


"""simple docstring"""


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 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=13 ,     lowerCamelCase__=7 ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=99 ,     lowerCamelCase__=32 ,     lowerCamelCase__=2 ,     lowerCamelCase__=4 ,     lowerCamelCase__=37 ,     lowerCamelCase__="gelu" ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=512 ,     lowerCamelCase__=16 ,     lowerCamelCase__=2 ,     lowerCamelCase__=0.02 ,     lowerCamelCase__=3 ,     lowerCamelCase__=4 ,     lowerCamelCase__=None ,     )      ->							Union[str, Any]:
    lowercase__       :       List[Any]				=						parent
    lowercase__       :       Optional[Any]				=						13
    lowercase__       :       Optional[Any]				=						7
    lowercase__       :       Any				=						True
    lowercase__       :       Optional[Any]				=						True
    lowercase__       :       Tuple				=						True
    lowercase__       :       int				=						True
    lowercase__       :       List[Any]				=						99
    lowercase__       :       Optional[int]				=						32
    lowercase__       :       Tuple				=						2
    lowercase__       :       str				=						4
    lowercase__       :       str				=						37
    lowercase__       :       str				=						"""gelu"""
    lowercase__       :       Any				=						0.1
    lowercase__       :       str				=						0.1
    lowercase__       :       Any				=						512
    lowercase__       :       List[str]				=						16
    lowercase__       :       int				=						2
    lowercase__       :       Any				=						0.02
    lowercase__       :       Tuple				=						3
    lowercase__       :       str				=						4
    lowercase__       :       Dict				=						None


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[str]				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

    lowercase__       :       int				=						None
    if self.use_input_mask:
     lowercase__       :       Optional[int]				=						random_attention_mask([self.batch_size, self.seq_length]						)

    lowercase__       :       List[str]				=						None
    if self.use_token_type_ids:
     lowercase__       :       List[Any]				=						ids_tensor([self.batch_size, self.seq_length] ,     self.type_vocab_size						)

    lowercase__       :       Optional[Any]				=						None
    lowercase__       :       Any				=						None
    lowercase__       :       str				=						None
    if self.use_labels:
     lowercase__       :       List[str]				=						ids_tensor([self.batch_size] ,     self.type_sequence_label_size						)
     lowercase__       :       Union[str, Any]				=						ids_tensor([self.batch_size, self.seq_length] ,     self.num_labels						)
     lowercase__       :       List[str]				=						ids_tensor([self.batch_size] ,     self.num_choices						)

    lowercase__       :       Optional[Any]				=						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 ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[int]:
    lowercase__       :       Optional[int]				=						TFRoFormerModel(config=lowerCamelCase__						)
    lowercase__       :       int				=						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

    lowercase__       :       Optional[Any]				=						[input_ids, input_mask]
    lowercase__       :       List[Any]				=						model(lowerCamelCase__						)

    lowercase__       :       List[str]				=						model(lowerCamelCase__						)

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


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


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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Tuple:
    lowercase__       :       List[str]				=						self.num_labels
    lowercase__       :       Optional[int]				=						TFRoFormerForSequenceClassification(config=lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						{
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }

    lowercase__       :       Optional[int]				=						model(lowerCamelCase__						)
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)


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


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


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

    lowercase__       :       Optional[int]				=						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]:
    lowercase__       :       Any				=						self.prepare_config_and_inputs()
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       Optional[int]				=						config_and_inputs
    lowercase__       :       Optional[int]				=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict

@require_tf
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       int									=    (
       (
           TFRoFormerModel,
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   _a   :       Tuple									=    (
       {
           '''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   :       Union[str, Any]									=    False
   _a   :       int									=    False


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

    return False


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Union[str, Any]				=						TFRoFormerModelTester(self						)
    lowercase__       :       List[Any]				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     hidden_size=37						)


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


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       int				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[Any]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       List[str]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_lm_head(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Any				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__						)


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


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       int				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[str]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__						)







   @slow
   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       str				=						TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""						)
    self.assertIsNotNone(lowerCamelCase__						)

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



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

    # TODO Replace vocab size
    lowercase__       :       int				=						5_0000

    lowercase__       :       Dict				=						[1, 6, vocab_size]
    self.assertEqual(output.shape ,     lowerCamelCase__						)

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

    # TODO Replace values below with what was printed above.
    lowercase__       :       Optional[Any]				=						tf.constant(
        [
            [
                [-0.1205_3341, -1.026_4901, 0.2922_1946],
                [-1.513_3783, 0.19_7433, 0.1519_0607],
                [-5.013_5403, -3.90_0256, -0.8403_8764],
            ]
        ]						)
    tf.debugging.assert_near(output[:, :3, :3] ,     lowerCamelCase__ ,     atol=1E-4						)

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



   _a   :       str									=    1E-4


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Tuple				=						tf.constant([[4, 10]]						)
    lowercase__       :       List[Any]				=						TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,     embedding_dim=6						)

    lowercase__       :       Tuple				=						emba(input_ids.shape						)
    lowercase__       :       Optional[int]				=						tf.constant(
        [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]						)

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







   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Optional[int]				=						tf.constant(
        [
            [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
            [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
            [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
        ]						)
    lowercase__       :       str				=						TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 ,     embedding_dim=512						)
    emba([2, 16, 512]						)
    lowercase__       :       Optional[int]				=						emba.weight[:3, :5]

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

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



   _a   :       str									=    1E-4


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

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

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

    lowercase__	,       lowercase__       :       Optional[Any]				=						TFRoFormerSelfAttention.apply_rotary_position_embeddings(
        lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)

    lowercase__       :       Dict				=						tf.constant(
        [
            [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
            [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
            [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
            [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
            [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
            [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
        ]						)
    lowercase__       :       Tuple				=						tf.constant(
        [
            [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
            [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
            [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
            [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
            [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
            [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
        ]						)

    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						)