






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFRoFormerForCausalLM,
     TFRoFormerForMaskedLM,
     TFRoFormerForMultipleChoice,
     TFRoFormerForQuestionAnswering,
     TFRoFormerForSequenceClassification,
     TFRoFormerForTokenClassification,
     TFRoFormerModel,
 )
 from transformers.models.roformer.modeling_tf_roformer import (
     TFRoFormerSelfAttention,
     TFRoFormerSinusoidalPositionalEmbedding,
 )


class     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=7		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=99		,		_UpperCAmelCase=32		,		_UpperCAmelCase=2		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=16		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=4		,		_UpperCAmelCase=None		,		)      ->			Union[str, Any]:
     UpperCamelCase_						=  parent
     UpperCamelCase_						=  13
     UpperCamelCase_						=  7
     UpperCamelCase_						=  True
     UpperCamelCase_						=  True
     UpperCamelCase_						=  True
     UpperCamelCase_						=  True
     UpperCamelCase_						=  99
     UpperCamelCase_						=  32
     UpperCamelCase_						=  2
     UpperCamelCase_						=  4
     UpperCamelCase_						=  37
     UpperCamelCase_						=  """gelu"""
     UpperCamelCase_						=  0.1
     UpperCamelCase_						=  0.1
     UpperCamelCase_						=  512
     UpperCamelCase_						=  16
     UpperCamelCase_						=  2
     UpperCamelCase_						=  0.0_2
     UpperCamelCase_						=  3
     UpperCamelCase_						=  4
     UpperCamelCase_						=  None




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size			)

     UpperCamelCase_						=  None
     if self.use_input_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_						=  None
     UpperCamelCase_						=  None
     UpperCamelCase_						=  None
     if self.use_labels:
        UpperCamelCase_						=  ids_tensor([self.batch_size]		,		self.type_sequence_label_size			)
        UpperCamelCase_						=  ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels			)
        UpperCamelCase_						=  ids_tensor([self.batch_size]		,		self.num_choices			)

     UpperCamelCase_						=  RoFormerConfig(
         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		,		initializer_range=self.initializer_range		,		return_dict=lowerCamelCase__		,		)

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  TFRoFormerModel(config=lowerCamelCase__			)
     UpperCamelCase_						=  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

     UpperCamelCase_						=  [input_ids, input_mask]
     UpperCamelCase_						=  model(lowerCamelCase__			)

     UpperCamelCase_						=  model(lowerCamelCase__			)

     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[Any]:
     UpperCamelCase_						=  True
     UpperCamelCase_						=  TFRoFormerForCausalLM(config=lowerCamelCase__			)
     UpperCamelCase_						=  {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }
     UpperCamelCase_						=  model(lowerCamelCase__			)["""logits"""]
     self.parent.assertListEqual(
         list(prediction_scores.numpy().shape			)		,		[self.batch_size, self.seq_length, self.vocab_size]			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  TFRoFormerForMaskedLM(config=lowerCamelCase__			)
     UpperCamelCase_						=  {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }
     UpperCamelCase_						=  model(lowerCamelCase__			)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     UpperCamelCase_						=  self.num_labels
     UpperCamelCase_						=  TFRoFormerForSequenceClassification(config=lowerCamelCase__			)
     UpperCamelCase_						=  {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }

     UpperCamelCase_						=  model(lowerCamelCase__			)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  self.num_choices
     UpperCamelCase_						=  TFRoFormerForMultipleChoice(config=lowerCamelCase__			)
     UpperCamelCase_						=  tf.tile(tf.expand_dims(lowerCamelCase__		,		1			)		,		(1, self.num_choices, 1)			)
     UpperCamelCase_						=  tf.tile(tf.expand_dims(lowerCamelCase__		,		1			)		,		(1, self.num_choices, 1)			)
     UpperCamelCase_						=  tf.tile(tf.expand_dims(lowerCamelCase__		,		1			)		,		(1, self.num_choices, 1)			)
     UpperCamelCase_						=  {
         """input_ids""": multiple_choice_inputs_ids,
         """attention_mask""": multiple_choice_input_mask,
         """token_type_ids""": multiple_choice_token_type_ids,
     }
     UpperCamelCase_						=  model(lowerCamelCase__			)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_choices)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     UpperCamelCase_						=  self.num_labels
     UpperCamelCase_						=  TFRoFormerForTokenClassification(config=lowerCamelCase__			)
     UpperCamelCase_						=  {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }
     UpperCamelCase_						=  model(lowerCamelCase__			)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			str:
     UpperCamelCase_						=  TFRoFormerForQuestionAnswering(config=lowerCamelCase__			)
     UpperCamelCase_						=  {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }

     UpperCamelCase_						=  model(lowerCamelCase__			)
     self.parent.assertEqual(result.start_logits.shape		,		(self.batch_size, self.seq_length)			)
     self.parent.assertEqual(result.end_logits.shape		,		(self.batch_size, self.seq_length)			)




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  self.prepare_config_and_inputs()
     (
         UpperCamelCase_       
     )						=  config_and_inputs
     UpperCamelCase_						=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
     return config, inputs_dict




@require_tf
class     _a							(	__UpperCAmelCase    ,							__UpperCAmelCase    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = (
      (
          TFRoFormerModel,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  A_						       = (
      {
          '''feature-extraction''': TFRoFormerModel,
          '''fill-mask''': TFRoFormerForMaskedLM,
          '''question-answering''': TFRoFormerForQuestionAnswering,
          '''text-classification''': TFRoFormerForSequenceClassification,
          '''text-generation''': TFRoFormerForCausalLM,
          '''token-classification''': TFRoFormerForTokenClassification,
          '''zero-shot''': TFRoFormerForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  A_						       = False
  A_						       = False




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			str:
     if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

     return False




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  TFRoFormerModelTester(self			)
     UpperCamelCase_						=  ConfigTester(self		,		config_class=lowerCamelCase__		,		hidden_size=37			)




  def  _UpperCAmelCase  (  self			)      ->			Any:
     self.config_tester.run_common_tests()




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCamelCase__			)




  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head(*lowerCamelCase__			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__			)




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__			)




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__			)




  @slow
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'			)
     self.assertIsNotNone(lowerCamelCase__			)




@require_tf
class     _a							(	unittest.TestCase     ):






  """simple docstring"""





  @slow
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'			)
     UpperCamelCase_						=  tf.constant([[0, 1, 2, 3, 4, 5]]			)
     UpperCamelCase_						=  model(lowerCamelCase__			)[0]

     # TODO Replace vocab size
     UpperCamelCase_						=  50000

     UpperCamelCase_						=  [1, 6, vocab_size]
     self.assertEqual(output.shape		,		lowerCamelCase__			)

     print(output[:, :3, :3]			)

     # TODO Replace values below with what was printed above.
     UpperCamelCase_						=  tf.constant(
         [
             [
                 [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
                 [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
                 [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
             ]
         ]			)
     tf.debugging.assert_near(output[:, :3, :3]		,		lowerCamelCase__		,		atol=1e-4			)




@require_tf
class     _a							(	unittest.TestCase     ):






  """simple docstring"""





  A_						       = 1e-4




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  tf.constant([[4, 10]]			)
     UpperCamelCase_						=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,		embedding_dim=6			)

     UpperCamelCase_						=  emba(input_ids.shape			)
     UpperCamelCase_						=  tf.constant(
         [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]			)

     tf.debugging.assert_near(lowerCamelCase__		,		lowerCamelCase__		,		atol=self.tolerance			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  tf.constant(
         [
             [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
             [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
             [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
         ]			)
     UpperCamelCase_						=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=512		,		embedding_dim=512			)
     emba([2, 16, 512]			)
     UpperCamelCase_						=  emba.weight[:3, :5]

     tf.debugging.assert_near(lowerCamelCase__		,		lowerCamelCase__		,		atol=self.tolerance			)




@require_tf
class     _a							(	unittest.TestCase     ):






  """simple docstring"""





  A_						       = 1e-4




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     # 2,12,16,64
     UpperCamelCase_						=  tf.reshape(tf.range(2 * 12 * 16 * 64		,		dtype=tf.floataa			)		,		shape=(2, 12, 16, 64)			) / 100

     UpperCamelCase_						=  -tf.reshape(tf.range(2 * 12 * 16 * 64		,		dtype=tf.floataa			)		,		shape=(2, 12, 16, 64)			) / 100

     UpperCamelCase_						=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		,		embedding_dim=64			)
     UpperCamelCase_						=  embed_positions([2, 16, 768]			)[None, None, :, :]

     UpperCamelCase_						=  TFRoFormerSelfAttention.apply_rotary_position_embeddings(
         lowerCamelCase__		,		lowerCamelCase__		,		lowerCamelCase__			)

     UpperCamelCase_						=  tf.constant(
         [
             [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
             [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
             [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
             [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
             [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
             [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
         ]			)
     UpperCamelCase_						=  tf.constant(
         [
             [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
             [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
             [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
             [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
             [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
             [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
         ]			)

     tf.debugging.assert_near(query_layer[0, 0, :6, :8]		,		lowerCamelCase__		,		atol=self.tolerance			)
     tf.debugging.assert_near(key_layer[0, 0, :6, :8]		,		lowerCamelCase__		,		atol=self.tolerance			)









"""simple docstring"""


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


__snake_case    					=					{
    '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 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							Dict:
    lowercase__       :       str				=						TOKEN
    HfFolder.save_token(lowerCamelCase__						)


   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							Optional[Any]:
    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 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       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						)

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

        # 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(lowerCamelCase__ ,     repo_id="""test-config""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

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


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       int				=						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						)

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

        # 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(
         lowerCamelCase__ ,     repo_id="""valid_org/test-config-org""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       Optional[Any]				=						BertConfig.from_pretrained("""valid_org/test-config-org"""						)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    CustomConfig.register_for_auto_class()
    lowercase__       :       int				=						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"""}						)

    lowercase__       :       Any				=						AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' ,     trust_remote_code=lowerCamelCase__						)
    # 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 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						GPTaConfig()

    # attempt to modify each of int/float/bool/str config records and verify they were updated
    lowercase__       :       List[str]				=						c.n_embd + 1  # int
    lowercase__       :       Union[str, Any]				=						c.resid_pdrop + 1.0  # float
    lowercase__       :       int				=						not c.scale_attn_weights  # bool
    lowercase__       :       Optional[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(lowerCamelCase__ ,     c.n_embd ,     """mismatch for key: n_embd"""						)
    self.assertEqual(lowerCamelCase__ ,     c.resid_pdrop ,     """mismatch for key: resid_pdrop"""						)
    self.assertEqual(lowerCamelCase__ ,     c.scale_attn_weights ,     """mismatch for key: scale_attn_weights"""						)
    self.assertEqual(lowerCamelCase__ ,     c.summary_type ,     """mismatch for key: summary_type"""						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       List[str]				=						PretrainedConfig()
    lowercase__       :       Union[str, 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(
        lowerCamelCase__ ,     ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""]						)
    lowercase__       :       List[Any]				=						[key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase__ ,     lowerCamelCase__						)]
    if len(lowerCamelCase__						) > 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(lowerCamelCase__						)}.'''						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    with self.assertRaises(lowerCamelCase__						):
     # config is in subfolder, the following should not work without specifying the subfolder
     lowercase__       :       str				=						BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""						)

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

    self.assertIsNotNone(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    # A mock response for an HTTP head request to emulate server down
    lowercase__       :       Dict				=						mock.Mock()
    lowercase__       :       str				=						500
    lowercase__       :       int				=						{}
    lowercase__       :       Optional[int]				=						HTTPError
    lowercase__       :       Dict				=						{}

    # Download this model to make sure it's in the cache.
    lowercase__       :       Any				=						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=lowerCamelCase__						) as mock_head:
     lowercase__       :       int				=						BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)
     # This check we did call the fake head request
     mock_head.assert_called()


   def 					UpperCAmelCase__(						self						)      ->							int:
    # This test is for deprecated behavior and can be removed in v5
    lowercase__       :       Optional[Any]				=						BertConfig.from_pretrained(
        """https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"""						)


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Optional[int]				=						AutoConfig.from_pretrained("""bert-base-cased"""						)
    lowercase__       :       Union[str, Any]				=						["""config.4.0.0.json"""]

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

     # This should pick the new configuration file as the version of Transformers is > 4.0.0
     lowercase__       :       Optional[int]				=						AutoConfig.from_pretrained(lowerCamelCase__						)
     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
     lowercase__       :       Optional[int]				=						["""config.42.0.0.json"""]
     lowercase__       :       Optional[int]				=						768
     configuration.save_pretrained(lowerCamelCase__						)
     shutil.move(os.path.join(lowerCamelCase__ ,     """config.4.0.0.json"""						) ,     os.path.join(lowerCamelCase__ ,     """config.42.0.0.json"""						)						)
     lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__						)
     self.assertEqual(new_configuration.hidden_size ,     768						)







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
    lowercase__       :       Any				=						"""hf-internal-testing/test-two-configs"""

    import transformers as new_transformers

    lowercase__       :       Union[str, Any]				=						"""v4.0.0"""
    lowercase__	,       lowercase__       :       int				=						new_transformers.models.auto.AutoConfig.from_pretrained(
        lowerCamelCase__ ,     return_unused_kwargs=lowerCamelCase__						)
    self.assertEqual(new_configuration.hidden_size ,     2						)
    # This checks `_configuration_file` ia not kept in the kwargs by mistake.
    self.assertDictEqual(lowerCamelCase__ ,     {}						)

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

    lowercase__       :       str				=						"""v3.0.0"""
    lowercase__       :       int				=						old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase__						)
    self.assertEqual(old_configuration.hidden_size ,     768						)