


'''simple docstring'''







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
   import tensorflow as tf

if is_tensorflow_text_available():
   from transformers.models.bert import TFBertTokenizer


A    						=			['''bert-base-uncased''', '''bert-base-cased''']
A    						=			'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():


   class 			__lowerCamelCase			(	tf.keras.Model	):
       """simple docstring"""


       def __init__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
              super().__init__()
              _A					:     Any     = tokenizer
              _A					:     List[Any]     = AutoConfig.from_pretrained(lowercase_)
              _A					:     int     = TFAutoModel.from_config(lowercase_)


       def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[str]):
              _A					:     str     = self.tokenizer(lowercase_)
              _A					:     str     = self.bert(**lowercase_)
              return out["pooler_output"]


@require_tf
@require_tensorflow_text
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							Optional[int]):
           super().setUp()

           _A					:     Any     = [
               BertTokenizer.from_pretrained(lowercase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
           ]  # repeat for when fast_bert_tokenizer=false
           _A					:     str     = [TFBertTokenizer.from_pretrained(lowercase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
               TFBertTokenizer.from_pretrained(lowercase_				,							use_fast_bert_tokenizer=lowercase_)
               for checkpoint in TOKENIZER_CHECKPOINTS
           ]
           assert len(self.tokenizers) == len(self.tf_tokenizers)

           _A					:     Optional[Any]     = [
               "This is a straightforward English test sentence.",
               "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
               "Now we're going to add some Chinese: 一 二 三 一二三",
               "And some much more rare Chinese: 齉 堃 齉堃",
               "Je vais aussi écrire en français pour tester les accents",
               "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
           ]
           _A					:     List[Any]     = list(zip(self.test_sentences				,							self.test_sentences[::-1]))


    def        A   (  self      :							List[str]):
           for tokenizer, tf_tokenizer in zip(self.tokenizers				,							self.tf_tokenizers):
                  for test_inputs in (self.test_sentences, self.paired_sentences):
                         _A					:     str     = tokenizer(lowercase_				,							return_tensors='tf'				,							padding='longest')
                         _A					:     Tuple     = tf_tokenizer(lowercase_)

                         for key in python_outputs.keys():
                                self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                                self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]				,							tf.intaa) == tf_outputs[key]))


    @slow
    def        A   (  self      :							Tuple):
           for tf_tokenizer in self.tf_tokenizers:
                  _A					:     Optional[int]     = tf_tokenizer(self.paired_sentences)
                  _A					:     Any     = tf_tokenizer(
                      text=[sentence[0] for sentence in self.paired_sentences]				,							text_pair=[sentence[1] for sentence in self.paired_sentences]				,							)
                  for key in merged_outputs.keys():
                         self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]				,							tf.intaa) == separated_outputs[key]))


    @slow
    def        A   (  self      :							Tuple):
           for tf_tokenizer in self.tf_tokenizers:
                  _A					:     int     = tf.function(lowercase_)
                  for test_inputs in (self.test_sentences, self.paired_sentences):
                         _A					:     str     = tf.constant(lowercase_)
                         _A					:     Any     = compiled_tokenizer(lowercase_)
                         _A					:     Tuple     = tf_tokenizer(lowercase_)

                         for key in eager_outputs.keys():
                                self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))


    @slow
    def        A   (  self      :							List[str]):
           for tf_tokenizer in self.tf_tokenizers:
                  _A					:     List[Any]     = ModelToSave(tokenizer=lowercase_)
                  _A					:     Union[str, Any]     = tf.convert_to_tensor(self.test_sentences)
                  _A					:     str     = model(lowercase_)  # Build model with some sample inputs
                  with TemporaryDirectory() as tempdir:
                         _A					:     Optional[int]     = Path(lowercase_) / "saved.model"
                         model.save(lowercase_)
                         _A					:     str     = tf.keras.models.load_model(lowercase_)
                  _A					:     Dict     = loaded_model(lowercase_)
                  # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
                  self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output))				,							1e-5)










'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


A				:					Tuple    						=			logging.get_logger(__name__)

A				:					Tuple    						=			{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "longformer"


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[List[int], int] = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 30522				,							SCREAMING_SNAKE_CASE      :							int = 768				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 3072				,							SCREAMING_SNAKE_CASE      :							str = "gelu"				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							float = 0.02				,							SCREAMING_SNAKE_CASE      :							float = 1e-12				,							SCREAMING_SNAKE_CASE      :							bool = False				,							**SCREAMING_SNAKE_CASE      :							List[Any]				,							):
           super().__init__(pad_token_id=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = attention_window
           _A					:     int     = sep_token_id
           _A					:     Tuple     = bos_token_id
           _A					:     Any     = eos_token_id
           _A					:     List[str]     = vocab_size
           _A					:     Any     = hidden_size
           _A					:     Optional[int]     = num_hidden_layers
           _A					:     int     = num_attention_heads
           _A					:     Dict     = hidden_act
           _A					:     List[Any]     = intermediate_size
           _A					:     int     = hidden_dropout_prob
           _A					:     Optional[int]     = attention_probs_dropout_prob
           _A					:     int     = max_position_embeddings
           _A					:     Any     = type_vocab_size
           _A					:     Dict     = initializer_range
           _A					:     Any     = layer_norm_eps
           _A					:     List[Any]     = onnx_export




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							"PretrainedConfig"				,							SCREAMING_SNAKE_CASE      :							str = "default"				,							SCREAMING_SNAKE_CASE      :							"List[PatchingSpec]" = None):
           super().__init__(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = True


    @property
    def        A   (  self      :							List[str]):
           if self.task == "multiple-choice":
                  _A					:     Tuple     = {0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                  _A					:     List[Any]     = {0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
                   ('global_attention_mask', dynamic_axis),
               ])


    @property
    def        A   (  self      :							str):
           _A					:     int     = super().outputs
           if self.task == "default":
                  _A					:     str     = {0: 'batch'}
           return outputs


    @property
    def        A   (  self      :							List[Any]):
           return 1e-4


    @property
    def        A   (  self      :							Dict):
           # needs to be >= 14 to support tril operator
           return max(super().default_onnx_opset				,							14)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							"PreTrainedTokenizerBase"				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							Optional[TensorType] = None				,							):
           _A					:     Union[str, Any]     = super().generate_dummy_inputs(
               preprocessor=SCREAMING_SNAKE_CASE				,							batch_size=SCREAMING_SNAKE_CASE				,							seq_length=SCREAMING_SNAKE_CASE				,							is_pair=SCREAMING_SNAKE_CASE				,							framework=SCREAMING_SNAKE_CASE)
           import torch

           # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
           # makes the export fail randomly
           _A					:     Tuple     = torch.zeros_like(inputs['input_ids'])
           # make every second token global
           _A					:     Dict     = 1

           return inputs






