    :   int      =     image_size
      _lowercase    :   Optional[int]      =     min_resolution
      _lowercase    :   Optional[int]      =     max_resolution
      _lowercase    :   Optional[Any]      =     do_resize
      _lowercase    :   Dict      =     size
      _lowercase    :   Any      =     do_normalize
      _lowercase    :   Tuple      =     image_mean
      _lowercase    :   Union[str, Any]      =     image_std
      _lowercase    :   Tuple      =     do_center_crop
      _lowercase    :   str      =     crop_size






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							TvltImageProcessor if is_vision_available() else None


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     TvltImageProcessorTester(self						)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_mean"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_std"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_normalize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_resize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_center_crop"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "size"						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Any      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PIL videos
      _lowercase    :   Any      =     prepare_video_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase						)
      for video in video_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       _UpperCamelCase						)
         self.assertIsInstance(video[0]    ,       Image.Image						)

      # Test not batched input
      _lowercase    :   Any      =     image_processor(video_inputs[0]    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   Union[str, Any]      =     image_processor(_UpperCamelCase    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _lowercase    :   List[str]      =     prepare_video_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       numpify=_UpperCamelCase						)
      for video in video_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       _UpperCamelCase						)
         self.assertIsInstance(video[0]    ,       np.ndarray						)

      # Test not batched input
      _lowercase    :   Optional[int]      =     image_processor(video_inputs[0]    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   List[Any]      =     image_processor(_UpperCamelCase    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Dict      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   Optional[int]      =     prepare_video_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
      for video in video_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       _UpperCamelCase						)
         self.assertIsInstance(video[0]    ,       torch.Tensor						)

      # Test not batched input
      _lowercase    :   int      =     image_processor(video_inputs[0]    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   List[Any]      =     image_processor(_UpperCamelCase    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )






