





'''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      SCREAMING_SNAKE_CASE__      (    unittest.TestCase				):




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	list(tokenizer_class.max_model_input_sizes.keys()      )
          for model_name in sa_models[:1]:
                SCREAMING_SNAKE_CASE_				:      Optional[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_      )
                      SCREAMING_SNAKE_CASE_				:      str					=	tokenizer.convert_tokens_to_ids(snake_case_      )
                      self.assertIsInstance(snake_case_    ,					snake_case_      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	pickle.dumps(snake_case_      )
          self.assertIsNotNone(snake_case_      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	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

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	BertTokenizer.from_pretrained("bert-base-cased"      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	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(lowercase__    ,					lowercase__      ):
                return tf.reduce_all(tf.equal(snake_case_    ,					snake_case_      )      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	BertTokenizer.from_pretrained("bert-base-cased"      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	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

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	BertTokenizer.from_pretrained("bert-base-cased"      )
          SCREAMING_SNAKE_CASE_				:      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"""






          SCREAMING_SNAKE_CASE_				:      Any					=	BertTokenizer.from_pretrained("bert-base-cased"      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	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"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	BertTokenizerFast.from_pretrained("bert-base-cased"      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	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"""






          SCREAMING_SNAKE_CASE_				:      Any					=	BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}      )
          SCREAMING_SNAKE_CASE_				:      List[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:
                SCREAMING_SNAKE_CASE_				:      Any					=	batch.convert_to_tensors(tensor_type="np"      )
          self.assertFalse(len(cs.err      )    ,					msg=F"should have no warning, but got {cs.err}"      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	BatchEncoding({"inputs": [1, 2, 3], "labels": 0}      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	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"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	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:
                SCREAMING_SNAKE_CASE_				:      int					=	batch.convert_to_tensors(tensor_type="pt"      )
          self.assertFalse(len(cs.err      )    ,					msg=F"should have no warning, but got {cs.err}"      )

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	BatchEncoding({"inputs": [1, 2, 3], "labels": 0}      )
          SCREAMING_SNAKE_CASE_				:      int					=	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"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	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:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	batch.convert_to_tensors(tensor_type="tf"      )
          self.assertFalse(len(cs.err      )    ,					msg=F"should have no warning, but got {cs.err}"      )

          SCREAMING_SNAKE_CASE_				:      int					=	BatchEncoding({"inputs": [1, 2, 3], "labels": 0}      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	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"""






          SCREAMING_SNAKE_CASE_				:      str					=	BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}      )
          SCREAMING_SNAKE_CASE_				:      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:
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	batch.convert_to_tensors(tensor_type="jax"      )
          self.assertFalse(len(cs.err      )    ,					msg=F"should have no warning, but got {cs.err}"      )

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	BatchEncoding({"inputs": [1, 2, 3], "labels": 0}      )
          SCREAMING_SNAKE_CASE_				:      Union[str, 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"""






          SCREAMING_SNAKE_CASE_				:      int					=	[{"input_ids": np.array([0, 1, 2]      )}, {"input_ids": np.array([0, 1, 2, 3]      )}]
          SCREAMING_SNAKE_CASE_				:      Any					=	BertTokenizer.from_pretrained("bert-base-cased"      )

          SCREAMING_SNAKE_CASE_				:      List[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]]      )
          SCREAMING_SNAKE_CASE_				:      Union[str, 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

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[{"input_ids": torch.tensor([0, 1, 2]      )}, {"input_ids": torch.tensor([0, 1, 2, 3]      )}]
          SCREAMING_SNAKE_CASE_				:      str					=	BertTokenizer.from_pretrained("bert-base-cased"      )

          SCREAMING_SNAKE_CASE_				:      Dict					=	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]]      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	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

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[{"input_ids": tf.constant([0, 1, 2]      )}, {"input_ids": tf.constant([0, 1, 2, 3]      )}]
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	BertTokenizer.from_pretrained("bert-base-cased"      )

          SCREAMING_SNAKE_CASE_				:      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]]      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	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"""






          SCREAMING_SNAKE_CASE_				:      Any					=	Tokenizer(WordPiece(unk_token="[UNK]"      )      )
          PreTrainedTokenizerFast(tokenizer_object=snake_case_      )







    @require_tokenizers
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	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  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[int]  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       List[str]:
      """simple docstring"""

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


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]  ,  SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	a + h
      while x < (b - h):
            yield x
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	x + h


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str   )							->       List[Any]:  # enter your function here
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(x - 0) * (x - 0)
      return y


def  __lowerCamelCase      (				)							->       int:
      """simple docstring"""

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


if __name__ == "__main__":
      main()

