from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
      from transformers import (
          TFAutoModel,
          TFAutoModelForCausalLM,
          TFAutoModelForMaskedLM,
          TFAutoModelForPreTraining,
          TFAutoModelForQuestionAnswering,
          TFAutoModelForSeqaSeqLM,
          TFAutoModelForSequenceClassification,
          TFAutoModelForTableQuestionAnswering,
          TFAutoModelForTokenClassification,
          TFAutoModelWithLMHead,
          TFBertForMaskedLM,
          TFBertForPreTraining,
          TFBertForQuestionAnswering,
          TFBertForSequenceClassification,
          TFBertModel,
          TFFunnelBaseModel,
          TFFunnelModel,
          TFGPTaLMHeadModel,
          TFRobertaForMaskedLM,
          TFTaForConditionalGeneration,
          TFTapasForQuestionAnswering,
      )
      from transformers.models.auto.modeling_tf_auto import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TF_MODEL_MAPPING,
      )
      from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''new-model'''


if is_tf_available():


      class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
        """simple docstring"""







        lowerCamelCase__   =  NewModelConfig


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







  @slow
  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     "bert-base-cased"
        SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				List[str]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     "bert-base-cased"
        SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     TFAutoModelForPreTraining.from_pretrained(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Optional[Any]      )     ->		List[Any]:
        for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     TFAutoModelForCausalLM.from_pretrained(__magic_name__      )
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     TFAutoModelForCausalLM.from_pretrained(__magic_name__     ,   output_loading_info=__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Dict      )     ->		int:
        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     TFAutoModelWithLMHead.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Optional[Any]      )     ->		Tuple:
        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     TFAutoModelForMaskedLM.from_pretrained(__magic_name__      )
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     TFAutoModelForMaskedLM.from_pretrained(__magic_name__     ,   output_loading_info=__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Any      )     ->		List[str]:
        for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     TFAutoModelForSeqaSeqLM.from_pretrained(__magic_name__      )
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     TFAutoModelForSeqaSeqLM.from_pretrained(__magic_name__     ,   output_loading_info=__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				List[str]      )     ->		str:
        # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        for model_name in ["bert-base-uncased"]:
              SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     TFAutoModelForSequenceClassification.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Union[str, Any]      )     ->		List[Any]:
        # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        for model_name in ["bert-base-uncased"]:
              SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     TFAutoModelForQuestionAnswering.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  @slow
  @require_tensorflow_probability
  def       __A   (							self       :				Tuple      )     ->		List[str]:
        for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
              SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     TFAutoModelForTableQuestionAnswering.from_pretrained(__magic_name__      )
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     TFAutoModelForTableQuestionAnswering.from_pretrained(
                  __magic_name__     ,   output_loading_info=__magic_name__      )
              self.assertIsNotNone(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  def       __A   (							self       :				Tuple      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     TFAutoModelWithLMHead.from_pretrained(__magic_name__      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )
        self.assertEqual(model.num_parameters()     ,   14_410      )
        self.assertEqual(model.num_parameters(only_trainable=__magic_name__      )     ,   14_410      )







  def       __A   (							self       :				int      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     TFAutoModelWithLMHead.from_pretrained(__magic_name__      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )
        self.assertEqual(model.num_parameters()     ,   14_410      )
        self.assertEqual(model.num_parameters(only_trainable=__magic_name__      )     ,   14_410      )







  def       __A   (							self       :				Tuple      )     ->		int:
        # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
        SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     copy.deepcopy(model.config      )
        SCREAMING_SNAKE_CASE_      =     ["FunnelBaseModel"]
        SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_config(__magic_name__      )
        self.assertIsInstance(__magic_name__     ,   __magic_name__      )

        with tempfile.TemporaryDirectory() as tmp_dir:
              model.save_pretrained(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained(__magic_name__      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )







  def       __A   (							self       :				List[str]      )     ->		Any:
        try:
              AutoConfig.register("new-model"     ,   __magic_name__      )

              SCREAMING_SNAKE_CASE_      =     [
                  TFAutoModel,
                  TFAutoModelForCausalLM,
                  TFAutoModelForMaskedLM,
                  TFAutoModelForPreTraining,
                  TFAutoModelForQuestionAnswering,
                  TFAutoModelForSequenceClassification,
                  TFAutoModelForTokenClassification,
              ]

              for auto_class in auto_classes:
                    with self.subTest(auto_class.__name__      ):
                          # Wrong config class will raise an error
                          with self.assertRaises(__magic_name__      ):
                                auto_class.register(__magic_name__     ,   __magic_name__      )
                          auto_class.register(__magic_name__     ,   __magic_name__      )
                          # Trying to register something existing in the Transformers library will raise an error
                          with self.assertRaises(__magic_name__      ):
                                auto_class.register(__magic_name__     ,   __magic_name__      )

                          # Now that the config is registered, it can be used as any other config with the auto-API
                          SCREAMING_SNAKE_CASE_      =     BertModelTester(self      ).get_config()
                          SCREAMING_SNAKE_CASE_      =     NewModelConfig(**tiny_config.to_dict()      )
                          SCREAMING_SNAKE_CASE_      =     auto_class.from_config(__magic_name__      )
                          self.assertIsInstance(__magic_name__     ,   __magic_name__      )

                          with tempfile.TemporaryDirectory() as tmp_dir:
                                model.save_pretrained(__magic_name__      )
                                SCREAMING_SNAKE_CASE_      =     auto_class.from_pretrained(__magic_name__      )
                                self.assertIsInstance(__magic_name__     ,   __magic_name__      )

        finally:
              if "new-model" in CONFIG_MAPPING._extra_content:
                    del CONFIG_MAPPING._extra_content["new-model"]
              for mapping in (
                  TF_MODEL_MAPPING,
                  TF_MODEL_FOR_PRETRAINING_MAPPING,
                  TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                  TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                  TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                  TF_MODEL_FOR_MASKED_LM_MAPPING,
              ):
                    if NewModelConfig in mapping._extra_content:
                          del mapping._extra_content[NewModelConfig]







  def       __A   (							self       :				List[Any]      )     ->		List[Any]:
        with self.assertRaisesRegex(
            __magic_name__     ,   "bert-base is not a local folder and is not a valid model identifier"      ):
              SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("bert-base"      )







  def       __A   (							self       :				List[Any]      )     ->		Optional[int]:
        with self.assertRaisesRegex(
            __magic_name__     ,   r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"      ):
              SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained(__magic_name__     ,   revision="aaaaaa"      )







  def       __A   (							self       :				str      )     ->		Optional[int]:
        with self.assertRaisesRegex(
            __magic_name__     ,   "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"     ,   ):
              SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"      )







  def       __A   (							self       :				Optional[int]      )     ->		List[str]:
        with self.assertRaisesRegex(__magic_name__     ,   "Use `from_pt=True` to load this model"      ):
              SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"      )







  def       __A   (							self       :				Optional[int]      )     ->		List[Any]:
        # Make sure we have cached the model.
        SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"      )
        with RequestCounter() as counter:
              SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"      )
              self.assertEqual(counter.get_request_count     ,   0      )
              self.assertEqual(counter.head_request_count     ,   1      )
              self.assertEqual(counter.other_request_count     ,   0      )

        # With a sharded checkpoint
        SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"      )
        with RequestCounter() as counter:
              SCREAMING_SNAKE_CASE_      =     TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"      )
              self.assertEqual(counter.get_request_count     ,   0      )
              self.assertEqual(counter.head_request_count     ,   1      )
              self.assertEqual(counter.other_request_count     ,   0      )



from __future__ import annotations


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				Union[str, Any]     ,   __magic_name__       :				str     ,   __magic_name__       :				str      )     ->		Dict:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     text, pattern
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     len(__magic_name__      ), len(__magic_name__      )







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				str      )     ->		int:

        for i in range(self.patLen - 1     ,   -1     ,   -1      ):
              if char == self.pattern[i]:
                    return i
        return -1







  def       __A   (							self       :				Dict     ,   __magic_name__       :				int      )     ->		int:

        for i in range(self.patLen - 1     ,   -1     ,   -1      ):
              if self.pattern[i] != self.text[current_pos + i]:
                    return current_pos + i
        return -1







  def       __A   (							self       :				Tuple      )     ->		list[int]:
        # searches pattern in text and returns index positions
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(self.textLen - self.patLen + 1      ):
              SCREAMING_SNAKE_CASE_      =     self.mismatch_in_text(__magic_name__      )
              if mismatch_index == -1:
                    positions.append(__magic_name__      )
              else:
                    SCREAMING_SNAKE_CASE_      =     self.match_in_pattern(self.text[mismatch_index]      )
                    SCREAMING_SNAKE_CASE_      =     (
                        mismatch_index - match_index
                    )  # shifting index lgtm [py/multiple-definition]
        return positions



A   :     Dict       =				"ABAABA"
A   :     Union[str, Any]       =				"AB"
A   :     str       =				BoyerMooreSearch(text, pattern)
A   :     Tuple       =				bms.bad_character_heuristic()

if len(positions) == 0:
      print("No match found")
else:
      print("Pattern found in following positions: ")
      print(positions)


