






'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
  from transformers import TvltImageProcessor

if is_speech_available():
  from transformers import TvltFeatureExtractor

from transformers import TvltProcessor






@require_torch
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													='''ZinengTang/tvlt-base'''
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=tempfile.mkdtemp()




 def        __magic_name__		( self		:							List[str]   , **__lowercase		:							Dict )		->  str:
       return TvltImageProcessor.from_pretrained(self.checkpoint   , **__lowercase )




 def        __magic_name__		( self		:							Optional[int]   , **__lowercase		:							int )		->  int:
       return TvltFeatureExtractor.from_pretrained(self.checkpoint   , **__lowercase )




 def        __magic_name__		( self		:							Dict )		->  Optional[Any]:
       shutil.rmtree(self.tmpdirname )




 def        __magic_name__		( self		:							List[str] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.get_image_processor()
       SCREAMING_SNAKE_CASE__       :			int													=self.get_feature_extractor()

       SCREAMING_SNAKE_CASE__       :			Dict													=TvltProcessor(image_processor=__lowercase   , feature_extractor=__lowercase )
       processor.save_pretrained(self.tmpdirname )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=TvltProcessor.from_pretrained(self.tmpdirname )

       self.assertIsInstance(processor.feature_extractor   , __lowercase )
       self.assertIsInstance(processor.image_processor   , __lowercase )




 def        __magic_name__		( self		:							int )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=self.get_image_processor()
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.get_feature_extractor()

       SCREAMING_SNAKE_CASE__       :			List[str]													=TvltProcessor(image_processor=__lowercase   , feature_extractor=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=np.ones([1_20_00] )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=feature_extractor(__lowercase   , return_tensors='''np''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													=processor(audio=__lowercase   , return_tensors='''np''' )

       for key in audio_dict.keys():
             self.assertAlmostEqual(audio_dict[key].sum()   , input_processor[key].sum()   , delta=1e-2 )




 def        __magic_name__		( self		:							Any )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			str													=self.get_image_processor()
       SCREAMING_SNAKE_CASE__       :			Any													=self.get_feature_extractor()

       SCREAMING_SNAKE_CASE__       :			str													=TvltProcessor(image_processor=__lowercase   , feature_extractor=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Dict													=np.ones([3, 2_24, 2_24] )

       SCREAMING_SNAKE_CASE__       :			Dict													=image_processor(__lowercase   , return_tensors='''np''' )
       SCREAMING_SNAKE_CASE__       :			Any													=processor(images=__lowercase   , return_tensors='''np''' )

       for key in image_dict.keys():
             self.assertAlmostEqual(image_dict[key].sum()   , input_processor[key].sum()   , delta=1e-2 )




 def        __magic_name__		( self		:							Union[str, Any] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.get_image_processor()
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.get_feature_extractor()

       SCREAMING_SNAKE_CASE__       :			Dict													=TvltProcessor(image_processor=__lowercase   , feature_extractor=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Tuple													=np.ones([1_20_00] )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=np.ones([3, 2_24, 2_24] )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=processor(audio=__lowercase   , images=__lowercase )

       self.assertListEqual(list(inputs.keys() )   , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )

       # test if it raises when no input is passed
       with pytest.raises(__lowercase ):
             processor()




 def        __magic_name__		( self		:							int )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=self.get_image_processor()
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.get_feature_extractor()

       SCREAMING_SNAKE_CASE__       :			Any													=TvltProcessor(image_processor=__lowercase   , feature_extractor=__lowercase )

       self.assertListEqual(
           processor.model_input_names   , image_processor.model_input_names + feature_extractor.model_input_names   , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''   , )







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''TransientGlobalSelfAttention'''
      else:
            raise ValueError(
                '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                ''' attribute with a value from [\'local\', \'transient-global].'''							)

      # Encoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

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

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			Any													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

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

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_encoder_global_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

            # Encoder-Decoder-Attention
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_enc_dec_attention_module['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Any													=tax_enc_dec_attention_module['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_enc_dec_attention_module['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_module['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

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

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

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

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

      # Decoder Normalization
      SCREAMING_SNAKE_CASE__       :			Any													=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=txa_decoder_norm

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

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

      # LM Head (only in v1.1 and LongT5 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
  )
  parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
  parser.add_argument(
      '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  )
  a_					=	parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)