






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

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

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


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

from test_module.custom_configuration import CustomConfig  # noqa E402


lowerCamelCase	:							str				={
    '''return_dict''': False,
    '''output_hidden_states''': True,
    '''output_attentions''': True,
    '''torchscript''': True,
    '''torch_dtype''': '''float16''',
    '''use_bfloat16''': True,
    '''tf_legacy_loss''': True,
    '''pruned_heads''': {'''a''': 1},
    '''tie_word_embeddings''': False,
    '''is_decoder''': True,
    '''cross_attention_hidden_size''': 128,
    '''add_cross_attention''': True,
    '''tie_encoder_decoder''': True,
    '''max_length''': 50,
    '''min_length''': 3,
    '''do_sample''': True,
    '''early_stopping''': True,
    '''num_beams''': 3,
    '''num_beam_groups''': 3,
    '''diversity_penalty''': 0.5,
    '''temperature''': 2.0,
    '''top_k''': 10,
    '''top_p''': 0.7,
    '''typical_p''': 0.2,
    '''repetition_penalty''': 0.8,
    '''length_penalty''': 0.8,
    '''no_repeat_ngram_size''': 5,
    '''encoder_no_repeat_ngram_size''': 5,
    '''bad_words_ids''': [1, 2, 3],
    '''num_return_sequences''': 3,
    '''chunk_size_feed_forward''': 5,
    '''output_scores''': True,
    '''return_dict_in_generate''': True,
    '''forced_bos_token_id''': 2,
    '''forced_eos_token_id''': 3,
    '''remove_invalid_values''': True,
    '''architectures''': ['''BertModel'''],
    '''finetuning_task''': '''translation''',
    '''id2label''': {0: '''label'''},
    '''label2id''': {'''label''': '''0'''},
    '''tokenizer_class''': '''BertTokenizerFast''',
    '''prefix''': '''prefix''',
    '''bos_token_id''': 6,
    '''pad_token_id''': 7,
    '''eos_token_id''': 8,
    '''sep_token_id''': 9,
    '''decoder_start_token_id''': 10,
    '''exponential_decay_length_penalty''': (5, 1.01),
    '''suppress_tokens''': [0, 1],
    '''begin_suppress_tokens''': 2,
    '''task_specific_params''': {'''translation''': '''some_params'''},
    '''problem_type''': '''regression''',
}





@is_staging_test
class 			__a  (    unittest.TestCase       ):







 @classmethod
 def 					__lowercase					(		cls					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  TOKEN
      HfFolder.save_token(SCREAMING_SNAKE_CASE						)







 @classmethod
 def 					__lowercase					(		cls					:   str						):


      '''simple docstring'''






      try:
           delete_repo(token=cls._token							,   repo_id="test-config"						)
      except HTTPError:
           pass

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

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







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  BertConfig(
          vocab_size=99							,   hidden_size=32							,   num_hidden_layers=5							,   num_attention_heads=4							,   intermediate_size=37						)
      config.push_to_hub("test-config"							,   use_auth_token=self._token						)

      UpperCamelCase__			:   Optional[int]       				=  BertConfig.from_pretrained(F'{USER}/test-config'						)
      for k, v in config.to_dict().items():
           if k != "transformers_version":
                self.assertEqual(SCREAMING_SNAKE_CASE							,   getattr(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)						)

        # Reset repo
      delete_repo(token=self._token							,   repo_id="test-config"						)

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

      UpperCamelCase__			:   List[Any]       				=  BertConfig.from_pretrained(F'{USER}/test-config'						)
      for k, v in config.to_dict().items():
           if k != "transformers_version":
                self.assertEqual(SCREAMING_SNAKE_CASE							,   getattr(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  BertConfig(
          vocab_size=99							,   hidden_size=32							,   num_hidden_layers=5							,   num_attention_heads=4							,   intermediate_size=37						)
      config.push_to_hub("valid_org/test-config-org"							,   use_auth_token=self._token						)

      UpperCamelCase__			:   int       				=  BertConfig.from_pretrained("valid_org/test-config-org"						)
      for k, v in config.to_dict().items():
           if k != "transformers_version":
                self.assertEqual(SCREAMING_SNAKE_CASE							,   getattr(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)						)

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

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

      UpperCamelCase__			:   str       				=  BertConfig.from_pretrained("valid_org/test-config-org"						)
      for k, v in config.to_dict().items():
           if k != "transformers_version":
                self.assertEqual(SCREAMING_SNAKE_CASE							,   getattr(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      CustomConfig.register_for_auto_class()
      UpperCamelCase__			:   Any       				=  CustomConfig(attribute=42						)

      config.push_to_hub("test-dynamic-config"							,   use_auth_token=self._token						)

      # This has added the proper auto_map field to the config
      self.assertDictEqual(config.auto_map							,   {"AutoConfig": "custom_configuration.CustomConfig"}						)

      UpperCamelCase__			:   Any       				=  AutoConfig.from_pretrained(F'{USER}/test-dynamic-config'							,   trust_remote_code=SCREAMING_SNAKE_CASE						)
      # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
      self.assertEqual(new_config.__class__.__name__							,   "CustomConfig"						)
      self.assertEqual(new_config.attribute							,   42						)







class 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  GPTaConfig()

      # attempt to modify each of int/float/bool/str config records and verify they were updated
      UpperCamelCase__			:   Any       				=  c.n_embd + 1  # int
      UpperCamelCase__			:   Optional[int]       				=  c.resid_pdrop + 1.0  # float
      UpperCamelCase__			:   Tuple       				=  not c.scale_attn_weights  # bool
      UpperCamelCase__			:   int       				=  c.summary_type + "foo"  # str
      c.update_from_string(
          F'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'						)
      self.assertEqual(SCREAMING_SNAKE_CASE							,   c.n_embd							,   "mismatch for key: n_embd"						)
      self.assertEqual(SCREAMING_SNAKE_CASE							,   c.resid_pdrop							,   "mismatch for key: resid_pdrop"						)
      self.assertEqual(SCREAMING_SNAKE_CASE							,   c.scale_attn_weights							,   "mismatch for key: scale_attn_weights"						)
      self.assertEqual(SCREAMING_SNAKE_CASE							,   c.summary_type							,   "mismatch for key: summary_type"						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  PretrainedConfig()
      UpperCamelCase__			:   List[Any]       				=  [key for key in base_config.__dict__ if key not in config_common_kwargs]
      # If this part of the test fails, you have arguments to addin config_common_kwargs above.
      self.assertListEqual(
          SCREAMING_SNAKE_CASE							,   ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]						)
      UpperCamelCase__			:   Dict       				=  [key for key, value in config_common_kwargs.items() if value == getattr(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)]
      if len(SCREAMING_SNAKE_CASE						) > 0:
           raise ValueError(
               "The following keys are set with the default values in"
               " `test_configuration_common.config_common_kwargs` pick another value for them:"
               F' {", ".join(SCREAMING_SNAKE_CASE						)}.'						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      with self.assertRaises(SCREAMING_SNAKE_CASE						):
           # config is in subfolder, the following should not work without specifying the subfolder
           UpperCamelCase__			:   str       				=  BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"						)

      UpperCamelCase__			:   Union[str, Any]       				=  BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"							,   subfolder="bert"						)

      self.assertIsNotNone(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  mock.Mock()
      UpperCamelCase__			:   List[Any]       				=  5_00
      UpperCamelCase__			:   List[Any]       				=  {}
      UpperCamelCase__			:   Optional[Any]       				=  HTTPError
      UpperCamelCase__			:   Optional[Any]       				=  {}

      # Download this model to make sure it's in the cache.
      UpperCamelCase__			:   int       				=  BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"						)

      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch("requests.Session.request"							,   return_value=SCREAMING_SNAKE_CASE						) as mock_head:
           UpperCamelCase__			:   Optional[Any]       				=  BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"						)
           # This check we did call the fake head request
           mock_head.assert_called()







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  BertConfig.from_pretrained(
          "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"						)







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  AutoConfig.from_pretrained("bert-base-cased"						)
      UpperCamelCase__			:   Dict       				=  ["config.4.0.0.json"]

      with tempfile.TemporaryDirectory() as tmp_dir:
           configuration.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Union[str, Any]       				=  2
           json.dump(configuration.to_dict()							,   open(os.path.join(SCREAMING_SNAKE_CASE							,   "config.4.0.0.json"						)							,   "w"						)						)

           # This should pick the new configuration file as the version of Transformers is > 4.0.0
           UpperCamelCase__			:   List[Any]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertEqual(new_configuration.hidden_size							,   2						)

           # Will need to be adjusted if we reach v42 and this test is still here.
           # Should pick the old configuration file as the version of Transformers is < 4.42.0
           UpperCamelCase__			:   str       				=  ["config.42.0.0.json"]
           UpperCamelCase__			:   Optional[int]       				=  7_68
           configuration.save_pretrained(SCREAMING_SNAKE_CASE						)
           shutil.move(os.path.join(SCREAMING_SNAKE_CASE							,   "config.4.0.0.json"						)							,   os.path.join(SCREAMING_SNAKE_CASE							,   "config.42.0.0.json"						)						)
           UpperCamelCase__			:   Optional[Any]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertEqual(new_configuration.hidden_size							,   7_68						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  "hf-internal-testing/test-two-configs"

      import transformers as new_transformers

      UpperCamelCase__			:   Any       				=  "v4.0.0"
      UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  new_transformers.models.auto.AutoConfig.from_pretrained(
          SCREAMING_SNAKE_CASE							,   return_unused_kwargs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(new_configuration.hidden_size							,   2						)
      # This checks `_configuration_file` ia not kept in the kwargs by mistake.
      self.assertDictEqual(SCREAMING_SNAKE_CASE							,   {}						)

      # Testing an older version by monkey-patching the version in the module it's used.
      import transformers as old_transformers

      UpperCamelCase__			:   int       				=  "v3.0.0"
      UpperCamelCase__			:   Optional[int]       				=  old_transformers.models.auto.AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
      self.assertEqual(old_configuration.hidden_size							,   7_68						)







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 			__a  (    A__       ):
 _lowerCAmelCase       :       str        = '''facebook/bart-large-mnli'''
 _lowerCAmelCase       :       Tuple        = (
     '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
     '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
     '''It returns the most likely label in the list of provided `labels` for the input text.'''
 )
 _lowerCAmelCase       :       Any        = '''text_classifier'''
 _lowerCAmelCase       :       int        = AutoTokenizer
 _lowerCAmelCase       :       Union[str, Any]        = AutoModelForSequenceClassification

 _lowerCAmelCase       :       Union[str, Any]        = ['''text''', ['''text''']]
 _lowerCAmelCase       :       Dict        = ['''text''']







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      super().setup()
      UpperCamelCase__			:   Dict       				=  self.model.config
      UpperCamelCase__			:   Union[str, Any]       				=  -1
      for idx, label in config.idalabel.items():
           if label.lower().startswith("entail"						):
                UpperCamelCase__			:   List[str]       				=  int(SCREAMING_SNAKE_CASE						)
      if self.entailment_id == -1:
           raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."						)







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  labels
      return self.pre_processor(
          [text] * len(SCREAMING_SNAKE_CASE						)							,   [F'This example is {label}' for label in labels]							,   return_tensors="pt"							,   padding="max_length"							,   )







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  outputs.logits
      UpperCamelCase__			:   Any       				=  torch.argmax(logits[:, 2]						).item()
      return self._labels[label_id]