


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 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"new-model"


if is_tf_available():







    class 			_lowercase (							lowercase__):


           """simple docstring"""




           A__											=			NewModelConfig







@require_tf
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       @slow
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  "bert-base-cased"
             lowerCamelCase__		:				Optional[Any]    =  AutoConfig.from_pretrained(__lowerCamelCase       )
             self.assertIsNotNone(__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

             lowerCamelCase__		:				Any    =  TFAutoModel.from_pretrained(__lowerCamelCase       )
             self.assertIsNotNone(__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  "bert-base-cased"
             lowerCamelCase__		:				Union[str, Any]    =  AutoConfig.from_pretrained(__lowerCamelCase       )
             self.assertIsNotNone(__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  TFAutoModelForPreTraining.from_pretrained(__lowerCamelCase       )
             self.assertIsNotNone(__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				Tuple    =  AutoConfig.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				int    =  TFAutoModelForCausalLM.from_pretrained(__lowerCamelCase       )
                   lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  TFAutoModelForCausalLM.from_pretrained(__lowerCamelCase					,      output_loading_info=__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				Tuple    =  AutoConfig.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				Union[str, Any]    =  TFAutoModelWithLMHead.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				List[Any]    =  AutoConfig.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				str    =  TFAutoModelForMaskedLM.from_pretrained(__lowerCamelCase       )
                   lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  TFAutoModelForMaskedLM.from_pretrained(__lowerCamelCase					,      output_loading_info=__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				List[str]    =  AutoConfig.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				int    =  TFAutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase       )
                   lowerCamelCase__    ,   lowerCamelCase__		:				int    =  TFAutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase					,      output_loading_info=__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             for model_name in ["bert-base-uncased"]:
                   lowerCamelCase__		:				Dict    =  AutoConfig.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				Any    =  TFAutoModelForSequenceClassification.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             for model_name in ["bert-base-uncased"]:
                   lowerCamelCase__		:				Optional[int]    =  AutoConfig.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				Dict    =  TFAutoModelForQuestionAnswering.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       @slow
       @require_tensorflow_probability
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
                   lowerCamelCase__		:				Dict    =  AutoConfig.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				str    =  TFAutoModelForTableQuestionAnswering.from_pretrained(__lowerCamelCase       )
                   lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  TFAutoModelForTableQuestionAnswering.from_pretrained(
                       __lowerCamelCase					,      output_loading_info=__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  TFAutoModelWithLMHead.from_pretrained(__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )
             self.assertEqual(model.num_parameters()					,      14410       )
             self.assertEqual(model.num_parameters(only_trainable=__lowerCamelCase       )					,      14410       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  TFAutoModelWithLMHead.from_pretrained(__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )
             self.assertEqual(model.num_parameters()					,      14410       )
             self.assertEqual(model.num_parameters(only_trainable=__lowerCamelCase       )					,      14410       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

             lowerCamelCase__		:				List[str]    =  copy.deepcopy(model.config       )
             lowerCamelCase__		:				Tuple    =  ["FunnelBaseModel"]
             lowerCamelCase__		:				Dict    =  TFAutoModel.from_config(__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

             with tempfile.TemporaryDirectory() as tmp_dir:
                   model.save_pretrained(__lowerCamelCase       )
                   lowerCamelCase__		:				Union[str, Any]    =  TFAutoModel.from_pretrained(__lowerCamelCase       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             try:
                   AutoConfig.register("new-model"					,      __lowerCamelCase       )

                   lowerCamelCase__		:				List[Any]    =  [
                       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(__lowerCamelCase       ):
                                     auto_class.register(__lowerCamelCase					,      __lowerCamelCase       )
                               auto_class.register(__lowerCamelCase					,      __lowerCamelCase       )
                               # Trying to register something existing in the Transformers library will raise an error
                               with self.assertRaises(__lowerCamelCase       ):
                                     auto_class.register(__lowerCamelCase					,      __lowerCamelCase       )

                               # Now that the config is registered, it can be used as any other config with the auto-API
                               lowerCamelCase__		:				Dict    =  BertModelTester(self       ).get_config()
                               lowerCamelCase__		:				int    =  NewModelConfig(**tiny_config.to_dict()       )
                               lowerCamelCase__		:				Optional[Any]    =  auto_class.from_config(__lowerCamelCase       )
                               self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                               with tempfile.TemporaryDirectory() as tmp_dir:
                                     model.save_pretrained(__lowerCamelCase       )
                                     lowerCamelCase__		:				Union[str, Any]    =  auto_class.from_pretrained(__lowerCamelCase       )
                                     self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

             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 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             with self.assertRaisesRegex(
                 __lowerCamelCase					,      "bert-base is not a local folder and is not a valid model identifier"       ):
                   lowerCamelCase__		:				Union[str, Any]    =  TFAutoModel.from_pretrained("bert-base"       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             with self.assertRaisesRegex(
                 __lowerCamelCase					,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"       ):
                   lowerCamelCase__		:				int    =  TFAutoModel.from_pretrained(__lowerCamelCase					,      revision="aaaaaa"       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             with self.assertRaisesRegex(
                 __lowerCamelCase					,      "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"					,      ):
                   lowerCamelCase__		:				Dict    =  TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             with self.assertRaisesRegex(__lowerCamelCase					,      "Use `from_pt=True` to load this model"       ):
                   lowerCamelCase__		:				Tuple    =  TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"       )
             with RequestCounter() as counter:
                   lowerCamelCase__		:				Tuple    =  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
             lowerCamelCase__		:				Optional[Any]    =  TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"       )
             with RequestCounter() as counter:
                   lowerCamelCase__		:				int    =  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

A      :    Union[str, Any]		=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			dict[str, list[str]]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  graph
             # mapping node to its parent in resulting breadth first tree
             lowerCamelCase__		:				dict[str, str | None]    =  {}
             lowerCamelCase__		:				Dict    =  source_vertex





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {self.source_vertex}
             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  [self.source_vertex]  # first in first out queue

             while queue:
                   lowerCamelCase__		:				Optional[int]    =  queue.pop(0       )
                   for adjacent_vertex in self.graph[vertex]:
                         if adjacent_vertex not in visited:
                               visited.add(__lowerCamelCase       )
                               lowerCamelCase__		:				List[str]    =  vertex
                               queue.append(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if target_vertex == self.source_vertex:
                   return self.source_vertex

             lowerCamelCase__		:				Tuple    =  self.parent.get(__lowerCamelCase       )
             if target_vertex_parent is None:
                   lowerCamelCase__		:				Tuple    =  (
                       f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                   )
                   raise ValueError(__lowerCamelCase       )

             return self.shortest_path(__lowerCamelCase       ) + f"->{target_vertex}"


if __name__ == "__main__":
    A      :    List[str]		=					Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


