




'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast





@require_vision
class       lowerCAmelCase				(	unittest.TestCase):


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



            '''simple docstring'''




            __snake_case         =					tempfile.mkdtemp()

            __snake_case         =					CLIPImageProcessor()
            __snake_case         =					BertTokenizer.from_pretrained(
                '''hf-internal-testing/tiny-random-BertModel'''      ,					model_input_names=['''input_ids''', '''attention_mask''']					)

            __snake_case         =					GitProcessor(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)

            processor.save_pretrained(self.tmpdirname					)


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



            '''simple docstring'''




            return AutoProcessor.from_pretrained(self.tmpdirname      ,					**__SCREAMING_SNAKE_CASE					).tokenizer


       def  lowerCAmelCase (				self      ,					**__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




            return AutoProcessor.from_pretrained(self.tmpdirname      ,					**__SCREAMING_SNAKE_CASE					).image_processor


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



            '''simple docstring'''




            shutil.rmtree(self.tmpdirname					)


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



            '''simple docstring'''




            __snake_case         =					[np.random.randint(255      ,					size=(3, 30, 400)      ,					dtype=np.uinta					)]

            __snake_case         =					[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE      ,					0      ,					-1					)					) for x in image_inputs]

            return image_inputs


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



            '''simple docstring'''




            __snake_case         =					GitProcessor(tokenizer=self.get_tokenizer()      ,					image_processor=self.get_image_processor()					)
            processor.save_pretrained(self.tmpdirname					)

            __snake_case         =					self.get_tokenizer(bos_token='''(BOS)'''      ,					eos_token='''(EOS)'''					)
            __snake_case         =					self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE      ,					padding_value=1.0					)

            __snake_case         =					GitProcessor.from_pretrained(
                self.tmpdirname      ,					bos_token='''(BOS)'''      ,					eos_token='''(EOS)'''      ,					do_normalize=__SCREAMING_SNAKE_CASE      ,					padding_value=1.0					)

            self.assertEqual(processor.tokenizer.get_vocab()      ,					tokenizer_add_kwargs.get_vocab()					)
            self.assertIsInstance(processor.tokenizer      ,					__SCREAMING_SNAKE_CASE					)

            self.assertEqual(processor.image_processor.to_json_string()      ,					image_processor_add_kwargs.to_json_string()					)
            self.assertIsInstance(processor.image_processor      ,					__SCREAMING_SNAKE_CASE					)


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



            '''simple docstring'''




            __snake_case         =					self.get_image_processor()
            __snake_case         =					self.get_tokenizer()

            __snake_case         =					GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,					image_processor=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					self.prepare_image_inputs()

            __snake_case         =					image_processor(__SCREAMING_SNAKE_CASE      ,					return_tensors='''np'''					)
            __snake_case         =					processor(images=__SCREAMING_SNAKE_CASE      ,					return_tensors='''np'''					)

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


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case         =					self.get_image_processor()
            __snake_case         =					self.get_tokenizer()

            __snake_case         =					GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,					image_processor=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					'''lower newer'''

            __snake_case         =					processor(text=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					tokenizer(__SCREAMING_SNAKE_CASE      ,					return_token_type_ids=__SCREAMING_SNAKE_CASE					)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]      ,					encoded_processor[key]					)


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



            '''simple docstring'''




            __snake_case         =					self.get_image_processor()
            __snake_case         =					self.get_tokenizer()

            __snake_case         =					GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,					image_processor=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					'''lower newer'''
            __snake_case         =					self.prepare_image_inputs()

            __snake_case         =					processor(text=__SCREAMING_SNAKE_CASE      ,					images=__SCREAMING_SNAKE_CASE					)

            self.assertListEqual(list(inputs.keys()					)      ,					['''input_ids''', '''attention_mask''', '''pixel_values''']					)

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


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case         =					self.get_image_processor()
            __snake_case         =					self.get_tokenizer()

            __snake_case         =					GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,					image_processor=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __snake_case         =					processor.batch_decode(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE					)

            self.assertListEqual(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					self.get_image_processor()
            __snake_case         =					self.get_tokenizer()

            __snake_case         =					GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,					image_processor=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					'''lower newer'''
            __snake_case         =					self.prepare_image_inputs()

            __snake_case         =					processor(text=__SCREAMING_SNAKE_CASE      ,					images=__SCREAMING_SNAKE_CASE					)

            # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
            self.assertListEqual(list(inputs.keys()					)      ,					['''input_ids''', '''attention_mask''', '''pixel_values''']					)












'''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)






