




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     lowerCamelCase__:
      def __init__(				self: str    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Union[str, Any]=99    ,       UpperCamelCase_: Any=13    ,       UpperCamelCase_: List[Any]=16    ,       UpperCamelCase_: str=7    ,       UpperCamelCase_: List[str]=True    ,       UpperCamelCase_: Tuple=True    ,       UpperCamelCase_: Tuple=True    ,       UpperCamelCase_: Optional[Any]=False    ,       UpperCamelCase_: Tuple=True    ,       UpperCamelCase_: Tuple=2    ,       UpperCamelCase_: Optional[int]=32    ,       UpperCamelCase_: Optional[int]=4    ,       UpperCamelCase_: str=4    ,       UpperCamelCase_: Union[str, Any]=30    ,       UpperCamelCase_: Dict=0    ,       UpperCamelCase_: Dict=1    ,       UpperCamelCase_: Union[str, Any]=2    ,       UpperCamelCase_: Union[str, Any]=None    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						decoder_seq_length
             # For common tests
             __lowerCamelCase   =						self.decoder_seq_length
             __lowerCamelCase   =						is_training
             __lowerCamelCase   =						use_attention_mask
             __lowerCamelCase   =						use_labels

             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						d_model
             __lowerCamelCase   =						d_model
             __lowerCamelCase   =						decoder_layers
             __lowerCamelCase   =						decoder_layers
             __lowerCamelCase   =						decoder_ffn_dim
             __lowerCamelCase   =						decoder_attention_heads
             __lowerCamelCase   =						decoder_attention_heads
             __lowerCamelCase   =						eos_token_id
             __lowerCamelCase   =						bos_token_id
             __lowerCamelCase   =						pad_token_id
             __lowerCamelCase   =						decoder_start_token_id
             __lowerCamelCase   =						use_cache
             __lowerCamelCase   =						max_position_embeddings

             __lowerCamelCase   =						None
             __lowerCamelCase   =						decoder_seq_length
             __lowerCamelCase   =						2
             __lowerCamelCase   =						1
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						ids_tensor([self.batch_size, self.decoder_seq_length]    ,       self.vocab_size   )

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

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

             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Optional[Any]    ,       ):
             __lowerCamelCase   =						True
             __lowerCamelCase   =						TrOCRDecoder(config=UpperCamelCase_   ).to(UpperCamelCase_   ).eval()
             __lowerCamelCase   =						input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             __lowerCamelCase   =						model(UpperCamelCase_    ,       use_cache=UpperCamelCase_   )
             __lowerCamelCase   =						model(UpperCamelCase_   )
             __lowerCamelCase   =						model(UpperCamelCase_    ,       use_cache=UpperCamelCase_   )

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

             __lowerCamelCase   =						outputs['''past_key_values''']

             # create hypothetical next token and extent to next_input_ids
             __lowerCamelCase   =						ids_tensor((2, 1)    ,       config.vocab_size - 1   ) + 1

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

             __lowerCamelCase   =						model(UpperCamelCase_   )['''last_hidden_state''']
             __lowerCamelCase   =						model(UpperCamelCase_    ,       past_key_values=UpperCamelCase_   )['''last_hidden_state''']

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

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




      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.prepare_config_and_inputs()
             __lowerCamelCase   =						config_and_inputs

             __lowerCamelCase   =						{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
             return config, inputs_dict





@require_torch
class     lowerCamelCase__(					A__ ,     A__ ,     A__ ,     unittest.TestCase):
      UpperCAmelCase__      :	Dict    		=   (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      UpperCAmelCase__      :	Tuple    		=   (TrOCRForCausalLM,) if is_torch_available() else ()
      UpperCAmelCase__      :	Tuple    		=   {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
      UpperCAmelCase__      :	List[Any]    		=   True
      UpperCAmelCase__      :	Dict    		=   False
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						TrOCRStandaloneDecoderModelTester(self    ,       is_training=UpperCamelCase_   )
             __lowerCamelCase   =						ConfigTester(self    ,       config_class=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             pass
      def 			lowerCAmelCase__						(				self: Dict   ):
             pass
      def 			lowerCAmelCase__						(				self: str   ):
             pass
      def 			lowerCAmelCase__						(				self: str   ):
             self.config_tester.run_common_tests()
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             return




      @unittest.skip("""The model doesn\'t support left padding"""   )  # and it's not used enough to be worth fixing :)
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             pass





'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					for split in ds.keys():
										print(f'''Splitting {split} with {ds[split].num_rows} records'''   )

										# to save to val.source, val.target like summary datasets
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= tgt_path.open('''w+'''   )

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]   ):
															UpperCamelCase__   :Union[str, Any]												= x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''   )
															tgt_fp.write(ex[tgt_lang] + '''\n'''   )

					print(f'''Saved {dataset} dataset to {save_dir}'''   )


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)