
import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch


SCREAMING_SNAKE_CASE				:List[str]									=							logging.get_logger(__name__)





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					Optional[int]				,A   :					str = None				,A   :					uuid.UUID = None				,A   :					List[Any]=None				,A   :					str=None					):
					if not conversation_id:
									__A       =					uuid.uuida()
					if past_user_inputs is None:
									__A       =					[]
					if generated_responses is None:
									__A       =					[]

					__A       =					conversation_id
					__A       =					past_user_inputs
					__A       =					generated_responses
					__A       =					text






	def __eq__(			self   :					int				,A   :					str					):
					if not isinstance(A				,A					):
									return False
					if self.uuid == other.uuid:
									return True
					return (
					    self.new_user_input == other.new_user_input
					    and self.past_user_inputs == other.past_user_inputs
					    and self.generated_responses == other.generated_responses
					)






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					str				,A   :					bool = False					):
					if self.new_user_input:
									if overwrite:
													logger.warning(
													    f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
													    f'''with: "{text}".'''					)
													__A       =					text
									else:
													logger.warning(
													    f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
													    f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''					)
					else:
									__A       =					text






	def 							UpperCamelCase_    (			self   :					List[str]					):
					if self.new_user_input:
									self.past_user_inputs.append(self.new_user_input					)
					__A       =					None






	def 							UpperCamelCase_    (			self   :					int				,A   :					str					):
					self.generated_responses.append(A					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					for user_input, generated_response in zip(self.past_user_inputs				,self.generated_responses					):
									yield True, user_input
									yield False, generated_response
					if self.new_user_input:
									yield True, self.new_user_input







	def __repr__(			self   :					Tuple					):
					__A       =					f'''Conversation id: {self.uuid} \n'''
					for is_user, text in self.iter_texts():
									__A       =					"user" if is_user else "bot"
									output += f'''{name} >> {text} \n'''
					return output





@add_end_docstrings(
    __SCREAMING_SNAKE_CASE     ,     R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "     ,     )
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def __init__(			self   :					List[str]				,*A   :					int				,**A   :					Any					):
					super().__init__(*A				,**A					)
					if self.tokenizer.pad_token_id is None:
									__A       =					self.tokenizer.eos_token






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					Tuple=None				,A   :					int=None				,A   :					Union[str, Any]=None				,**A   :					Dict					):
					__A       =					{}
					__A       =					{}
					__A       =					{}

					if min_length_for_response is not None:
									__A       =					min_length_for_response
					if minimum_tokens is not None:
									__A       =					minimum_tokens

					if "max_length" in generate_kwargs:
									__A       =					generate_kwargs["max_length"]
									# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
					if clean_up_tokenization_spaces is not None:
									__A       =					clean_up_tokenization_spaces

					if generate_kwargs:
									forward_params.update(A					)
					return preprocess_params, forward_params, postprocess_params






	def __call__(			self   :					Optional[Any]				,A   :					Union[Conversation, List[Conversation]]				,A   :					Tuple=0				,**A   :					int					):
					__A       =					super().__call__(A				,num_workers=A				,**A					)
					if isinstance(A				,A					) and len(A					) == 1:
									return outputs[0]
					return outputs






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					Conversation				,A   :					Union[str, Any]=32					):
					if not isinstance(A				,A					):
									raise ValueError("ConversationalPipeline, expects Conversation as inputs"					)
					if conversation.new_user_input is None:
									raise ValueError(
									    f'''Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. '''
									    "Add user inputs with the conversation's `add_user_input` method"					)
					if hasattr(self.tokenizer				,"_build_conversation_input_ids"					):
									__A       =					self.tokenizer._build_conversation_input_ids(A					)
					else:
									# If the tokenizer cannot handle conversations, we default to only the old version
									__A       =					self._legacy_parse_and_tokenize(A					)

					if self.framework == "pt":
									__A       =					torch.LongTensor([input_ids]					)
					elif self.framework == "tf":
									__A       =					tf.constant([input_ids]					)
					return {"input_ids": input_ids, "conversation": conversation}






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					str				,A   :					Union[str, Any]=10				,**A   :					Union[str, Any]					):
					__A       =					generate_kwargs.get("max_length"				,self.model.config.max_length					)

					__A       =					model_inputs["input_ids"].shape[1]
					if max_length - minimum_tokens < n:
									logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''					)
									__A       =					max_length - minimum_tokens
									__A       =					model_inputs["input_ids"][:, -trim:]
									if "attention_mask" in model_inputs:
													__A       =					model_inputs["attention_mask"][:, -trim:]
					__A       =					model_inputs.pop("conversation"					)
					__A       =					max_length
					__A       =					self.model.generate(**A				,**A					)
					if self.model.config.is_encoder_decoder:
									__A       =					1
					else:
									__A       =					n
					return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






	def 							UpperCamelCase_    (			self   :					Any				,A   :					str				,A   :					Tuple=True					):
					__A       =					model_outputs["output_ids"]
					__A       =					self.tokenizer.decode(
					    output_ids[0]				,skip_special_tokens=A				,clean_up_tokenization_spaces=A				,)
					__A       =					model_outputs["conversation"]
					conversation.mark_processed()
					conversation.append_response(A					)
					return conversation







	def 							UpperCamelCase_    (			self   :					Any				,A   :					Conversation					):
					__A       =					self.tokenizer.eos_token_id
					__A       =					[]
					for is_user, text in conversation.iter_texts():
									if eos_token_id is not None:
													input_ids.extend(self.tokenizer.encode(A				,add_special_tokens=A					) + [eos_token_id]					)
									else:
													input_ids.extend(self.tokenizer.encode(A				,add_special_tokens=A					)					)

					if len(A					) > self.tokenizer.model_max_length:
									__A       =					input_ids[-self.tokenizer.model_max_length :]
					return input_ids





"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     else:
          raise ValueError(f'Task {task} not supported.'							)

     print(f'Building PyTorch model from configuration: {config}'							)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

     print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS 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."
 )
 _lowerCAmelCase							:   Tuple				=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )
