

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor





@require_vision
class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =tempfile.mkdtemp()

             __A    =[
                 '''[UNK]''',
                 '''[CLS]''',
                 '''[SEP]''',
                 '''[PAD]''',
                 '''[MASK]''',
                 '''want''',
                 '''##want''',
                 '''##ed''',
                 '''wa''',
                 '''un''',
                 '''runn''',
                 '''##ing''',
                 ''',''',
                 '''low''',
                 '''lowest''',
             ]
             __A    =os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''vocab_file''']     )
             with open(self.vocab_file ,   '''w''' ,   encoding='''utf-8'''     ) as vocab_writer:
                   vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]     )     )

             __A    ={
                 '''do_resize''': True,
                 '''size''': 2_0,
                 '''do_center_crop''': True,
                 '''crop_size''': 1_8,
                 '''do_normalize''': True,
                 '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
                 '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
             }
             __A    =os.path.join(self.tmpdirname ,   lowercase__     )
             with open(self.image_processor_file ,   '''w''' ,   encoding='''utf-8'''     ) as fp:
                   json.dump(lowercase__ ,   lowercase__     )

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             return BertTokenizer.from_pretrained(self.tmpdirname ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             return BertTokenizerFast.from_pretrained(self.tmpdirname ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,   **lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             shutil.rmtree(self.tmpdirname     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =[np.random.randint(2_5_5 ,   size=(3, 3_0, 4_0_0) ,   dtype=np.uinta     )]
             __A    =[Image.fromarray(np.moveaxis(lowercase__ ,   0 ,   -1     )     ) for x in image_inputs]
             return image_inputs

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_tokenizer()
             __A    =self.get_rust_tokenizer()
             __A    =self.get_image_processor()

             __A    =AlignProcessor(tokenizer=lowercase__ ,   image_processor=lowercase__     )
             processor_slow.save_pretrained(self.tmpdirname     )
             __A    =AlignProcessor.from_pretrained(self.tmpdirname ,   use_fast=lowercase__     )

             __A    =AlignProcessor(tokenizer=lowercase__ ,   image_processor=lowercase__     )
             processor_fast.save_pretrained(self.tmpdirname     )
             __A    =AlignProcessor.from_pretrained(self.tmpdirname     )

             self.assertEqual(processor_slow.tokenizer.get_vocab() ,   tokenizer_slow.get_vocab()     )
             self.assertEqual(processor_fast.tokenizer.get_vocab() ,   tokenizer_fast.get_vocab()     )
             self.assertEqual(tokenizer_slow.get_vocab() ,   tokenizer_fast.get_vocab()     )
             self.assertIsInstance(processor_slow.tokenizer ,   lowercase__     )
             self.assertIsInstance(processor_fast.tokenizer ,   lowercase__     )

             self.assertEqual(processor_slow.image_processor.to_json_string() ,   image_processor.to_json_string()     )
             self.assertEqual(processor_fast.image_processor.to_json_string() ,   image_processor.to_json_string()     )
             self.assertIsInstance(processor_slow.image_processor ,   lowercase__     )
             self.assertIsInstance(processor_fast.image_processor ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =AlignProcessor(tokenizer=self.get_tokenizer() ,   image_processor=self.get_image_processor()     )
             processor.save_pretrained(self.tmpdirname     )

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

             __A    =AlignProcessor.from_pretrained(
                 self.tmpdirname ,   bos_token='''(BOS)''' ,   eos_token='''(EOS)''' ,   do_normalize=lowercase__ ,   padding_value=1.0     )

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

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_image_processor()
             __A    =self.get_tokenizer()

             __A    =AlignProcessor(tokenizer=lowercase__ ,   image_processor=lowercase__     )

             __A    =self.prepare_image_inputs()

             __A    =image_processor(lowercase__ ,   return_tensors='''np'''     )
             __A    =processor(images=lowercase__ ,   return_tensors='''np'''     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_image_processor()
             __A    =self.get_tokenizer()

             __A    =AlignProcessor(tokenizer=lowercase__ ,   image_processor=lowercase__     )

             __A    ='''lower newer'''

             __A    =processor(text=lowercase__     )

             __A    =tokenizer(lowercase__ ,   padding='''max_length''' ,   max_length=6_4     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_image_processor()
             __A    =self.get_tokenizer()

             __A    =AlignProcessor(tokenizer=lowercase__ ,   image_processor=lowercase__     )

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

             __A    =processor(text=lowercase__ ,   images=lowercase__     )

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

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_image_processor()
             __A    =self.get_tokenizer()

             __A    =AlignProcessor(tokenizer=lowercase__ ,   image_processor=lowercase__     )

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

             __A    =processor.batch_decode(lowercase__     )
             __A    =tokenizer.batch_decode(lowercase__     )

             self.assertListEqual(lowercase__ ,   lowercase__     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_image_processor()
             __A    =self.get_tokenizer()

             __A    =AlignProcessor(tokenizer=lowercase__ ,   image_processor=lowercase__     )

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

             __A    =processor(text=lowercase__ ,   images=lowercase__     )

             self.assertListEqual(list(inputs.keys()     ) ,   processor.model_input_names     )





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

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





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.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
      __A    ='''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''     ,				__A			)

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

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

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =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:
            __A    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =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.'''			)
                  __A    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.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:
            __A    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__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__":
   _lowerCamelCase      :					List[str]					    =      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
   )
   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


