



import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf



class 				lowerCAmelCase_		(  enum.Enum							):
       UpperCAmelCase__				:					List[str]        =  0
       UpperCAmelCase__				:					Optional[int]        =  1
       UpperCAmelCase__				:					Dict        =  2






@add_end_docstrings(a__							)
class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Dict        =  "\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    "
       def __init__(			self,			*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          super().__init__(*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
          self.check_model_type(
              TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == 'tf' else MODEL_FOR_CAUSAL_LM_MAPPING       )
          if "prefix" not in self._preprocess_params:
             # This is very specific. The logic is quite complex and needs to be done
             # as a "default".
             # It also defines both some preprocess_kwargs and generate_kwargs
             # which is why we cannot put them in their respective methods.
             UpperCamelCase    :  Tuple							=	None
             if self.model.config.prefix is not None:
                UpperCamelCase    :  Any							=	self.model.config.prefix
             if prefix is None and self.model.__class__.__name__ in [
                 "XLNetLMHeadModel",
                 "TransfoXLLMHeadModel",
                 "TFXLNetLMHeadModel",
                 "TFTransfoXLLMHeadModel",
             ]:
                # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                UpperCamelCase    :  List[Any]							=	self.XL_PREFIX
             if prefix is not None:
                # Recalculate some generate_kwargs linked to prefix.
                UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	self._sanitize_parameters(prefix=SCREAMING_SNAKE_CASE_,			**self._forward_params       )
                UpperCamelCase    :  str							=	{**self._preprocess_params, **preprocess_params}
                UpperCamelCase    :  Optional[int]							=	{**self._forward_params, **forward_params}
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  int							=	{}
          if prefix is not None:
             UpperCamelCase    :  Dict							=	prefix
          if prefix:
             UpperCamelCase    :  Optional[Any]							=	self.tokenizer(
                 SCREAMING_SNAKE_CASE_,			padding=SCREAMING_SNAKE_CASE_,			add_special_tokens=SCREAMING_SNAKE_CASE_,			return_tensors=self.framework       )
             UpperCamelCase    :  Tuple							=	prefix_inputs['input_ids'].shape[-1]

          if handle_long_generation is not None:
             if handle_long_generation not in {"hole"}:
                raise ValueError(
                    F"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
                    ' [None, \'hole\']'       )
             UpperCamelCase    :  List[str]							=	handle_long_generation

          preprocess_params.update(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	generate_kwargs

          UpperCamelCase    :  Tuple							=	{}
          if return_full_text is not None and return_type is None:
             if return_text is not None:
                raise ValueError('`return_text` is mutually exclusive with `return_full_text`'       )
             if return_tensors is not None:
                raise ValueError('`return_full_text` is mutually exclusive with `return_tensors`'       )
             UpperCamelCase    :  Any							=	ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
          if return_tensors is not None and return_type is None:
             if return_text is not None:
                raise ValueError('`return_text` is mutually exclusive with `return_tensors`'       )
             UpperCamelCase    :  int							=	ReturnType.TENSORS
          if return_type is not None:
             UpperCamelCase    :  Optional[Any]							=	return_type
          if clean_up_tokenization_spaces is not None:
             UpperCamelCase    :  Optional[int]							=	clean_up_tokenization_spaces

          if stop_sequence is not None:
             UpperCamelCase    :  List[str]							=	self.tokenizer.encode(SCREAMING_SNAKE_CASE_,			add_special_tokens=SCREAMING_SNAKE_CASE_       )
             if len(SCREAMING_SNAKE_CASE_       ) > 1:
                warnings.warn(
                    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                    ' the stop sequence will be used as the stop sequence string in the interim.'       )
             UpperCamelCase    :  Optional[Any]							=	stop_sequence_ids[0]

          return preprocess_params, forward_params, postprocess_params
       def 					snake_case_       (			self,			*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          # Parse arguments
          if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
             kwargs.update({'add_space_before_punct_symbol': True}       )

          return super()._parse_and_tokenize(*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
       def __call__(			self,			SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return super().__call__(SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_="",			SCREAMING_SNAKE_CASE_=None,			**SCREAMING_SNAKE_CASE_       )     ->		Tuple:
          UpperCamelCase    :  int							=	self.tokenizer(
              prefix + prompt_text,			padding=SCREAMING_SNAKE_CASE_,			add_special_tokens=SCREAMING_SNAKE_CASE_,			return_tensors=self.framework       )
          UpperCamelCase    :  str							=	prompt_text

          if handle_long_generation == "hole":
             UpperCamelCase    :  List[Any]							=	inputs['input_ids'].shape[-1]
             if "max_new_tokens" in generate_kwargs:
                UpperCamelCase    :  Any							=	generate_kwargs['max_new_tokens']
             else:
                UpperCamelCase    :  List[str]							=	generate_kwargs.get('max_length',			self.model.config.max_length       ) - cur_len
                if new_tokens < 0:
                   raise ValueError('We cannot infer how many new tokens are expected'       )
             if cur_len + new_tokens > self.tokenizer.model_max_length:
                UpperCamelCase    :  Union[str, Any]							=	self.tokenizer.model_max_length - new_tokens
                if keep_length <= 0:
                   raise ValueError(
                       'We cannot use `hole` to handle this generation the number of desired tokens exceeds the'
                       ' models max length'       )

                UpperCamelCase    :  Any							=	inputs['input_ids'][:, -keep_length:]
                if "attention_mask" in inputs:
                   UpperCamelCase    :  int							=	inputs['attention_mask'][:, -keep_length:]

          return inputs
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		int:
          UpperCamelCase    :  Tuple							=	model_inputs['input_ids']
          UpperCamelCase    :  str							=	model_inputs.get('attention_mask',			SCREAMING_SNAKE_CASE_       )
          # Allow empty prompts
          if input_ids.shape[1] == 0:
             UpperCamelCase    :  List[Any]							=	None
             UpperCamelCase    :  Optional[Any]							=	None
             UpperCamelCase    :  List[str]							=	1
          else:
             UpperCamelCase    :  Optional[int]							=	input_ids.shape[0]
          UpperCamelCase    :  List[Any]							=	model_inputs.pop('prompt_text'       )

          # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
          # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
          UpperCamelCase    :  Any							=	generate_kwargs.pop('prefix_length',			0       )
          if prefix_length > 0:
             UpperCamelCase    :  str							=	'max_new_tokens' in generate_kwargs or (
                 'generation_config' in generate_kwargs
                 and generate_kwargs['generation_config'].max_new_tokens is not None
             )
             if not has_max_new_tokens:
                UpperCamelCase    :  int							=	generate_kwargs.get('max_length'       ) or self.model.config.max_length
                generate_kwargs["max_length"] += prefix_length
             UpperCamelCase    :  Union[str, Any]							=	'min_new_tokens' in generate_kwargs or (
                 'generation_config' in generate_kwargs
                 and generate_kwargs['generation_config'].min_new_tokens is not None
             )
             if not has_min_new_tokens and "min_length" in generate_kwargs:
                generate_kwargs["min_length"] += prefix_length

        # BS x SL
          UpperCamelCase    :  str							=	self.model.generate(input_ids=SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	generated_sequence.shape[0]
          if self.framework == "pt":
             UpperCamelCase    :  Optional[int]							=	generated_sequence.reshape(SCREAMING_SNAKE_CASE_,			out_b // in_b,			*generated_sequence.shape[1:]       )
          elif self.framework == "tf":
             UpperCamelCase    :  Any							=	tf.reshape(SCREAMING_SNAKE_CASE_,			(in_b, out_b // in_b, *generated_sequence.shape[1:])       )
          return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=ReturnType.FULL_TEXT,			SCREAMING_SNAKE_CASE_=True       )     ->		int:
          UpperCamelCase    :  Dict							=	model_outputs['generated_sequence'][0]
          UpperCamelCase    :  Any							=	model_outputs['input_ids']
          UpperCamelCase    :  Union[str, Any]							=	model_outputs['prompt_text']
          UpperCamelCase    :  str							=	generated_sequence.numpy().tolist()
          UpperCamelCase    :  List[str]							=	[]
          for sequence in generated_sequence:
             if return_type == ReturnType.TENSORS:
                UpperCamelCase    :  Optional[int]							=	{'generated_token_ids': sequence}
             elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                # Decode text
                UpperCamelCase    :  Tuple							=	self.tokenizer.decode(
                    SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,			)

                # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                if input_ids is None:
                   UpperCamelCase    :  Union[str, Any]							=	0
                else:
                   UpperCamelCase    :  int							=	len(
                       self.tokenizer.decode(
                           input_ids[0],			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,			)       )

                if return_type == ReturnType.FULL_TEXT:
                   UpperCamelCase    :  int							=	prompt_text + text[prompt_length:]
                else:
                   UpperCamelCase    :  Dict							=	text[prompt_length:]

                UpperCamelCase    :  Tuple							=	{'generated_text': all_text}
             records.append(SCREAMING_SNAKE_CASE_       )

          return records








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



