
"""simple docstring"""




import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM


@require_torch
class       __magic_name__							:
       def __init__(  self			,					A_			,					A_=99			,					A_=13			,					A_=16			,					A_=7			,					A_=True			,					A_=True			,					A_=True			,					A_=False			,					A_=True			,					A_=2			,					A_=32			,					A_=4			,					A_=4			,					A_=30			,					A_=0			,					A_=1			,					A_=2			,					A_=None			,					)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      List[Any]   			=  batch_size
         _lowercase:      Optional[Any]   			=  decoder_seq_length
         # For common tests
         _lowercase:      List[str]   			=  self.decoder_seq_length
         _lowercase:      Union[str, Any]   			=  is_training
         _lowercase:      List[str]   			=  use_attention_mask
         _lowercase:      Tuple   			=  use_labels

         _lowercase:      Any   			=  vocab_size
         _lowercase:      Tuple   			=  d_model
         _lowercase:      str   			=  d_model
         _lowercase:      Optional[Any]   			=  decoder_layers
         _lowercase:      List[str]   			=  decoder_layers
         _lowercase:      int   			=  decoder_ffn_dim
         _lowercase:      Optional[int]   			=  decoder_attention_heads
         _lowercase:      Union[str, Any]   			=  decoder_attention_heads
         _lowercase:      List[str]   			=  eos_token_id
         _lowercase:      Optional[int]   			=  bos_token_id
         _lowercase:      Any   			=  pad_token_id
         _lowercase:      str   			=  decoder_start_token_id
         _lowercase:      str   			=  use_cache
         _lowercase:      Any   			=  max_position_embeddings

         _lowercase:      List[Any]   			=  None
         _lowercase:      Tuple   			=  decoder_seq_length
         _lowercase:      str   			=  2
         _lowercase:      List[Any]   			=  1
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      List[str]   			=  ids_tensor([self.batch_size, self.decoder_seq_length]			,					self.vocab_size							)

         _lowercase:      Any   			=  None
         if self.use_attention_mask:
           _lowercase:      Optional[Any]   			=  ids_tensor([self.batch_size, self.decoder_seq_length]			,					vocab_size=2							)

         _lowercase:      Any   			=  None
         if self.use_labels:
           _lowercase:      List[Any]   			=  ids_tensor([self.batch_size, self.decoder_seq_length]			,					self.vocab_size							)

         _lowercase:      Union[str, Any]   			=  TrOCRConfig(
             vocab_size=self.vocab_size			,					d_model=self.d_model			,					decoder_layers=self.decoder_layers			,					decoder_ffn_dim=self.decoder_ffn_dim			,					decoder_attention_heads=self.decoder_attention_heads			,					eos_token_id=self.eos_token_id			,					bos_token_id=self.bos_token_id			,					use_cache=self.use_cache			,					pad_token_id=self.pad_token_id			,					decoder_start_token_id=self.decoder_start_token_id			,					max_position_embeddings=self.max_position_embeddings			,					)

         return (config, input_ids, attention_mask, lm_labels)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_			,					)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Tuple   			=  True
         _lowercase:      int   			=  TrOCRDecoder(config=__a							).to(__a							).eval()
         _lowercase:      Union[str, Any]   			=  input_ids[:2]

         input_ids[input_ids == 0] += 1
         # first forward pass
         _lowercase:      Union[str, Any]   			=  model(__a			,					use_cache=__a							)
         _lowercase:      Any   			=  model(__a							)
         _lowercase:      Dict   			=  model(__a			,					use_cache=__a							)

         self.parent.assertTrue(len(__a							) == len(__a							)							)
         self.parent.assertTrue(len(__a							) == len(__a							) + 1							)

         _lowercase:      str   			=  outputs["""past_key_values"""]

         # create hypothetical next token and extent to next_input_ids
         _lowercase:      Optional[int]   			=  ids_tensor((2, 1)			,					config.vocab_size - 1							) + 1

         # append to next input_ids and
         _lowercase:      Tuple   			=  torch.cat([input_ids, next_tokens]			,					dim=-1							)

         _lowercase:      Any   			=  model(__a							)["""last_hidden_state"""]
         _lowercase:      Any   			=  model(__a			,					past_key_values=__a							)["""last_hidden_state"""]

         # select random slice
         _lowercase:      List[str]   			=  ids_tensor((1,)			,					output_from_past.shape[-1]							).item()
         _lowercase:      Dict   			=  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
         _lowercase:      Dict   			=  output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         assert torch.allclose(__a			,					__a			,					atol=1E-3							)



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





         """simple docstring"""




         _lowercase:      Tuple   			=  self.prepare_config_and_inputs()
         _lowercase:      List[Any]   			=  config_and_inputs

         _lowercase:      Optional[int]   			=  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
         return config, inputs_dict


@require_torch
class       __magic_name__							(							a__ ,       a__ ,       a__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
       UpperCamelCase_ =		(TrOCRForCausalLM,) if is_torch_available() else ()
       UpperCamelCase_ =		{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
       UpperCamelCase_ =		True
       UpperCamelCase_ =		False
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  TrOCRStandaloneDecoderModelTester(self			,					is_training=__a							)
         _lowercase:      Tuple   			=  ConfigTester(self			,					config_class=__a							)
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




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





         """simple docstring"""




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





         """simple docstring"""




         self.config_tester.run_common_tests()
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past(*__a							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         return



       @unittest.skip('''The model doesn\'t support left padding'''							)  # and it's not used enough to be worth fixing :)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         pass




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(
           metadata={"help": "The output directory where the model will be written."}						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The encoder model checkpoint for weights initialization."
                   "Don't set if you want to train an encoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The decoder model checkpoint for weights initialization."
                   "Don't set if you want to train a decoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}						)
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}						)







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.encoder_config_name					)
   # Use pretrained encoder model's config
   else:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path					)

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.decoder_config_name					)
   # Use pretrained decoder model's config
   else:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path					)

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   lowerCamelCase_						:   Optional[Any]		= True
   lowerCamelCase_						:   List[Any]		= True

   lowerCamelCase_						:   List[Any]		= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,						decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,						encoder_config=A__						,						decoder_config=A__						,						)

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   lowerCamelCase_						:   Tuple		= decoder_config.decoder_start_token_id
   lowerCamelCase_						:   int		= decoder_config.pad_token_id
   if decoder_start_token_id is None:
      lowerCamelCase_						:   Any		= decoder_config.bos_token_id
   if pad_token_id is None:
      lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id
   lowerCamelCase_						:   Tuple		= decoder_start_token_id
   lowerCamelCase_						:   Optional[Any]		= pad_token_id

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= tokenizer.convert_ids_to_tokens(model.config.pad_token_id					)

   model.save_pretrained(model_args.output_dir					)
   image_processor.save_pretrained(model_args.output_dir					)
   tokenizer.save_pretrained(model_args.output_dir					)


if __name__ == "__main__":
      main()





