
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__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=9_9    ,  __magic_name__=3_2    ,  __magic_name__=2    ,  __magic_name__=4    ,  __magic_name__=3_7    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=1_6    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=None    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      parent
       lowerCamelCase		:	List[str]							=      1_3
       lowerCamelCase		:	Union[str, Any]							=      7
       lowerCamelCase		:	int							=      True
       lowerCamelCase		:	Optional[int]							=      True
       lowerCamelCase		:	Tuple							=      True
       lowerCamelCase		:	List[Any]							=      True
       lowerCamelCase		:	Union[str, Any]							=      9_9
       lowerCamelCase		:	List[str]							=      3_2
       lowerCamelCase		:	Optional[Any]							=      2
       lowerCamelCase		:	Union[str, Any]							=      4
       lowerCamelCase		:	str							=      3_7
       lowerCamelCase		:	Union[str, Any]							=      """gelu"""
       lowerCamelCase		:	str							=      0.1
       lowerCamelCase		:	List[str]							=      0.1
       lowerCamelCase		:	str							=      5_1_2
       lowerCamelCase		:	int							=      1_6
       lowerCamelCase		:	List[str]							=      2
       lowerCamelCase		:	Optional[Any]							=      0.02
       lowerCamelCase		:	Optional[Any]							=      3
       lowerCamelCase		:	List[Any]							=      4
       lowerCamelCase		:	Dict							=      None
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )

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

       lowerCamelCase		:	Optional[Any]							=      None
       if self.use_token_type_ids:
        lowerCamelCase		:	Dict							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size      )

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

       lowerCamelCase		:	Dict							=      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=__magic_name__    ,  )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Union[str, Any]							=      TFRoFormerModel(config=__magic_name__      )
       lowerCamelCase		:	Optional[int]							=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

       lowerCamelCase		:	int							=      [input_ids, input_mask]
       lowerCamelCase		:	Any							=      model(__magic_name__      )

       lowerCamelCase		:	Tuple							=      model(__magic_name__      )

       self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      True
       lowerCamelCase		:	Tuple							=      TFRoFormerForCausalLM(config=__magic_name__      )
       lowerCamelCase		:	str							=      {
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       lowerCamelCase		:	Tuple							=      model(__magic_name__      )["""logits"""]
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape      )    ,  [self.batch_size, self.seq_length, self.vocab_size]      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      TFRoFormerForMaskedLM(config=__magic_name__      )
       lowerCamelCase		:	Dict							=      {
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       lowerCamelCase		:	Dict							=      model(__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Dict							=      self.num_labels
       lowerCamelCase		:	Optional[int]							=      TFRoFormerForSequenceClassification(config=__magic_name__      )
       lowerCamelCase		:	str							=      {
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }

       lowerCamelCase		:	Dict							=      model(__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Any							=      self.num_choices
       lowerCamelCase		:	str							=      TFRoFormerForMultipleChoice(config=__magic_name__      )
       lowerCamelCase		:	List[str]							=      tf.tile(tf.expand_dims(__magic_name__    ,  1      )    ,  (1, self.num_choices, 1)      )
       lowerCamelCase		:	Dict							=      tf.tile(tf.expand_dims(__magic_name__    ,  1      )    ,  (1, self.num_choices, 1)      )
       lowerCamelCase		:	int							=      tf.tile(tf.expand_dims(__magic_name__    ,  1      )    ,  (1, self.num_choices, 1)      )
       lowerCamelCase		:	Tuple							=      {
           """input_ids""": multiple_choice_inputs_ids,
           """attention_mask""": multiple_choice_input_mask,
           """token_type_ids""": multiple_choice_token_type_ids,
       }
       lowerCamelCase		:	Optional[Any]							=      model(__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_choices)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	List[Any]							=      self.num_labels
       lowerCamelCase		:	List[str]							=      TFRoFormerForTokenClassification(config=__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      {
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       lowerCamelCase		:	int							=      model(__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.num_labels)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      TFRoFormerForQuestionAnswering(config=__magic_name__      )
       lowerCamelCase		:	Optional[int]							=      {
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }

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





@require_tf
class 							A__		(	__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    List[Any]           =			(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      _UpperCAmelCase				:    str           =			(
          {
              """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 {}
      )

      _UpperCAmelCase				:    Tuple           =			False
      _UpperCAmelCase				:    str           =			False
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

       return False
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      TFRoFormerModelTester(self      )
       lowerCamelCase		:	Optional[int]							=      ConfigTester(self    ,  config_class=__magic_name__    ,  hidden_size=3_7      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__magic_name__      )






      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""      )
       self.assertIsNotNone(__magic_name__      )





@require_tf
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""      )
       lowerCamelCase		:	Optional[int]							=      tf.constant([[0, 1, 2, 3, 4, 5]]      )
       lowerCamelCase		:	Optional[Any]							=      model(__magic_name__      )[0]

       # TODO Replace vocab size
       lowerCamelCase		:	Optional[Any]							=      5_0_0_0_0

       lowerCamelCase		:	Union[str, Any]							=      [1, 6, vocab_size]
       self.assertEqual(output.shape    ,  __magic_name__      )

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

       # TODO Replace values below with what was printed above.
       lowerCamelCase		:	Union[str, Any]							=      tf.constant(
           [
               [
                   [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                   [-1.5_133_783, 0.197_433, 0.15_190_607],
                   [-5.0_135_403, -3.900_256, -0.84_038_764],
               ]
           ]      )
       tf.debugging.assert_near(output[:, :3, :3]    ,  __magic_name__    ,  atol=1e-4      )





@require_tf
class 							A__		(	unittest.TestCase):
      _UpperCAmelCase				:    List[str]           =			1E-4
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      tf.constant([[4, 1_0]]      )
       lowerCamelCase		:	Optional[int]							=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6    ,  embedding_dim=6      )

       lowerCamelCase		:	Optional[Any]							=      emba(input_ids.shape      )
       lowerCamelCase		:	Tuple							=      tf.constant(
           [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]      )

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






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      tf.constant(
           [
               [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
               [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
               [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
           ]      )
       lowerCamelCase		:	Dict							=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2    ,  embedding_dim=5_1_2      )
       emba([2, 1_6, 5_1_2]      )
       lowerCamelCase		:	List[str]							=      emba.weight[:3, :5]

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





@require_tf
class 							A__		(	unittest.TestCase):
      _UpperCAmelCase				:    Optional[int]           =			1E-4
      def 						UpperCamelCase__							(      self      ):
       # 2,12,16,64
       lowerCamelCase		:	Union[str, Any]							=      tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4    ,  dtype=tf.floataa      )    ,  shape=(2, 1_2, 1_6, 6_4)      ) / 1_0_0

       lowerCamelCase		:	Dict							=      -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4    ,  dtype=tf.floataa      )    ,  shape=(2, 1_2, 1_6, 6_4)      ) / 1_0_0

       lowerCamelCase		:	Union[str, Any]							=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2    ,  embedding_dim=6_4      )
       lowerCamelCase		:	List[str]							=      embed_positions([2, 1_6, 7_6_8]      )[None, None, :, :]

       lowerCamelCase			,    lowerCamelCase		:	Any							=      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           __magic_name__    ,  __magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	Union[str, Any]							=      tf.constant(
           [
               [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
               [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
               [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
               [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
               [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
               [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
           ]      )
       lowerCamelCase		:	str							=      tf.constant(
           [
               [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
               [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
               [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
               [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
               [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
               [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
           ]      )

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






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					   ={
    """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""": 1_2_8,
    """add_cross_attention""": True,
    """tie_encoder_decoder""": True,
    """max_length""": 5_0,
    """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""": 1_0,
    """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""": 1_0,
    """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 						UpperCamelCase__							(      cls      ):
       lowerCamelCase		:	int							=      TOKEN
       HfFolder.save_token(__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls      ):
       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      ):
       lowerCamelCase		:	List[Any]							=      BertConfig(
           vocab_size=9_9    ,  hidden_size=3_2    ,  num_hidden_layers=5    ,  num_attention_heads=4    ,  intermediate_size=3_7      )
       config.push_to_hub("""test-config"""    ,  use_auth_token=self._token      )

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

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

       lowerCamelCase		:	Optional[Any]							=      BertConfig.from_pretrained(F'''{USER}/test-config'''      )
       for k, v in config.to_dict().items():
        if k != "transformers_version":
         self.assertEqual(__magic_name__    ,  getattr(__magic_name__    ,  __magic_name__      )      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      BertConfig(
           vocab_size=9_9    ,  hidden_size=3_2    ,  num_hidden_layers=5    ,  num_attention_heads=4    ,  intermediate_size=3_7      )
       config.push_to_hub("""valid_org/test-config-org"""    ,  use_auth_token=self._token      )

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

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

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






      def 						UpperCamelCase__							(      self      ):
       CustomConfig.register_for_auto_class()
       lowerCamelCase		:	Optional[Any]							=      CustomConfig(attribute=4_2      )

       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"""}      )

       lowerCamelCase		:	List[str]							=      AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''    ,  trust_remote_code=__magic_name__      )
       # 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    ,  4_2      )





class 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      GPTaConfig()

       # attempt to modify each of int/float/bool/str config records and verify they were updated
       lowerCamelCase		:	Optional[int]							=      c.n_embd + 1  # int
       lowerCamelCase		:	Optional[int]							=      c.resid_pdrop + 1.0  # float
       lowerCamelCase		:	Tuple							=      not c.scale_attn_weights  # bool
       lowerCamelCase		:	Any							=      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(__magic_name__    ,  c.n_embd    ,  """mismatch for key: n_embd"""      )
       self.assertEqual(__magic_name__    ,  c.resid_pdrop    ,  """mismatch for key: resid_pdrop"""      )
       self.assertEqual(__magic_name__    ,  c.scale_attn_weights    ,  """mismatch for key: scale_attn_weights"""      )
       self.assertEqual(__magic_name__    ,  c.summary_type    ,  """mismatch for key: summary_type"""      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      PretrainedConfig()
       lowerCamelCase		:	int							=      [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(
           __magic_name__    ,  ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""]      )
       lowerCamelCase		:	List[str]							=      [key for key, value in config_common_kwargs.items() if value == getattr(__magic_name__    ,  __magic_name__      )]
       if len(__magic_name__      ) > 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(__magic_name__      )}.'''      )
      def 						UpperCamelCase__							(      self      ):
       with self.assertRaises(__magic_name__      ):
        # config is in subfolder, the following should not work without specifying the subfolder
        lowerCamelCase		:	Dict							=      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""      )

       lowerCamelCase		:	str							=      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""    ,  subfolder="""bert"""      )

       self.assertIsNotNone(__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       # A mock response for an HTTP head request to emulate server down
       lowerCamelCase		:	Dict							=      mock.Mock()
       lowerCamelCase		:	Optional[int]							=      5_0_0
       lowerCamelCase		:	List[Any]							=      {}
       lowerCamelCase		:	Tuple							=      HTTPError
       lowerCamelCase		:	Union[str, Any]							=      {}

       # Download this model to make sure it's in the cache.
       lowerCamelCase		:	List[str]							=      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=__magic_name__      ) as mock_head:
        lowerCamelCase		:	Any							=      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      ):
       # This test is for deprecated behavior and can be removed in v5
       lowerCamelCase		:	List[str]							=      BertConfig.from_pretrained(
           """https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"""      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      AutoConfig.from_pretrained("""bert-base-cased"""      )
       lowerCamelCase		:	Optional[Any]							=      ["""config.4.0.0.json"""]

       with tempfile.TemporaryDirectory() as tmp_dir:
        configuration.save_pretrained(__magic_name__      )
        lowerCamelCase		:	str							=      2
        json.dump(configuration.to_dict()    ,  open(os.path.join(__magic_name__    ,  """config.4.0.0.json"""      )    ,  """w"""      )      )

        # This should pick the new configuration file as the version of Transformers is > 4.0.0
        lowerCamelCase		:	Union[str, Any]							=      AutoConfig.from_pretrained(__magic_name__      )
        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
        lowerCamelCase		:	Any							=      ["""config.42.0.0.json"""]
        lowerCamelCase		:	Optional[Any]							=      7_6_8
        configuration.save_pretrained(__magic_name__      )
        shutil.move(os.path.join(__magic_name__    ,  """config.4.0.0.json"""      )    ,  os.path.join(__magic_name__    ,  """config.42.0.0.json"""      )      )
        lowerCamelCase		:	int							=      AutoConfig.from_pretrained(__magic_name__      )
        self.assertEqual(new_configuration.hidden_size    ,  7_6_8      )






      def 						UpperCamelCase__							(      self      ):
       # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
       lowerCamelCase		:	str							=      """hf-internal-testing/test-two-configs"""

       import transformers as new_transformers

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

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

       lowerCamelCase		:	Tuple							=      """v3.0.0"""
       lowerCamelCase		:	Any							=      old_transformers.models.auto.AutoConfig.from_pretrained(__magic_name__      )
       self.assertEqual(old_configuration.hidden_size    ,  7_6_8      )




