


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


A			:						int        =  logging.get_logger(__name__)



class 				A       :
					'''simple docstring'''






					def __init__(self       :		Union[str, Any] ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		uuid.UUID = None ,   _UpperCAmelCase       :		List[str]=None ,   _UpperCAmelCase       :		Optional[Any]=None						)		->  Optional[Any]:




							"""simple docstring"""


							if not conversation_id:
									lowercase__	     =						uuid.uuida()
							if past_user_inputs is None:
									lowercase__	     =						[]
							if generated_responses is None:
									lowercase__	     =						[]

							lowercase__	     =						conversation_id
							lowercase__	     =						past_user_inputs
							lowercase__	     =						generated_responses
							lowercase__	     =						text

					def __eq__(self       :		Tuple ,   _UpperCAmelCase       :		str						)		->  Optional[Any]:




							"""simple docstring"""


							if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
									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     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		bool = False						)		->  int:




							"""simple docstring"""


							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}".'''						)
											lowercase__	     =						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:
									lowercase__	     =						text

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Dict:




							"""simple docstring"""


							if self.new_user_input:
									self.past_user_inputs.append(self.new_user_input						)
							lowercase__	     =						None

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		str						)		->  List[Any]:




							"""simple docstring"""


							self.generated_responses.append(_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Dict:




							"""simple docstring"""


							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       :		Any						)		->  List[Any]:




							"""simple docstring"""


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






@add_end_docstrings(
    UpperCAmelCase__					,    r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''					,    )
class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Tuple ,   *_UpperCAmelCase       :		int ,   **_UpperCAmelCase       :		List[str]						)		->  Any:




							"""simple docstring"""


							super().__init__(*_UpperCAmelCase ,   **_UpperCAmelCase						)
							if self.tokenizer.pad_token_id is None:
									lowercase__	     =						self.tokenizer.eos_token

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		int=None ,   _UpperCAmelCase       :		Tuple=None ,   _UpperCAmelCase       :		int=None ,   **_UpperCAmelCase       :		str						)		->  int:




							"""simple docstring"""


							lowercase__	     =						{}
							lowercase__	     =						{}
							lowercase__	     =						{}

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

							if "max_length" in generate_kwargs:
									lowercase__	     =						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:
									lowercase__	     =						clean_up_tokenization_spaces

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

					def __call__(self       :		List[str] ,   _UpperCAmelCase       :		Union[Conversation, List[Conversation]] ,   _UpperCAmelCase       :		Dict=0 ,   **_UpperCAmelCase       :		Any						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						super().__call__(_UpperCAmelCase ,   num_workers=_UpperCAmelCase ,   **_UpperCAmelCase						)
							if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) and len(_UpperCAmelCase						) == 1:
									return outputs[0]
							return outputs

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Conversation ,   _UpperCAmelCase       :		Any=32						)		->  Dict[str, Any]:




							"""simple docstring"""


							if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
									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"""						):
									lowercase__	     =						self.tokenizer._build_conversation_input_ids(_UpperCAmelCase						)
							else:
									# If the tokenizer cannot handle conversations, we default to only the old version
									lowercase__	     =						self._legacy_parse_and_tokenize(_UpperCAmelCase						)

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

					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[Any]=10 ,   **_UpperCAmelCase       :		List[Any]						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						generate_kwargs.get("""max_length""" ,   self.model.config.max_length						)

							lowercase__	     =						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})'''						)
									lowercase__	     =						max_length - minimum_tokens
									lowercase__	     =						model_inputs["""input_ids"""][:, -trim:]
									if "attention_mask" in model_inputs:
											lowercase__	     =						model_inputs["""attention_mask"""][:, -trim:]
							lowercase__	     =						model_inputs.pop("""conversation"""						)
							lowercase__	     =						max_length
							lowercase__	     =						self.model.generate(**_UpperCAmelCase ,   **_UpperCAmelCase						)
							if self.model.config.is_encoder_decoder:
									lowercase__	     =						1
							else:
									lowercase__	     =						n
							return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any=True						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						model_outputs["""output_ids"""]
							lowercase__	     =						self.tokenizer.decode(
							    output_ids[0] ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase ,   )
							lowercase__	     =						model_outputs["""conversation"""]
							conversation.mark_processed()
							conversation.append_response(_UpperCAmelCase						)
							return conversation


					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		Conversation						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						self.tokenizer.eos_token_id
							lowercase__	     =						[]
							for is_user, text in conversation.iter_texts():
									if eos_token_id is not None:
											input_ids.extend(self.tokenizer.encode(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						) + [eos_token_id]						)
									else:
											input_ids.extend(self.tokenizer.encode(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						)						)

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





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

		if finetuned:
				lowercase__	     =						make_linear_from_emb(model.model.shared	)

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
	)
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--hf_config',
	    default='facebook/mbart-large-cc25',
	    type=str,
	    help='Which huggingface architecture to use: mbart-large',
	)
	parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)

