






'''simple docstring'''

import argparse
import collections
import os
import re

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_table.py
SCREAMING_SNAKE_CASE__     =       'src/transformers'
SCREAMING_SNAKE_CASE__     =       'docs/source/en'
SCREAMING_SNAKE_CASE__     =       '.'







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Tuple:
      with open(__UpperCamelCase		,						"""r"""		,						encoding="""utf-8"""		,						newline="""\n"""   ) as f:
            UpperCamelCase      = f.readlines()
      # Find the start prompt.
      UpperCamelCase      = 0
      while not lines[start_index].startswith(__UpperCamelCase   ):
            start_index += 1
      start_index += 1

      UpperCamelCase      = start_index
      while not lines[end_index].startswith(__UpperCamelCase   ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]   ) <= 1:
            start_index += 1
      while len(lines[end_index]   ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE__     =       'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__     =       re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE__     =       re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE__     =       re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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







def    lowercase__			(				__UpperCamelCase   )->			str:
      UpperCamelCase      = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""		,						__UpperCamelCase   )
      return [m.group(0   ) for m in matches]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Union[str, Any]:
      UpperCamelCase      = 2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase   )
      UpperCamelCase      = (width - text_length) // 2
      UpperCamelCase      = width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent







def    lowercase__			(				)->			Union[str, Any]:
      UpperCamelCase      = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      UpperCamelCase      = {
          name: config_maping_names[code]
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if code in config_maping_names
      }
      UpperCamelCase      = {name: config.replace("""Config"""		,						""""""   ) for name, config in model_name_to_config.items()}

      # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
      UpperCamelCase      = collections.defaultdict(__UpperCamelCase   )
      UpperCamelCase      = collections.defaultdict(__UpperCamelCase   )
      UpperCamelCase      = collections.defaultdict(__UpperCamelCase   )
      UpperCamelCase      = collections.defaultdict(__UpperCamelCase   )
      UpperCamelCase      = collections.defaultdict(__UpperCamelCase   )

      # Let's lookup through all transformers object (once).
      for attr_name in dir(__UpperCamelCase   ):
            UpperCamelCase      = None
            if attr_name.endswith("""Tokenizer"""   ):
                  UpperCamelCase      = slow_tokenizers
                  UpperCamelCase      = attr_name[:-9]
            elif attr_name.endswith("""TokenizerFast"""   ):
                  UpperCamelCase      = fast_tokenizers
                  UpperCamelCase      = attr_name[:-13]
            elif _re_tf_models.match(__UpperCamelCase   ) is not None:
                  UpperCamelCase      = tf_models
                  UpperCamelCase      = _re_tf_models.match(__UpperCamelCase   ).groups()[0]
            elif _re_flax_models.match(__UpperCamelCase   ) is not None:
                  UpperCamelCase      = flax_models
                  UpperCamelCase      = _re_flax_models.match(__UpperCamelCase   ).groups()[0]
            elif _re_pt_models.match(__UpperCamelCase   ) is not None:
                  UpperCamelCase      = pt_models
                  UpperCamelCase      = _re_pt_models.match(__UpperCamelCase   ).groups()[0]

            if lookup_dict is not None:
                  while len(__UpperCamelCase   ) > 0:
                        if attr_name in model_name_to_prefix.values():
                              UpperCamelCase      = True
                              break
                        # Try again after removing the last word in the name
                        UpperCamelCase      = """""".join(camel_case_split(__UpperCamelCase   )[:-1]   )

    # Let's build that table!
      UpperCamelCase      = list(model_name_to_config.keys()   )
      model_names.sort(key=str.lower   )
      UpperCamelCase      = ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
      # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
      UpperCamelCase      = [len(__UpperCamelCase   ) + 2 for c in columns]
      UpperCamelCase      = max([len(__UpperCamelCase   ) for name in model_names]   ) + 2

      # Build the table per se
      UpperCamelCase      = """|""" + """|""".join([_center_text(__UpperCamelCase		,						__UpperCamelCase   ) for c, w in zip(__UpperCamelCase		,						__UpperCamelCase   )]   ) + """|\n"""
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]   ) + "|\n"

      UpperCamelCase      = {True: """✅""", False: """❌"""}
      for name in model_names:
            UpperCamelCase      = model_name_to_prefix[name]
            UpperCamelCase      = [
                name,
                check[slow_tokenizers[prefix]],
                check[fast_tokenizers[prefix]],
                check[pt_models[prefix]],
                check[tf_models[prefix]],
                check[flax_models[prefix]],
            ]
            table += "|" + "|".join([_center_text(__UpperCamelCase		,						__UpperCamelCase   ) for l, w in zip(__UpperCamelCase		,						__UpperCamelCase   )]   ) + "|\n"
      return table







def    lowercase__			(				__UpperCamelCase=False   )->			List[str]:
      UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = _find_text_in_file(
          filename=os.path.join(__UpperCamelCase		,						"""index.md"""   )		,						start_prompt="""<!--This table is updated automatically from the auto modules"""		,						end_prompt="""<!-- End table-->"""		,						)
      UpperCamelCase      = get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(__UpperCamelCase		,						"""index.md"""   )		,						"""w"""		,						encoding="""utf-8"""		,						newline="""\n"""   ) as f:
                        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
            else:
                  raise ValueError(
                      """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     SCREAMING_SNAKE_CASE__     =       parser.parse_args()

     check_model_table(args.fix_and_overwrite)








'''simple docstring'''

import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch







def    lowercase__			(				__UpperCamelCase   )->			Dict:  # picklable for multiprocessing
      return x.sum()







def    lowercase__			(				__UpperCamelCase   )->			Tuple:  # picklable for multiprocessing
      return i + 1






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42






class     a_						(      lowerCamelCase    ):

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



        """simple docstring"""






        UpperCamelCase      = {}
        UpperCamelCase      = []
        UpperCamelCase      = 1
        UpperCamelCase      = [1, 2]
        UpperCamelCase      = {"""a""": 1, """b""": 2}
        UpperCamelCase      = {"""a""": [1, 2], """b""": [3, 4]}
        UpperCamelCase      = {"""a""": {"""1""": 1}, """b""": 2}
        UpperCamelCase      = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
        UpperCamelCase      = {}
        UpperCamelCase      = []
        UpperCamelCase      = 2
        UpperCamelCase      = [2, 3]
        UpperCamelCase      = {"""a""": 2, """b""": 3}
        UpperCamelCase      = {"""a""": [2, 3], """b""": [4, 5]}
        UpperCamelCase      = {"""a""": {"""1""": 2}, """b""": 3}
        UpperCamelCase      = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = 2
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = {"""a""": np.eye(2				), """b""": np.zeros(3				), """c""": np.ones(2				)}
        UpperCamelCase      = {"""a""": 2, """b""": 0, """c""": 2}
        UpperCamelCase      = {
            """a""": np.eye(2				).astype(_SCREAMING_SNAKE_CASE				),
            """b""": np.zeros(3				).astype(_SCREAMING_SNAKE_CASE				),
            """c""": np.ones(2				).astype(_SCREAMING_SNAKE_CASE				),
        }
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		map_numpy=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		map_numpy=_SCREAMING_SNAKE_CASE				).items()}      ,		{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}      ,		)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		map_numpy=_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		map_numpy=_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				).items()}      ,		{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}      ,		)
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):  # can't pickle a local lambda
              map_nested(lambda _SCREAMING_SNAKE_CASE				: x + 1      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = {"""a""": 1, """b""": 2}
        UpperCamelCase      = {"""a""": 3, """b""": 4}
        UpperCamelCase      = {"""a""": 5, """b""": 6}
        UpperCamelCase      = sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]				)
        self.assertEqual(sorted(zip_dict(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)      ,		_SCREAMING_SNAKE_CASE				)


  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        class     a_						:
          lowercase							=				"""bar"""


        UpperCamelCase      = Foo()
        self.assertEqual(foo.my_attr      ,		"""bar"""				)
        with temporary_assignment(_SCREAMING_SNAKE_CASE      ,		"""my_attr"""      ,		"""BAR"""				):
              self.assertEqual(foo.my_attr      ,		"""BAR"""				)
        self.assertEqual(foo.my_attr      ,		"""bar"""				)







@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""		,						[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]		,						)
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      with patch("""datasets.utils.py_utils._single_map_nested"""   ) as mock_single_map_nested, patch(
          """datasets.parallel.parallel.Pool"""   ) as mock_multiprocessing_pool:
            UpperCamelCase      = {F"{i}": i for i in range(__UpperCamelCase   )}
            UpperCamelCase      = map_nested(lambda __UpperCamelCase   : x + 10		,						__UpperCamelCase		,						num_proc=__UpperCamelCase		,						parallel_min_length=16   )
            if expected_num_proc == 1:
                  assert mock_single_map_nested.called
                  assert not mock_multiprocessing_pool.called
            else:
                  assert not mock_single_map_nested.called
                  assert mock_multiprocessing_pool.called
                  assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc






class     a_						(      lowerCamelCase    ):

  @require_tf
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        import tensorflow as tf
        from tensorflow.keras import layers

        UpperCamelCase      = layers.Dense(2				)

        def gen_random_output():
              UpperCamelCase      = tf.random.uniform((1, 3)				)
              return model(_SCREAMING_SNAKE_CASE				).numpy()

        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

        np.testing.assert_equal(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertGreater(np.abs(outa - outa				).sum()      ,		0				)

  @require_torch
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        import torch

        def gen_random_output():
              UpperCamelCase      = torch.nn.Linear(3      ,		2				)
              UpperCamelCase      = torch.rand(1      ,		3				)
              return model(_SCREAMING_SNAKE_CASE				).detach().numpy()

        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

        np.testing.assert_equal(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertGreater(np.abs(outa - outa				).sum()      ,		0				)


  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        def gen_random_output():
              return np.random.rand(1      ,		3				)

        with temp_seed(42				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

        np.testing.assert_equal(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertGreater(np.abs(outa - outa				).sum()      ,		0				)







@pytest.mark.parametrize("""input_data"""		,						[{}]   )
def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = NestedDataStructure(__UpperCamelCase   ).data
      assert output_data == input_data







@pytest.mark.parametrize(
    """data, expected_output"""		,						[
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]		,						)
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = NestedDataStructure(__UpperCamelCase   ).flatten()
      assert output == expected_output







def    lowercase__			(				)->			Union[str, Any]:
      UpperCamelCase      = A(x=1		,						y="""foobar"""   )
      UpperCamelCase      = {"""x""": 1, """y""": """foobar"""}
      assert asdict(__UpperCamelCase   ) == expected_output

      UpperCamelCase      = {"""a""": {"""b""": A(x=10		,						y="""foo"""   )}, """c""": [A(x=20		,						y="""bar"""   )]}
      UpperCamelCase      = {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
      assert asdict(__UpperCamelCase   ) == expected_output

      with pytest.raises(__UpperCamelCase   ):
            asdict([1, A(x=10		,						y="""foo"""   )]   )







def    lowercase__			(				__UpperCamelCase   )->			List[Any]:
      return text.split()







def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      yield (time.time(), content)
      time.sleep(2   )
      yield (time.time(), content)







def    lowercase__			(				)->			int:
      with Pool(2   ) as pool:
            UpperCamelCase      = list(iflatmap_unordered(__UpperCamelCase		,						_split_text		,						kwargs_iterable=[{"""text""": """hello there"""}] * 10   )   )
            assert out.count("""hello"""   ) == 10
            assert out.count("""there"""   ) == 10
            assert len(__UpperCamelCase   ) == 20

      # check multiprocess from pathos (uses dill for pickling)
      with multiprocess.Pool(2   ) as pool:
            UpperCamelCase      = list(iflatmap_unordered(__UpperCamelCase		,						_split_text		,						kwargs_iterable=[{"""text""": """hello there"""}] * 10   )   )
            assert out.count("""hello"""   ) == 10
            assert out.count("""there"""   ) == 10
            assert len(__UpperCamelCase   ) == 20

      # check that we get items as fast as possible
      with Pool(2   ) as pool:
            UpperCamelCase      = []
            for yield_time, content in iflatmap_unordered(
                __UpperCamelCase		,						_aseconds_generator_of_aitems_with_timing		,						kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]   ):
                  assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
                  out.append(__UpperCamelCase   )
            assert out.count("""a"""   ) == 2
            assert out.count("""b"""   ) == 2
            assert len(__UpperCamelCase   ) == 4
