

import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







class   __lowercase				(_UpperCAmelCase			):
 def      UpperCamelCase__  (							self				,							A_				,							A_=False ) ->str:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Dict = (
           filename_skip_take['''filename'''],
           filename_skip_take['''skip'''] if '''skip''' in filename_skip_take else None,
           filename_skip_take['''take'''] if '''take''' in filename_skip_take else None,
       )
       open(os.path.join(A_ )				,							'''wb''' ).close()
       __lowerCAmelCase :						List[str] = pa.Table.from_pydict({'''filename''': [Path(A_ ).name] * 100} )
       if take == -1:
             __lowerCAmelCase :						List[Any] = len(A_ ) - skip
       if skip is not None and take is not None:
             __lowerCAmelCase :						Optional[Any] = pa_table.slice(A_				,							A_ )
       return pa_table







class   __lowercase				(_UpperCAmelCase			):
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = '''my_name'''
       __lowerCAmelCase :						Optional[Any] = SplitInfo(name='''train'''				,							num_examples=100 )
       __lowerCAmelCase :						Dict = SplitInfo(name='''test'''				,							num_examples=100 )
       __lowerCAmelCase :						Optional[Any] = [train_info, test_info]
       __lowerCAmelCase :						Union[str, Any] = SplitDict()
       split_dict.add(A_ )
       split_dict.add(A_ )
       __lowerCAmelCase :						Any = DatasetInfo(splits=A_ )

       with tempfile.TemporaryDirectory() as tmp_dir:
             __lowerCAmelCase :						Tuple = ReaderTest(A_				,							A_ )

             __lowerCAmelCase :						Any = '''test[:33%]'''
             __lowerCAmelCase :						Optional[int] = Dataset(**reader.read(A_				,							A_				,							A_ ) )
             self.assertEqual(dset['''filename'''][0]				,							f"""{name}-test""" )
             self.assertEqual(dset.num_rows				,							33 )
             self.assertEqual(dset.num_columns				,							1 )

             __lowerCAmelCase :						Any = ['''train''', '''test[:33%]''']
             __lowerCAmelCase :						Any = [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''' )]
             for instructions in [instructionsa, instructionsa]:
                   __lowerCAmelCase :						Dict = [reader.read(A_				,							A_				,							A_ ) for instr in instructions]
                   __lowerCAmelCase,							__lowerCAmelCase :						str = (Dataset(**A_ ) for dataset_kwargs in datasets_kwargs)
                   self.assertEqual(train_dset['''filename'''][0]				,							f"""{name}-train""" )
                   self.assertEqual(train_dset.num_rows				,							100 )
                   self.assertEqual(train_dset.num_columns				,							1 )
                   self.assertIsInstance(train_dset.split				,							A_ )
                   self.assertEqual(str(train_dset.split )				,							'''train''' )
                   self.assertEqual(test_dset['''filename'''][0]				,							f"""{name}-test""" )
                   self.assertEqual(test_dset.num_rows				,							33 )
                   self.assertEqual(test_dset.num_columns				,							1 )
                   self.assertIsInstance(test_dset.split				,							A_ )
                   self.assertEqual(str(test_dset.split )				,							'''test[:33%]''' )
                   del train_dset, test_dset
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = '''my_name'''
       __lowerCAmelCase :						int = SplitInfo(name='''train'''				,							num_examples=1000				,							shard_lengths=[100] * 10 )
       __lowerCAmelCase :						Tuple = [train_info]
       __lowerCAmelCase :						int = SplitDict()
       split_dict.add(A_ )
       __lowerCAmelCase :						int = DatasetInfo(splits=A_ )

       with tempfile.TemporaryDirectory() as tmp_dir:
             __lowerCAmelCase :						Optional[Any] = ReaderTest(A_				,							A_ )

             __lowerCAmelCase :						int = '''train[:33%]'''
             __lowerCAmelCase :						Union[str, Any] = Dataset(**reader.read(A_				,							A_				,							A_ ) )
             self.assertEqual(dset['''filename'''][0]				,							f"""{name}-train-00000-of-00010""" )
             self.assertEqual(dset['''filename'''][-1]				,							f"""{name}-train-00003-of-00010""" )
             self.assertEqual(dset.num_rows				,							330 )
             self.assertEqual(dset.num_columns				,							1 )





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = SplitInfo(name='''train'''				,							num_examples=100 )
       __lowerCAmelCase :						Any = SplitInfo(name='''test'''				,							num_examples=100 )
       __lowerCAmelCase :						Dict = SplitDict()
       split_dict.add(A_ )
       split_dict.add(A_ )
       __lowerCAmelCase :						Optional[int] = DatasetInfo(splits=A_ )

       with tempfile.TemporaryDirectory() as tmp_dir:
             __lowerCAmelCase :						str = ReaderTest(A_				,							A_ )

             __lowerCAmelCase :						str = [
                 {'''filename''': os.path.join(A_				,							'''train''' )},
                 {'''filename''': os.path.join(A_				,							'''test''' ), '''skip''': 10, '''take''': 10},
             ]
             __lowerCAmelCase :						str = Dataset(**reader.read_files(A_				,							original_instructions='''train+test[10:20]''' ) )
             self.assertEqual(dset.num_rows				,							110 )
             self.assertEqual(dset.num_columns				,							1 )
             del dset







@pytest.mark.parametrize('''in_memory'''       ,     [False, True]						)
def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Dict = arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            __lowerCAmelCase :						Union[str, Any] = ArrowReader.read_table(lowercase__       ,     in_memory=lowercase__						)
      assert table.shape == dataset.data.shape
      assert set(table.column_names						) == set(dataset.data.column_names						)
      assert dict(table.to_pydict()						) == dict(dataset.data.to_pydict()						)  # to_pydict returns OrderedDict







@pytest.mark.parametrize('''in_memory'''       ,     [False, True]						)
def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Tuple = arrow_file
      __lowerCAmelCase :						Optional[int] = ArrowReader(''''''       ,     lowercase__						)
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            __lowerCAmelCase :						Optional[int] = reader.read_files([{'''filename''': filename}]       ,     in_memory=lowercase__						)
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      __lowerCAmelCase :						Dict = dataset_kwargs['''arrow_table''']
      assert table.shape == dataset.data.shape
      assert set(table.column_names						) == set(dataset.data.column_names						)
      assert dict(table.to_pydict()						) == dict(dataset.data.to_pydict()						)  # to_pydict returns OrderedDict







def 		_lowercase			(					):
      assert ReadInstruction('''train'''       ,     to=1_0       ,     unit='''abs'''						).to_spec() == "train[:10]"
      assert ReadInstruction('''train'''       ,     from_=-8_0       ,     to=1_0       ,     unit='''%'''						).to_spec() == "train[-80%:10%]"

      __lowerCAmelCase :						List[Any] = '''train+test'''
      assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_test

      __lowerCAmelCase :						Dict = '''train[2:10]'''
      assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_abs

      __lowerCAmelCase :						str = '''train[15%:-20%]'''
      assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_pct

      __lowerCAmelCase :						Any = '''train[:10%](closest)'''
      assert ReadInstruction.from_spec(lowercase__						).to_spec() == "train[:10%]"

      __lowerCAmelCase :						List[str] = '''train[:10%](pct1_dropremainder)'''
      assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_pct_rounding

      __lowerCAmelCase :						Any = '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
      assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_test_pct_rounding







def 		_lowercase			(					):
      __lowerCAmelCase :						List[Any] = '''dummy'''
      __lowerCAmelCase :						List[Any] = [SplitInfo(name='''train'''       ,     num_examples=1_0_0						)]
      __lowerCAmelCase :						str = '''train[:33%]'''
      __lowerCAmelCase :						Optional[int] = '''arrow'''
      __lowerCAmelCase :						Any = '''prefix'''

      __lowerCAmelCase :						Union[str, Any] = make_file_instructions(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
      assert isinstance(lowercase__       ,     lowercase__						)
      assert file_instructions.num_examples == 3_3
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(lowercase__       ,     f"""{name}-train.arrow"""						), "skip": 0, "take": 3_3}
      ]

      __lowerCAmelCase :						str = [SplitInfo(name='''train'''       ,     num_examples=1_0_0       ,     shard_lengths=[1_0] * 1_0						)]
      __lowerCAmelCase :						str = make_file_instructions(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
      assert isinstance(lowercase__       ,     lowercase__						)
      assert file_instructions.num_examples == 3_3
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(lowercase__       ,     f"""{name}-train-00000-of-00010.arrow"""						), "skip": 0, "take": -1},
          {"filename": os.path.join(lowercase__       ,     f"""{name}-train-00001-of-00010.arrow"""						), "skip": 0, "take": -1},
          {"filename": os.path.join(lowercase__       ,     f"""{name}-train-00002-of-00010.arrow"""						), "skip": 0, "take": -1},
          {"filename": os.path.join(lowercase__       ,     f"""{name}-train-00003-of-00010.arrow"""						), "skip": 0, "take": 3},
      ]







@pytest.mark.parametrize('''name, expected_exception'''       ,     [(None, TypeError), ('''''', ValueError)]						)
def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Tuple = [SplitInfo(name='''train'''       ,     num_examples=1_0_0						)]
      __lowerCAmelCase :						Tuple = '''train'''
      __lowerCAmelCase :						List[Any] = '''arrow'''
      __lowerCAmelCase :						Optional[int] = '''prefix_path'''
      with pytest.raises(lowercase__						):
            __lowerCAmelCase :						Dict = make_file_instructions(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)





import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor







def 		_lowercase			(					):
      __lowerCAmelCase :						Union[str, Any] = set()
      # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
      for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
            __lowerCAmelCase :						Any = getattr(transformers.models.auto       ,     lowercase__       ,     lowercase__						)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            __lowerCAmelCase :						Optional[int] = [
                x
                for x in dir(lowercase__						)
                if x.endswith('''_MAPPING_NAMES'''						)
                and (x.startswith('''MODEL_'''						) or x.startswith('''TF_MODEL_'''						) or x.startswith('''FLAX_MODEL_'''						))
            ]
            for name in mapping_names:
                  __lowerCAmelCase :						int = getattr(lowercase__       ,     lowercase__						)
                  if mapping is not None:
                        for v in mapping.values():
                              if isinstance(lowercase__       ,     (list, tuple)						):
                                    model_names.update(lowercase__						)
                              elif isinstance(lowercase__       ,     lowercase__						):
                                    model_names.add(lowercase__						)

      return sorted(lowercase__						)







def 		_lowercase			(					):
      # All model names defined in auto mappings
      __lowerCAmelCase :						int = set(get_all_model_names()						)

      with open('''tests/utils/tiny_model_summary.json'''						) as fp:
            __lowerCAmelCase :						Tuple = json.load(lowercase__						)
      __lowerCAmelCase :						Tuple = set()
      for model_base_name in tiny_model_info:
            tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']						)

      # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
      __lowerCAmelCase :						List[Any] = model_names.difference(lowercase__						)
      for model_name in copy.copy(lowercase__						):
            if not model_name.startswith('''TF'''						) and f"""TF{model_name}""" in not_on_hub:
                  tiny_models_names.remove(lowercase__						)
            elif model_name.startswith('''TF'''						) and model_name[2:] in not_on_hub:
                  tiny_models_names.remove(lowercase__						)

      return sorted(lowercase__						)







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      __lowerCAmelCase :						Optional[Any] = get_all_model_names()
      __lowerCAmelCase :						str = hf_api.list_models(
          filter=ModelFilter(
              author='''hf-internal-testing'''       ,     )						)
      __lowerCAmelCase :						List[str] = set()
      for x in models:
            __lowerCAmelCase :						str = x.modelId
            __lowerCAmelCase,							__lowerCAmelCase :						List[Any] = model.split('''/'''						)
            if not model.startswith('''tiny-random-'''						):
                  continue
            __lowerCAmelCase :						Dict = model.replace('''tiny-random-'''       ,     ''''''						)
            if not model[0].isupper():
                  continue
            if model not in model_names and model not in special_models:
                  continue
            _models.add(lowercase__						)

      __lowerCAmelCase :						Optional[Any] = sorted(_models						)
      # All tiny model names on Hub
      __lowerCAmelCase :						List[str] = {}
      for model in models:
            __lowerCAmelCase :						int = f"""hf-internal-testing/tiny-random-{model}"""
            __lowerCAmelCase :						Dict = model.split('''-'''						)[0]
            try:
                  __lowerCAmelCase :						Optional[Any] = hf_api.repo_info(lowercase__						)
                  __lowerCAmelCase :						str = {
                      '''tokenizer_classes''': set(),
                      '''processor_classes''': set(),
                      '''model_classes''': set(),
                      '''sha''': repo_info.sha,
                  }
            except Exception:
                  continue
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						str = AutoTokenizer.from_pretrained(lowercase__						)
                  content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						Any = AutoTokenizer.from_pretrained(lowercase__       ,     use_fast=lowercase__						)
                  content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						str = AutoImageProcessor.from_pretrained(lowercase__						)
                  content["processor_classes"].add(img_p.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						int = AutoFeatureExtractor.from_pretrained(lowercase__						)
                  if not isinstance(lowercase__       ,     lowercase__						):
                        content["processor_classes"].add(feat_p.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						int = getattr(lowercase__       ,     lowercase__						)
                  __lowerCAmelCase :						str = model_class.from_pretrained(lowercase__						)
                  content["model_classes"].add(m.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						Optional[Any] = getattr(lowercase__       ,     f"""TF{model}"""						)
                  __lowerCAmelCase :						Any = model_class.from_pretrained(lowercase__						)
                  content["model_classes"].add(m.__class__.__name__						)
            except Exception:
                  pass

            __lowerCAmelCase :						Tuple = sorted(content['''tokenizer_classes''']						)
            __lowerCAmelCase :						List[Any] = sorted(content['''processor_classes''']						)
            __lowerCAmelCase :						int = sorted(content['''model_classes''']						)

            __lowerCAmelCase :						Union[str, Any] = content
            with open(os.path.join(lowercase__       ,     '''hub_tiny_model_summary.json'''						)       ,     '''w'''						) as fp:
                  json.dump(lowercase__       ,     lowercase__       ,     ensure_ascii=lowercase__       ,     indent=4						)


if __name__ == "__main__":
 _UpperCamelCase			      =		argparse.ArgumentParser()
 parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
 _UpperCamelCase			      =		parser.parse_args()

 # This has to be `spawn` to avoid hanging forever!
 multiprocessing.set_start_method("spawn")

 _UpperCamelCase			      =		"tiny_models"
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		None
 _UpperCamelCase			      =		get_tiny_model_names_from_repo()
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		"hf-internal-testing"

 create_tiny_models(
     output_path,
     all,
     model_types,
     models_to_skip,
     no_check,
     upload,
     organization,
     token=os.environ.get("TOKEN", None),
     num_workers=args.num_workers,
 )


