





"""simple docstring"""




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
   from tokenizers import Tokenizer
   from tokenizers.models import WordPiece

class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			List[str]									=    list(tokenizer_class.max_model_input_sizes.keys()    )
        for model_name in sa_models[:1]:
          A_    :			List[Any]									=    tokenizer_class.from_pretrained(snake_case_    )
          self.assertIsNotNone(snake_case_    )
          self.assertIsInstance(snake_case_   , snake_case_    )
          self.assertIsInstance(snake_case_   , snake_case_    )

          for special_tok in tokenizer.all_special_tokens:
            self.assertIsInstance(snake_case_   , snake_case_    )
            A_    :			Optional[int]									=    tokenizer.convert_tokens_to_ids(snake_case_    )
            self.assertIsInstance(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        A_    :			Tuple									=    pickle.dumps(snake_case_    )
        self.assertIsNotNone(snake_case_    )

        A_    :			Tuple									=    pickle.loads(snake_case_    )

        # Ensure is_fast is correctly restored
        self.assertEqual(be_restored.is_fast   , be_original.is_fast    )

        # Ensure encodings are potentially correctly restored
        if be_original.is_fast:
          self.assertIsNotNone(be_restored.encodings    )
        else:
          self.assertIsNone(be_restored.encodings    )

        # Ensure the keys are the same
        for original_v, restored_v in zip(be_original.values()   , be_restored.values()    ):
          if equal_op:
            self.assertTrue(equal_op(snake_case_   , snake_case_    )    )
          else:
            self.assertEqual(snake_case_   , snake_case_    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.check_tokenizer_from_pretrained(snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.assertEqual(TensorType('tf'    )   , TensorType.TENSORFLOW    )
        self.assertEqual(TensorType('pt'    )   , TensorType.PYTORCH    )
        self.assertEqual(TensorType('np'    )   , TensorType.NUMPY    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import numpy as np

        A_    :			List[str]									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			Optional[int]									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        # Python no tensor
        with self.subTest('BatchEncoding (Python, return_tensors=None)'    ):
          self.assert_dump_and_restore(tokenizer_p('Small example to encode'    )    )

        with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.NUMPY    )   , np.array_equal    )

        with self.subTest('BatchEncoding (Rust, return_tensors=None)'    ):
          self.assert_dump_and_restore(tokenizer_r('Small example to encode'    )    )

        with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.NUMPY    )   , np.array_equal    )

      @require_tf
      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import tensorflow as tf

        def tf_array_equals(snake_case_   , snake_case_    ):
          return tf.reduce_all(tf.equal(snake_case_   , snake_case_    )    )

        A_    :			Dict									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			int									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.TENSORFLOW    )   , snake_case_    )

        with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.TENSORFLOW    )   , snake_case_    )

      @require_torch
      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import torch

        A_    :			Optional[int]									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			List[Any]									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.PYTORCH    )   , torch.equal    )

        with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.PYTORCH    )   , torch.equal    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			Any									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('Python Tokenizer'    ):
          self.assertFalse(tokenizer_p('Small example to_encode'    ).is_fast    )

        with self.subTest('Rust Tokenizer'    ):
          self.assertTrue(tokenizer_r('Small example to_encode'    ).is_fast    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )
        A_    :			Any									=    tokenizer_r(['Test', '\xad', 'test']   , is_split_into_words=snake_case_    )

        self.assertEqual(encoded.word_to_tokens(0    )   , TokenSpan(start=1   , end=2    )    )
        self.assertEqual(encoded.word_to_tokens(1    )   , snake_case_    )
        self.assertEqual(encoded.word_to_tokens(2    )   , TokenSpan(start=2   , end=3    )    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			str									=    batch.convert_to_tensors(tensor_type='np'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Any									=    batch.convert_to_tensors(tensor_type='np'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			List[Any]									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Dict									=    batch.convert_to_tensors(tensor_type='np'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_torch
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			Union[str, Any]									=    batch.convert_to_tensors(tensor_type='pt'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			str									=    batch.convert_to_tensors(tensor_type='pt'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			str									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Any									=    batch.convert_to_tensors(tensor_type='pt'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_tf
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			Dict									=    batch.convert_to_tensors(tensor_type='tf'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Optional[int]									=    batch.convert_to_tensors(tensor_type='tf'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			int									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Union[str, Any]									=    batch.convert_to_tensors(tensor_type='tf'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_flax
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			List[str]									=    batch.convert_to_tensors(tensor_type='jax'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Optional[Any]									=    batch.convert_to_tensors(tensor_type='jax'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			Union[str, Any]									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Optional[Any]									=    batch.convert_to_tensors(tensor_type='jax'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
        A_    :			List[str]									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , np.ndarray    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			Optional[Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='np'    )
        self.assertTrue(isinstance(batch['input_ids']   , np.ndarray    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_torch
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import torch

        A_    :			Tuple									=    [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
        A_    :			int									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Any									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , torch.Tensor    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			int									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='pt'    )
        self.assertTrue(isinstance(batch['input_ids']   , torch.Tensor    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_tf
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import tensorflow as tf

        A_    :			Dict									=    [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
        A_    :			Optional[int]									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , tf.Tensor    )    )
        self.assertEqual(batch['input_ids'].numpy().tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='tf'    )
        self.assertTrue(isinstance(batch['input_ids']   , tf.Tensor    )    )
        self.assertEqual(batch['input_ids'].numpy().tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    Tokenizer(WordPiece(unk_token='[UNK]'    )    )
        PreTrainedTokenizerFast(tokenizer_object=snake_case_    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    Tokenizer(WordPiece(unk_token='[UNK]'    )    )
        with tempfile.TemporaryDirectory() as tmpdirname:
          bert_tokenizer.save(os.path.join(snake_case_   , 'tokenizer.json'    )    )
          PreTrainedTokenizerFast(tokenizer_file=os.path.join(snake_case_   , 'tokenizer.json'    )    )






"""simple docstring"""




def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    (boundary[1] - boundary[0]) / steps
  A_    :			Optional[int]									=    boundary[0]
  A_    :			Optional[int]									=    boundary[1]
  A_    :			Any									=    make_points(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			Dict									=    0.0
  y += (h / 3.0) * f(_UpperCAmelCase   )
  A_    :			Optional[int]									=    2
  for i in x_i:
    y += (h / 3) * (4 - 2 * (cnt % 2)) * f(_UpperCAmelCase   )
    cnt += 1
  y += (h / 3.0) * f(_UpperCAmelCase   )
  return y





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    a + h
  while x < (b - h):
    yield x
    A_    :			Dict									=    x + h





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):  # enter your function here






  """simple docstring"""





  A_    :			List[Any]									=    (x - 0) * (x - 0)
  return y





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			int									=    0.0  # Lower bound of integration
  A_    :			int									=    1.0  # Upper bound of integration
  A_    :			Tuple									=    10.0  # define number of steps or resolution
  A_    :			List[str]									=    [a, b]  # define boundary of integration
  A_    :			Optional[Any]									=    method_a(_UpperCAmelCase					,   _UpperCAmelCase   )
  print(f"""y = {y}"""   )


if __name__ == "__main__":
   main()