


from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.xglm.modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
    )



@require_tf
class 							_UpperCamelCase :

   '''simple docstring'''







   a_				:							Optional[Any]    = XGLMConfig
   a_				:							str    = {}
   a_				:							List[Any]    = "gelu"

   def __init__(  self  : Tuple       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : str=1_4       ,  _lowerCamelCase  : Optional[Any]=7       ,  _lowerCamelCase  : Optional[Any]=True       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : List[Any]=True       ,  _lowerCamelCase  : Any=9_9       ,  _lowerCamelCase  : Optional[Any]=3_2       ,  _lowerCamelCase  : str=2       ,  _lowerCamelCase  : Dict=4       ,  _lowerCamelCase  : Optional[Any]=3_7       ,  _lowerCamelCase  : Union[str, Any]="gelu"       ,  _lowerCamelCase  : Dict=0.1       ,  _lowerCamelCase  : List[Any]=0.1       ,  _lowerCamelCase  : Dict=5_1_2       ,  _lowerCamelCase  : Optional[int]=0.02       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						parent
         __lowerCamelCase     :      Tuple       						=						batch_size
         __lowerCamelCase     :      List[Any]       						=						seq_length
         __lowerCamelCase     :      int       						=						is_training
         __lowerCamelCase     :      List[str]       						=						use_input_mask
         __lowerCamelCase     :      Tuple       						=						use_labels
         __lowerCamelCase     :      Optional[Any]       						=						vocab_size
         __lowerCamelCase     :      Dict       						=						d_model
         __lowerCamelCase     :      Union[str, Any]       						=						num_hidden_layers
         __lowerCamelCase     :      Optional[int]       						=						num_attention_heads
         __lowerCamelCase     :      List[str]       						=						ffn_dim
         __lowerCamelCase     :      int       						=						activation_function
         __lowerCamelCase     :      int       						=						activation_dropout
         __lowerCamelCase     :      List[Any]       						=						attention_dropout
         __lowerCamelCase     :      Union[str, Any]       						=						max_position_embeddings
         __lowerCamelCase     :      Union[str, Any]       						=						initializer_range
         __lowerCamelCase     :      Any       						=						None
         __lowerCamelCase     :      List[Any]       						=						0
         __lowerCamelCase     :      List[str]       						=						2
         __lowerCamelCase     :      Dict       						=						1

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         return XGLMConfig.from_pretrained("""facebook/xglm-564M"""							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size							)       ,  clip_value_min=0       ,  clip_value_max=3							)

         __lowerCamelCase     :      Optional[int]       						=						None
         if self.use_input_mask:
               __lowerCamelCase     :      str       						=						random_attention_mask([self.batch_size, self.seq_length]							)

         __lowerCamelCase     :      Any       						=						self.get_config()

         __lowerCamelCase     :      Dict       						=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]       ,  2							)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         return XGLMConfig(
             vocab_size=self.vocab_size       ,  d_model=self.hidden_size       ,  num_layers=self.num_hidden_layers       ,  attention_heads=self.num_attention_heads       ,  ffn_dim=self.ffn_dim       ,  activation_function=self.activation_function       ,  activation_dropout=self.activation_dropout       ,  attention_dropout=self.attention_dropout       ,  max_position_embeddings=self.max_position_embeddings       ,  initializer_range=self.initializer_range       ,  use_cache=_lowerCamelCase       ,  bos_token_id=self.bos_token_id       ,  eos_token_id=self.eos_token_id       ,  pad_token_id=self.pad_token_id       ,  return_dict=_lowerCamelCase       ,  )


   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						self.prepare_config_and_inputs()

         (
             __lowerCamelCase 
         )     :      List[str]       						=						config_and_inputs

         __lowerCamelCase     :      Optional[int]       						=						{
             """input_ids""": input_ids,
             """head_mask""": head_mask,
         }

         return config, inputs_dict



@require_tf
class 							_UpperCamelCase (						A,A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							str    = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   a_				:							str    = (TFXGLMForCausalLM,) if is_tf_available() else ()
   a_				:							int    = (
       {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
   )
   a_				:							Any    = False
   a_				:							Tuple    = False
   a_				:							List[str]    = False

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						TFXGLMModelTester(self							)
         __lowerCamelCase     :      str       						=						ConfigTester(self       ,  config_class=_lowerCamelCase       ,  n_embd=3_7							)

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         self.config_tester.run_common_tests()

   @slow
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __lowerCamelCase     :      Dict       						=						TFXGLMModel.from_pretrained(_lowerCamelCase							)
               self.assertIsNotNone(_lowerCamelCase							)


   @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""							)
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         super().test_resize_token_embeddings()



@require_tf
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   @slow
   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : Dict=True							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)
         __lowerCamelCase     :      Optional[int]       						=						tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]       ,  dtype=tf.intaa							)  # The dog
         # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
         # fmt: off
         __lowerCamelCase     :      int       						=						[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
         # fmt: on
         __lowerCamelCase     :      str       						=						model.generate(_lowerCamelCase       ,  do_sample=_lowerCamelCase       ,  num_beams=1							)
         if verify_outputs:
               self.assertListEqual(output_ids[0].numpy().tolist()       ,  _lowerCamelCase							)

   @slow
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""							)
         __lowerCamelCase     :      Optional[int]       						=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)

         tf.random.set_seed(0							)
         __lowerCamelCase     :      List[str]       						=						tokenizer("""Today is a nice day and"""       ,  return_tensors="""tf"""							)
         __lowerCamelCase     :      str       						=						tokenized.input_ids
         # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
         with tf.device(""":/CPU:0"""							):
               __lowerCamelCase     :      Optional[int]       						=						model.generate(_lowerCamelCase       ,  do_sample=_lowerCamelCase       ,  seed=[7, 0]							)
         __lowerCamelCase     :      Tuple       						=						tokenizer.decode(output_ids[0]       ,  skip_special_tokens=_lowerCamelCase							)

         __lowerCamelCase     :      Any       						=						(
             """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
         )
         self.assertEqual(_lowerCamelCase       ,  _lowerCamelCase							)


   @slow
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)
         __lowerCamelCase     :      int       						=						XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""							)

         __lowerCamelCase     :      List[str]       						=						"""left"""

         # use different length sentences to test batching
         __lowerCamelCase     :      int       						=						[
             """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
             """left-padding, such as in batched generation. The output for the sequence below should be the same """
             """regardless of whether left padding is applied or not. When""",
             """Hello, my dog is a little""",
         ]

         __lowerCamelCase     :      List[str]       						=						tokenizer(_lowerCamelCase       ,  return_tensors="""tf"""       ,  padding=_lowerCamelCase							)
         __lowerCamelCase     :      List[Any]       						=						inputs["""input_ids"""]

         __lowerCamelCase     :      int       						=						model.generate(input_ids=_lowerCamelCase       ,  attention_mask=inputs["""attention_mask"""]       ,  max_new_tokens=1_2							)

         __lowerCamelCase     :      Optional[int]       						=						tokenizer(sentences[0]       ,  return_tensors="""tf"""							).input_ids
         __lowerCamelCase     :      Union[str, Any]       						=						model.generate(input_ids=_lowerCamelCase       ,  max_new_tokens=1_2							)

         __lowerCamelCase     :      Optional[Any]       						=						tokenizer(sentences[1]       ,  return_tensors="""tf"""							).input_ids
         __lowerCamelCase     :      Dict       						=						model.generate(input_ids=_lowerCamelCase       ,  max_new_tokens=1_2							)

         __lowerCamelCase     :      int       						=						tokenizer.batch_decode(_lowerCamelCase       ,  skip_special_tokens=_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						tokenizer.decode(output_non_padded[0]       ,  skip_special_tokens=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						tokenizer.decode(output_padded[0]       ,  skip_special_tokens=_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						[
             """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
             """left-padding, such as in batched generation. The output for the sequence below should be the same """
             """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
             """a single""",
             """Hello, my dog is a little bit of a shy one, but he is very friendly""",
         ]
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  [non_padded_sentence, padded_sentence]							)










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Any=1_024      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      str       						=						[], []

      __lowerCamelCase     :      Any       						=						list(zip(UpperCAmelCase						,	UpperCAmelCase      )      )
      __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						sorted_examples[0]

      def is_too_big(UpperCAmelCase      :			Optional[Any]      ):
            return tok(UpperCAmelCase						,	return_tensors="""pt"""      ).input_ids.shape[1] > max_tokens

      for src, tgt in tqdm(sorted_examples[1:]      ):
            __lowerCamelCase     :      Union[str, Any]       						=						new_src + """ """ + src
            __lowerCamelCase     :      str       						=						new_tgt + """ """ + tgt
            if is_too_big(UpperCAmelCase      ) or is_too_big(UpperCAmelCase      ):  # cant fit, finalize example
                  finished_src.append(UpperCAmelCase      )
                  finished_tgt.append(UpperCAmelCase      )
                  __lowerCamelCase						, __lowerCamelCase     :      str       						=						src, tgt
            else:  # can fit, keep adding
                  __lowerCamelCase						, __lowerCamelCase     :      int       						=						cand_src, cand_tgt

    # cleanup
      if new_src:
            assert new_tgt
            finished_src.append(UpperCAmelCase      )
            finished_tgt.append(UpperCAmelCase      )
      return finished_src, finished_tgt





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Path						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						Path(UpperCAmelCase      )
      save_path.mkdir(exist_ok=UpperCAmelCase      )
      for split in ["train"]:
            __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase						, __lowerCamelCase     :      int       						=						pack_examples(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
            print(f"""packed {split} split from {len(UpperCAmelCase      )} examples -> {len(UpperCAmelCase      )}."""      )
            Path(save_path / f"""{split}.source"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
            Path(save_path / f"""{split}.target"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
      for split in ["val", "test"]:
            __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.source"""      )
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.target"""      )





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						argparse.ArgumentParser()
      parser.add_argument("""--tok_name"""						,	type=UpperCAmelCase						,	help="""like facebook/bart-large-cnn,t5-base, etc."""      )
      parser.add_argument("""--max_seq_len"""						,	type=UpperCAmelCase						,	default=128      )
      parser.add_argument("""--data_dir"""						,	type=UpperCAmelCase      )
      parser.add_argument("""--save_path"""						,	type=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						parser.parse_args()
      __lowerCamelCase     :      Tuple       						=						AutoTokenizer.from_pretrained(args.tok_name      )
      return pack_data_dir(UpperCAmelCase						,	Path(args.data_dir      )						,	args.max_seq_len						,	args.save_path      )


if __name__ == "__main__":
    packer_cli()



