
"""simple docstring"""

import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
      from test_module.custom_tokenization_fast import CustomTokenizerFast
class      _UpperCAmelCase							(		unittest.TestCase							):




      def     a_      (							self							)						->					Optional[Any]:
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase									=					mock.Mock()
          UpperCAmelCase									=					5_0_0
          UpperCAmelCase									=					{}
          UpperCAmelCase									=					HTTPError
          UpperCAmelCase									=					{}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase									=					BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'							)

          # Under the mock environment we get a 500 error when trying to reach the tokenizer.
          with mock.patch('requests.Session.request'   ,       return_value=lowercase_							) as mock_head:
              UpperCAmelCase									=					BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'							)
              # This check we did call the fake head request
              mock_head.assert_called()




      @require_tokenizers
      def     a_      (							self							)						->					Optional[int]:
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase									=					mock.Mock()
          UpperCAmelCase									=					5_0_0
          UpperCAmelCase									=					{}
          UpperCAmelCase									=					HTTPError
          UpperCAmelCase									=					{}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase									=					GPTaTokenizerFast.from_pretrained('gpt2'							)

          # Under the mock environment we get a 500 error when trying to reach the tokenizer.
          with mock.patch('requests.Session.request'   ,       return_value=lowercase_							) as mock_head:
              UpperCAmelCase									=					GPTaTokenizerFast.from_pretrained('gpt2'							)
              # This check we did call the fake head request
              mock_head.assert_called()




      def     a_      (							self							)						->					int:
          # This test is for deprecated behavior and can be removed in v5
          try:
              UpperCAmelCase									=					tempfile.mktemp()
              with open(lowercase_   ,       'wb'							) as f:
                  http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'   ,       lowercase_							)

              UpperCAmelCase									=					AlbertTokenizer.from_pretrained(lowercase_							)
          finally:
              os.remove(lowercase_							)

          # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
          # the current folder and have the right name.
          if os.path.isfile('tokenizer.json'							):
              # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
              return
          try:
              with open('tokenizer.json'   ,       'wb'							) as f:
                  http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'   ,       lowercase_							)
              UpperCAmelCase									=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
              # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
              self.assertEqual(tokenizer.vocab_size   ,       1_0_0_0							)
              # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

          finally:
              os.remove('tokenizer.json'							)




      def     a_      (							self							)						->					str:
          # This test is for deprecated behavior and can be removed in v5
          UpperCAmelCase									=					AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'							)

@is_staging_test
class      _UpperCAmelCase							(		unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							str							      =	["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]




      @classmethod
      def     a_      (							cls							)						->					List[str]:
          UpperCAmelCase									=					TOKEN
          HfFolder.save_token(lowercase_							)




      @classmethod
      def     a_      (							cls							)						->					Union[str, Any]:
          try:
              delete_repo(token=cls._token   ,       repo_id='test-tokenizer'							)
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token   ,       repo_id='valid_org/test-tokenizer-org'							)
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token   ,       repo_id='test-dynamic-tokenizer'							)
          except HTTPError:
              pass




      def     a_      (							self							)						->					Any:
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase									=					os.path.join(lowercase_   ,       'vocab.txt'							)
              with open(lowercase_   ,       'w'   ,       encoding='utf-8'							) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)
              UpperCAmelCase									=					BertTokenizer(lowercase_							)

          tokenizer.push_to_hub('test-tokenizer'   ,       use_auth_token=self._token							)
          UpperCAmelCase									=					BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"							)
          self.assertDictEqual(new_tokenizer.vocab   ,       tokenizer.vocab							)

          # Reset repo
          delete_repo(token=self._token   ,       repo_id='test-tokenizer'							)

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(lowercase_   ,       repo_id='test-tokenizer'   ,       push_to_hub=lowercase_   ,       use_auth_token=self._token							)

          UpperCAmelCase									=					BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"							)
          self.assertDictEqual(new_tokenizer.vocab   ,       tokenizer.vocab							)




      def     a_      (							self							)						->					Optional[int]:
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase									=					os.path.join(lowercase_   ,       'vocab.txt'							)
              with open(lowercase_   ,       'w'   ,       encoding='utf-8'							) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)
              UpperCAmelCase									=					BertTokenizer(lowercase_							)

          tokenizer.push_to_hub('valid_org/test-tokenizer-org'   ,       use_auth_token=self._token							)
          UpperCAmelCase									=					BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'							)
          self.assertDictEqual(new_tokenizer.vocab   ,       tokenizer.vocab							)

          # Reset repo
          delete_repo(token=self._token   ,       repo_id='valid_org/test-tokenizer-org'							)

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(
                  lowercase_   ,       repo_id='valid_org/test-tokenizer-org'   ,       push_to_hub=lowercase_   ,       use_auth_token=self._token							)

          UpperCAmelCase									=					BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'							)
          self.assertDictEqual(new_tokenizer.vocab   ,       tokenizer.vocab							)




      @require_tokenizers
      def     a_      (							self							)						->					Union[str, Any]:
          CustomTokenizer.register_for_auto_class()
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase									=					os.path.join(lowercase_   ,       'vocab.txt'							)
              with open(lowercase_   ,       'w'   ,       encoding='utf-8'							) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)
              UpperCAmelCase									=					CustomTokenizer(lowercase_							)

          # No fast custom tokenizer
          tokenizer.push_to_hub('test-dynamic-tokenizer'   ,       use_auth_token=self._token							)

          UpperCAmelCase									=					AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer"   ,       trust_remote_code=lowercase_							)
          # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__   ,       'CustomTokenizer'							)

          # Fast and slow custom tokenizer
          CustomTokenizerFast.register_for_auto_class()
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase									=					os.path.join(lowercase_   ,       'vocab.txt'							)
              with open(lowercase_   ,       'w'   ,       encoding='utf-8'							) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)

              UpperCAmelCase									=					BertTokenizerFast.from_pretrained(lowercase_							)
              bert_tokenizer.save_pretrained(lowercase_							)
              UpperCAmelCase									=					CustomTokenizerFast.from_pretrained(lowercase_							)

          tokenizer.push_to_hub('test-dynamic-tokenizer'   ,       use_auth_token=self._token							)

          UpperCAmelCase									=					AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer"   ,       trust_remote_code=lowercase_							)
          # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__   ,       'CustomTokenizerFast'							)
          UpperCAmelCase									=					AutoTokenizer.from_pretrained(
              F"{USER}/test-dynamic-tokenizer"   ,       use_fast=lowercase_   ,       trust_remote_code=lowercase_							)
          # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__   ,       'CustomTokenizer'							)

class      _UpperCAmelCase							(		unittest.TestCase							):




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					Trie()
          trie.add('Hello 友達'							)
          self.assertEqual(trie.data   ,       {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}							)
          trie.add('Hello'							)
          trie.data
          self.assertEqual(trie.data   ,       {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}							)




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					Trie()
          self.assertEqual(trie.split('[CLS] This is a extra_id_100'							)   ,       ['[CLS] This is a extra_id_100']							)
          trie.add('[CLS]'							)
          trie.add('extra_id_1'							)
          trie.add('extra_id_100'							)
          self.assertEqual(trie.split('[CLS] This is a extra_id_100'							)   ,       ['[CLS]', ' This is a ', 'extra_id_100']							)




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					Trie()
          trie.add('A'							)
          self.assertEqual(trie.split('ABC'							)   ,       ['A', 'BC']							)
          self.assertEqual(trie.split('BCA'							)   ,       ['BC', 'A']							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					Trie()
          trie.add('TOKEN]'							)
          trie.add('[SPECIAL_TOKEN]'							)
          self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'							)   ,       ['This is something ', '[SPECIAL_TOKEN]']							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					Trie()
          trie.add('A'							)
          trie.add('P'							)
          trie.add('[SPECIAL_TOKEN]'							)
          self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'							)   ,       ['This is something ', '[SPECIAL_TOKEN]']							)




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					Trie()
          trie.add('AB'							)
          trie.add('B'							)
          trie.add('C'							)
          self.assertEqual(trie.split('ABC'							)   ,       ['AB', 'C']							)




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					Trie()
          trie.add('ABC'							)
          trie.add('B'							)
          trie.add('CD'							)
          self.assertEqual(trie.split('ABCD'							)   ,       ['ABC', 'D']							)




      def     a_      (							self							)						->					Optional[Any]:
          # Even if the offsets are wrong, we necessarily output correct string
          # parts.
          UpperCAmelCase									=					Trie()
          UpperCAmelCase									=					trie.cut_text('ABC'   ,       [0, 0, 2, 1, 2, 3]							)
          self.assertEqual(lowercase_   ,       ['AB', 'C']							)





"""simple docstring"""

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=9_9   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=5_1_2   ,       lowercase_=1_6   ,       lowercase_=2   ,       lowercase_=0.0_2   ,       lowercase_=4   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					seq_length
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_attention_mask
          UpperCAmelCase									=					use_token_type_ids
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					vocab_size
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					intermediate_size
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					hidden_dropout_prob
          UpperCAmelCase									=					attention_probs_dropout_prob
          UpperCAmelCase									=					max_position_embeddings
          UpperCAmelCase									=					type_vocab_size
          UpperCAmelCase									=					type_sequence_label_size
          UpperCAmelCase									=					initializer_range
          UpperCAmelCase									=					num_choices




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size							)

          UpperCAmelCase									=					None
          if self.use_attention_mask:
              UpperCAmelCase									=					random_attention_mask([self.batch_size, self.seq_length]							)

          UpperCAmelCase									=					None
          if self.use_token_type_ids:
              UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size							)

          UpperCAmelCase									=					RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       is_decoder=lowercase_   ,       initializer_range=self.initializer_range   ,       )

          return config, input_ids, token_type_ids, attention_mask




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs

          UpperCAmelCase									=					True
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       vocab_size=2							)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	True

      __SCREAMING_SNAKE_CASE   :							int							      =	(
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModelTester(self							)




      @slow
      def     a_      (							self							)						->					Dict:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase									=					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)
              UpperCAmelCase									=					model(np.ones((1, 1)							)							)
              self.assertIsNotNone(lowercase_							)

@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @slow
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          UpperCAmelCase									=					[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape							)   ,       lowercase_							)
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)




      @slow
      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)



