

import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__lowercase				      =						"""bert-base-cased"""
__lowercase				      =						"""google/pegasus-xsum"""
__lowercase				      =						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__lowercase				      =						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__lowercase				      =						"""patrickvonplaten/t5-tiny-random"""
__lowercase				      =						"""sshleifer/bart-tiny-random"""
__lowercase				      =						"""sshleifer/tiny-mbart"""
__lowercase				      =						"""sshleifer/tiny-marian-en-de"""







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		'''\n'''.join(SCREAMING_SNAKE_CASE   )
      Path(SCREAMING_SNAKE_CASE   ).open('''w'''   ).writelines(SCREAMING_SNAKE_CASE   )







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE		,				f"{split}.source"   )		,				SCREAMING_SNAKE_CASE   )
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE		,				f"{split}.target"   )		,				SCREAMING_SNAKE_CASE   )
      return tmp_dir





class    _lowercase				(					__lowerCamelCase			):

   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]			,						)
   @slow
   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Any       ) ->			Dict:
         """simple docstring"""





         A_							       =		AutoTokenizer.from_pretrained(lowerCamelCase__       )
         A_							       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         A_							       =		max(len(tokenizer.encode(lowerCamelCase__       )       ) for a in ARTICLES       )
         A_							       =		max(len(tokenizer.encode(lowerCamelCase__       )       ) for a in SUMMARIES       )
         A_							       =		4
         A_							       =		8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         A_       ,A_							       =		'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
         A_							       =		SeqaSeqDataset(
             lowerCamelCase__			,						data_dir=lowerCamelCase__			,						type_path='''train'''			,						max_source_length=lowerCamelCase__			,						max_target_length=lowerCamelCase__			,						src_lang=lowerCamelCase__			,						tgt_lang=lowerCamelCase__			,						)
         A_							       =		DataLoader(lowerCamelCase__			,						batch_size=2			,						collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
               assert isinstance(lowerCamelCase__			,						lowerCamelCase__       )
               assert batch["attention_mask"].shape == batch["input_ids"].shape
               # show that articles were trimmed.
               assert batch["input_ids"].shape[1] == max_src_len
               # show that targets are the same len
               assert batch["labels"].shape[1] == max_tgt_len
               if tok_name != MBART_TINY:
                     continue
               # check language codes in correct place
               A_							       =		shift_tokens_right(batch['''labels''']			,						tokenizer.pad_token_id       )
               assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
               assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
               assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
               assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

               break  # No need to test every batch

   @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    List[str]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		AutoTokenizer.from_pretrained(lowerCamelCase__       )
         A_							       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         A_							       =		max(len(tokenizer.encode(lowerCamelCase__       )       ) for a in ARTICLES       )
         A_							       =		max(len(tokenizer.encode(lowerCamelCase__       )       ) for a in SUMMARIES       )
         A_							       =		4
         A_							       =		LegacySeqaSeqDataset(
             lowerCamelCase__			,						data_dir=lowerCamelCase__			,						type_path='''train'''			,						max_source_length=2_0			,						max_target_length=lowerCamelCase__			,						)
         A_							       =		DataLoader(lowerCamelCase__			,						batch_size=2			,						collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
               assert batch["attention_mask"].shape == batch["input_ids"].shape
               # show that articles were trimmed.
               assert batch["input_ids"].shape[1] == max_len_source
               assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
               # show that targets were truncated
               assert batch["labels"].shape[1] == trunc_target  # Truncated
               assert max_len_target > trunc_target  # Truncated
               break  # No need to test every batch

   def     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





         A_							       =		AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''       )

         A_							       =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         A_							       =		tmp_dir.joinpath('''train.source'''       ).open().readlines()
         A_							       =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(lowerCamelCase__			,						lowerCamelCase__			,						1_2_8			,						lowerCamelCase__       )
         A_							       =		{x.name for x in tmp_dir.iterdir()}
         A_							       =		{x.name for x in save_dir.iterdir()}
         A_							       =		save_dir.joinpath('''train.source'''       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(lowerCamelCase__       ) < len(lowerCamelCase__       )
         assert len(lowerCamelCase__       ) == 1
         assert len(packed_examples[0]       ) == sum(len(lowerCamelCase__       ) for x in orig_examples       )
         assert orig_paths == new_paths

   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,						reason='''This test requires fairseq'''       )
   def     UpperCamelCase					(				self	:    Any       ) ->			List[Any]:
         """simple docstring"""





         if not FAIRSEQ_AVAILABLE:
               return
         A_       ,A_       ,A_							       =		self._get_dataset(max_len=6_4       )
         A_							       =		6_4
         A_							       =		ds.make_dynamic_sampler(lowerCamelCase__			,						required_batch_size_multiple=lowerCamelCase__       )
         A_							       =		[len(lowerCamelCase__       ) for x in batch_sampler]
         assert len(set(lowerCamelCase__       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(lowerCamelCase__       ) == len(lowerCamelCase__       )  # no dropped or added examples
         A_							       =		DataLoader(lowerCamelCase__			,						batch_sampler=lowerCamelCase__			,						collate_fn=ds.collate_fn			,						num_workers=2       )
         A_							       =		[]
         A_							       =		[]
         for batch in data_loader:
               A_							       =		batch['''input_ids'''].shape
               A_							       =		src_shape[0]
               assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
               A_							       =		np.product(batch['''input_ids'''].shape       )
               num_src_per_batch.append(lowerCamelCase__       )
               if num_src_tokens > (max_tokens * 1.1):
                     failures.append(lowerCamelCase__       )
         assert num_src_per_batch[0] == max(lowerCamelCase__       )
         if failures:
               raise AssertionError(F"too many tokens in {len(lowerCamelCase__       )} batches"       )

   def     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





         A_       ,A_       ,A_							       =		self._get_dataset(max_len=5_1_2       )
         A_							       =		2
         A_							       =		ds.make_sortish_sampler(lowerCamelCase__			,						shuffle=lowerCamelCase__       )

         A_							       =		DataLoader(lowerCamelCase__			,						batch_size=lowerCamelCase__			,						collate_fn=ds.collate_fn			,						num_workers=2       )
         A_							       =		DataLoader(lowerCamelCase__			,						batch_size=lowerCamelCase__			,						collate_fn=ds.collate_fn			,						num_workers=2			,						sampler=lowerCamelCase__       )

         A_							       =		tokenizer.pad_token_id

         def count_pad_tokens(lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    str="input_ids"       ):
               return [batch[k].eq(lowerCamelCase__       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(lowerCamelCase__			,						k='''labels'''       )       ) < sum(count_pad_tokens(lowerCamelCase__			,						k='''labels'''       )       )
         assert sum(count_pad_tokens(lowerCamelCase__       )       ) < sum(count_pad_tokens(lowerCamelCase__       )       )
         assert len(lowerCamelCase__       ) == len(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Dict=1_0_0_0			,						lowerCamelCase__	:    List[str]=1_2_8       ) ->			Any:
         """simple docstring"""





         if os.getenv('''USE_REAL_DATA'''			,						lowerCamelCase__       ):
               A_							       =		'''examples/seq2seq/wmt_en_ro'''
               A_							       =		max_len * 2 * 6_4
               if not Path(lowerCamelCase__       ).joinpath('''train.len'''       ).exists():
                     save_len_file(lowerCamelCase__			,						lowerCamelCase__       )
         else:
               A_							       =		'''examples/seq2seq/test_data/wmt_en_ro'''
               A_							       =		max_len * 4
               save_len_file(lowerCamelCase__			,						lowerCamelCase__       )

         A_							       =		AutoTokenizer.from_pretrained(lowerCamelCase__       )
         A_							       =		SeqaSeqDataset(
             lowerCamelCase__			,						data_dir=lowerCamelCase__			,						type_path='''train'''			,						max_source_length=lowerCamelCase__			,						max_target_length=lowerCamelCase__			,						n_obs=lowerCamelCase__			,						)
         return ds, max_tokens, tokenizer

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[Any]:
         """simple docstring"""





         A_       ,A_       ,A_							       =		self._get_dataset()
         A_							       =		set(DistributedSortishSampler(lowerCamelCase__			,						2_5_6			,						num_replicas=2			,						rank=0			,						add_extra_examples=lowerCamelCase__       )       )
         A_							       =		set(DistributedSortishSampler(lowerCamelCase__			,						2_5_6			,						num_replicas=2			,						rank=1			,						add_extra_examples=lowerCamelCase__       )       )
         assert idsa.intersection(lowerCamelCase__       ) == set()





   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]			,						)
   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    Tuple       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		AutoTokenizer.from_pretrained(lowerCamelCase__			,						use_fast=lowerCamelCase__       )
         if tok_name == MBART_TINY:
               A_							       =		SeqaSeqDataset(
                   lowerCamelCase__			,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )			,						type_path='''train'''			,						max_source_length=4			,						max_target_length=8			,						src_lang='''EN'''			,						tgt_lang='''FR'''			,						)
               A_							       =		train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
               A_							       =		SeqaSeqDataset(
                   lowerCamelCase__			,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )			,						type_path='''train'''			,						max_source_length=4			,						max_target_length=8			,						)
               A_							       =		train_dataset.dataset_kwargs
               assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
               assert len(lowerCamelCase__       ) == 1 if tok_name == BART_TINY else len(lowerCamelCase__       ) == 0








import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowercase				      =						"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowercase				      =						direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowercase				      =						transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowercase				      =						{
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		False
      for attribute in attributes:
            for modeling_source in source_strings:
                  # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                  if (
                      f"config.{attribute}" in modeling_source
                      or f"getattr(config, \"{attribute}\"" in modeling_source
                      or f"getattr(self.config, \"{attribute}\"" in modeling_source
                  ):
                        A_							       =		True
                  # Deal with multi-line cases
                  elif (
                      re.search(
                          Rf"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""		,				SCREAMING_SNAKE_CASE		,				)
                      is not None
                  ):
                        A_							       =		True
                  # `SequenceSummary` is called with `SequenceSummary(config)`
                  elif attribute in [
                      "summary_type",
                      "summary_use_proj",
                      "summary_activation",
                      "summary_last_dropout",
                      "summary_proj_to_labels",
                      "summary_first_dropout",
                  ]:
                        if "SequenceSummary" in modeling_source:
                              A_							       =		True
                  if attribute_used:
                        break
            if attribute_used:
                  break

    # common and important attributes, even if they do not always appear in the modeling files
      A_							       =		[
          '''bos_index''',
          '''eos_index''',
          '''pad_index''',
          '''unk_index''',
          '''mask_index''',
          '''image_size''',
          '''use_cache''',
          '''out_features''',
          '''out_indices''',
      ]
      A_							       =		['''encoder_no_repeat_ngram_size''']

      # Special cases to be allowed
      A_							       =		True
      if not attribute_used:
            A_							       =		False
            for attribute in attributes:
                  # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                  if attribute in ["is_encoder_decoder"] and default_value is True:
                        A_							       =		True
                  elif attribute in ["tie_word_embeddings"] and default_value is False:
                        A_							       =		True

                  # Allow cases without checking the default value in the configuration class
                  elif attribute in attributes_to_allow + attributes_used_in_generation:
                        A_							       =		True
                  elif attribute.endswith('''_token_id'''   ):
                        A_							       =		True

                  # configuration class specific cases
                  if not case_allowed:
                        A_							       =		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__		,				[]   )
                        A_							       =		allowed_cases is True or attribute in allowed_cases

      return attribute_used or case_allowed







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		dict(inspect.signature(config_class.__init__   ).parameters   )
      A_							       =		[x for x in list(signature.keys()   ) if x not in ['''self''', '''kwargs''']]
      A_							       =		[signature[param].default for param in parameter_names]

      # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
      # as one variant is used, the test should pass
      A_							       =		{}
      if len(config_class.attribute_map   ) > 0:
            A_							       =		{v: k for k, v in config_class.attribute_map.items()}

      # Get the path to modeling source files
      A_							       =		inspect.getsourcefile(SCREAMING_SNAKE_CASE   )
      A_							       =		os.path.dirname(SCREAMING_SNAKE_CASE   )
      # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
      A_							       =		[os.path.join(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ) for fn in os.listdir(SCREAMING_SNAKE_CASE   ) if fn.startswith('''modeling_'''   )]

      # Get the source code strings
      A_							       =		[]
      for path in modeling_paths:
            if os.path.isfile(SCREAMING_SNAKE_CASE   ):
                  with open(SCREAMING_SNAKE_CASE   ) as fp:
                        modeling_sources.append(fp.read()   )

      A_							       =		[]
      for config_param, default_value in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
            # `attributes` here is all the variant names for `config_param`
            A_							       =		[config_param]
            # some configuration classes have non-empty `attribute_map`, and both names could be used in the
            # corresponding modeling files. As long as one of them appears, it is fine.
            if config_param in reversed_attribute_map:
                  attributes.append(reversed_attribute_map[config_param]   )

            if not check_attribute_being_used(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
                  unused_attributes.append(attributes[0]   )

      return sorted(SCREAMING_SNAKE_CASE   )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      A_							       =		{}
      for _config_class in list(CONFIG_MAPPING.values()   ):
            # Skip deprecated models
            if "models.deprecated" in _config_class.__module__:
                  continue
            # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
            A_							       =		[
                cls
                for name, cls in inspect.getmembers(
                    inspect.getmodule(_config_class   )		,				lambda SCREAMING_SNAKE_CASE   : inspect.isclass(SCREAMING_SNAKE_CASE   )
                    and issubclass(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                    and inspect.getmodule(SCREAMING_SNAKE_CASE   ) == inspect.getmodule(_config_class   )		,				)
            ]
            for config_class in config_classes_in_module:
                  A_							       =		check_config_attributes_being_used(SCREAMING_SNAKE_CASE   )
                  if len(SCREAMING_SNAKE_CASE   ) > 0:
                        A_							       =		unused_attributes

      if len(SCREAMING_SNAKE_CASE   ) > 0:
            A_							       =		'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
            for name, attributes in configs_with_unused_attributes.items():
                  error += f"{name}: {attributes}\n"

            raise ValueError(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      check_config_attributes()





