



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Union[str, Any]   ,		_a       :						str   ,		_a       :						Tuple   ,		_a       :						Any    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					BertConfig.from_pretrained(
        """bert-large-cased"""   ,		vocab_size=_a   ,		max_position_embeddings=512   ,		is_decoder=_a   ,		add_cross_attention=_a   ,		)
    UpperCAmelCase_     :       Any  		=					bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCAmelCase_     :       str  		=					BertGenerationConfig(**_a    )
    if is_encoder:
        UpperCAmelCase_     :       List[str]  		=					BertGenerationEncoder(_a    )
    else:
        UpperCAmelCase_     :       Any  		=					BertGenerationDecoder(_a    )
    print(F'''Building PyTorch model from configuration: {config}'''    )

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _a   ,		_a   ,		model_class="""bert"""   ,		is_encoder_named_decoder=_a   ,		is_encoder=_a   ,		)

    # Save pytorch-model
    print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
    model.save_pretrained(_a    )


if __name__ == "__main__":
      UpperCamelCase_									=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--is_encoder_named_decoder''',
          action='''store_true''',
          help='''If decoder has to be renamed to encoder in PyTorch model.''',
      )
      parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
      parser.add_argument('''--vocab_size''', default=50358, type=int, help='''Vocab size of model''')
      UpperCamelCase_									=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_    :       int       =							{
    '''agents''': ['''Agent''', '''AzureOpenAiAgent''', '''HfAgent''', '''LocalAgent''', '''OpenAiAgent'''],
    '''base''': ['''PipelineTool''', '''RemoteTool''', '''Tool''', '''launch_gradio_demo''', '''load_tool'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       int       =							['''DocumentQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''ImageCaptioningTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''ImageQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       str       =							['''ImageSegmentationTool''']
 SCREAMING_SNAKE_CASE_    :       Any       =							['''SpeechToTextTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''TextClassificationTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''TextQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TextSummarizationTool''']
 SCREAMING_SNAKE_CASE_    :       Optional[int]       =							['''TextToSpeechTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TranslationTool''']

if TYPE_CHECKING:
 from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
 from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .document_question_answering import DocumentQuestionAnsweringTool
  from .image_captioning import ImageCaptioningTool
  from .image_question_answering import ImageQuestionAnsweringTool
  from .image_segmentation import ImageSegmentationTool
  from .speech_to_text import SpeechToTextTool
  from .text_classification import TextClassificationTool
  from .text_question_answering import TextQuestionAnsweringTool
  from .text_summarization import TextSummarizationTool
  from .text_to_speech import TextToSpeechTool
  from .translation import TranslationTool
else:
 import sys

 SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






