


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


_snake_case	:						Union[str, Any]         =							["bert-base-uncased", "bert-base-cased"]
_snake_case	:						Dict         =							"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():

			class 							a						(tf.keras.Model ):




										"""simple docstring"""


										def __init__(				self			:    List[Any]       ,      lowerCamelCase			:    List[Any]							)      ->       Dict:
																	super().__init__()
																	__snake_case						:     List[Any]									=  tokenizer
																	__snake_case						:     Dict									=  AutoConfig.from_pretrained(lowerCamelCase							)
																	__snake_case						:     Dict									=  TFAutoModel.from_config(lowerCamelCase							)






										def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    Optional[Any]							)      ->       Optional[int]:
																	__snake_case						:     int									=  self.tokenizer(lowerCamelCase							)
																	__snake_case						:     Dict									=  self.bert(**lowerCamelCase							)
																	return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Optional[Any]:
														super().setUp()

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

														__snake_case						:     Optional[int]									=  [
														    "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",
														]
														__snake_case						:     Tuple									=  list(zip(self.test_sentences       ,      self.test_sentences[::-1]							)							)

							def        __snake_case   (				self			:    Tuple							)      ->       str:
														for tokenizer, tf_tokenizer in zip(self.tokenizers       ,      self.tf_tokenizers							):
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												__snake_case						:     Optional[Any]									=  tokenizer(lowerCamelCase       ,      return_tensors="tf"       ,      padding="longest"							)
																												__snake_case						:     int									=  tf_tokenizer(lowerCamelCase							)

																												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        __snake_case   (				self			:    Any							)      ->       Dict:
														for tf_tokenizer in self.tf_tokenizers:
																					__snake_case						:     List[Any]									=  tf_tokenizer(self.paired_sentences							)
																					__snake_case						:     List[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        __snake_case   (				self			:    Dict							)      ->       Optional[int]:
														for tf_tokenizer in self.tf_tokenizers:
																					__snake_case						:     Optional[int]									=  tf.function(lowerCamelCase							)
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												__snake_case						:     Tuple									=  tf.constant(lowerCamelCase							)
																												__snake_case						:     int									=  compiled_tokenizer(lowerCamelCase							)
																												__snake_case						:     str									=  tf_tokenizer(lowerCamelCase							)

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






							@slow
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       str:
														for tf_tokenizer in self.tf_tokenizers:
																					__snake_case						:     Optional[int]									=  ModelToSave(tokenizer=lowerCamelCase							)
																					__snake_case						:     Any									=  tf.convert_to_tensor(self.test_sentences							)
																					__snake_case						:     Any									=  model(lowerCamelCase							)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												__snake_case						:     Optional[Any]									=  Path(lowerCamelCase							) / "saved.model"
																												model.save(lowerCamelCase							)
																												__snake_case						:     Tuple									=  tf.keras.models.load_model(lowerCamelCase							)
																					__snake_case						:     Optional[Any]									=  loaded_model(lowerCamelCase							)
																					# 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							)









import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:str="sayef/fsner-bert-base-uncased"):



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

          return self.softmax(T * self.cos(snake_case,      snake_case))





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

          _lowercase              =W_supports['sizes'].tolist()
          _lowercase              =W_supports['start_token_id'].item()
          _lowercase              =W_supports['end_token_id'].item()

          del W_supports["sizes"]
          del W_supports["start_token_id"]
          del W_supports["end_token_id"]

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

          _lowercase              =W_supports['input_ids'] == start_token_id
          _lowercase              =W_supports['input_ids'] == end_token_id

          for i, size in enumerate(snake_case):
             if i == 0:
                _lowercase              =0
             else:
                _lowercase              =support_sizes[i - 1]

             _lowercase              =S[s : s + size][start_token_masks[s : s + size]]
             _lowercase              =S[s : s + size][end_token_masks[s : s + size]]

             _lowercase              =torch.matmul(q[i],      s_start.T).sum(1).softmax(0)
             _lowercase              =torch.matmul(q[i],      s_end.T).sum(1).softmax(0)

             if p_starts is not None:
                _lowercase              =torch.vstack((p_starts, p_start))
                _lowercase              =torch.vstack((p_ends, p_end))
             else:
                _lowercase              =p_start
                _lowercase              =p_end

          return p_starts, p_ends


