



import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
						raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
A__    :    Any        =   logging.get_logger(__name__)

A__    :    Union[str, Any]        =   'Hello world! cécé herlolip'


def      UpperCamelCase(    __UpperCamelCase				:						str	,__UpperCamelCase				:						str	,__UpperCamelCase				:						bool					):
		lowerCAmelCase_     :	List[str]							   =					FairseqRobertaModel.from_pretrained(__UpperCamelCase					)
		roberta.eval()  # disable dropout
		lowerCAmelCase_     :	Any							   =					roberta.model.encoder.sentence_encoder
		lowerCAmelCase_     :	Union[str, Any]							   =					XLMRobertaConfig(
		    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,hidden_size=roberta.cfg.model.encoder_embed_dim	,num_hidden_layers=roberta.cfg.model.encoder_layers	,num_attention_heads=roberta.cfg.model.encoder_attention_heads	,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim	,max_position_embeddings=514	,type_vocab_size=1	,layer_norm_eps=1e-5	,)
		if classification_head:
				lowerCAmelCase_     :	Dict							   =					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

		print('''Our RoBERTa config:'''	,__UpperCamelCase					)

		lowerCAmelCase_     :	Optional[int]							   =					XLMRobertaXLForSequenceClassification(__UpperCamelCase					) if classification_head else XLMRobertaXLForMaskedLM(__UpperCamelCase					)
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		lowerCAmelCase_     :	str							   =					roberta_sent_encoder.embed_tokens.weight
		lowerCAmelCase_     :	str							   =					roberta_sent_encoder.embed_positions.weight
		lowerCAmelCase_     :	List[Any]							   =					torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.

		lowerCAmelCase_     :	List[str]							   =					roberta_sent_encoder.layer_norm.weight
		lowerCAmelCase_     :	Union[str, Any]							   =					roberta_sent_encoder.layer_norm.bias

		for i in range(config.num_hidden_layers					):
				# Encoder: start of layer
				lowerCAmelCase_     :	str							   =					model.roberta.encoder.layer[i]
				lowerCAmelCase_     :	Dict							   =					roberta_sent_encoder.layers[i]

				lowerCAmelCase_     :	Dict							   =					layer.attention
				lowerCAmelCase_     :	Optional[int]							   =					roberta_layer.self_attn_layer_norm.weight
				lowerCAmelCase_     :	List[Any]							   =					roberta_layer.self_attn_layer_norm.bias

				# self attention
				lowerCAmelCase_     :	Optional[Any]							   =					layer.attention.self
				assert (
				    roberta_layer.self_attn.k_proj.weight.data.shape
				    == roberta_layer.self_attn.q_proj.weight.data.shape
				    == roberta_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size)					)
				)

				lowerCAmelCase_     :	Any							   =					roberta_layer.self_attn.q_proj.weight
				lowerCAmelCase_     :	List[Any]							   =					roberta_layer.self_attn.q_proj.bias
				lowerCAmelCase_     :	str							   =					roberta_layer.self_attn.k_proj.weight
				lowerCAmelCase_     :	Tuple							   =					roberta_layer.self_attn.k_proj.bias
				lowerCAmelCase_     :	int							   =					roberta_layer.self_attn.v_proj.weight
				lowerCAmelCase_     :	Any							   =					roberta_layer.self_attn.v_proj.bias

				# self-attention output
				lowerCAmelCase_     :	Optional[Any]							   =					layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				lowerCAmelCase_     :	Optional[int]							   =					roberta_layer.self_attn.out_proj.weight
				lowerCAmelCase_     :	List[str]							   =					roberta_layer.self_attn.out_proj.bias

				# this one is final layer norm
				lowerCAmelCase_     :	str							   =					roberta_layer.final_layer_norm.weight
				lowerCAmelCase_     :	Tuple							   =					roberta_layer.final_layer_norm.bias

				# intermediate
				lowerCAmelCase_     :	List[str]							   =					layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				lowerCAmelCase_     :	int							   =					roberta_layer.fca.weight
				lowerCAmelCase_     :	int							   =					roberta_layer.fca.bias

				# output
				lowerCAmelCase_     :	str							   =					layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				lowerCAmelCase_     :	str							   =					roberta_layer.fca.weight
				lowerCAmelCase_     :	Optional[int]							   =					roberta_layer.fca.bias
				# end of layer

		if classification_head:
				lowerCAmelCase_     :	str							   =					roberta.model.classification_heads['''mnli'''].dense.weight
				lowerCAmelCase_     :	List[Any]							   =					roberta.model.classification_heads['''mnli'''].dense.bias
				lowerCAmelCase_     :	str							   =					roberta.model.classification_heads['''mnli'''].out_proj.weight
				lowerCAmelCase_     :	Optional[Any]							   =					roberta.model.classification_heads['''mnli'''].out_proj.bias
		else:
				# LM Head
				lowerCAmelCase_     :	Optional[int]							   =					roberta.model.encoder.lm_head.dense.weight
				lowerCAmelCase_     :	List[Any]							   =					roberta.model.encoder.lm_head.dense.bias
				lowerCAmelCase_     :	List[Any]							   =					roberta.model.encoder.lm_head.layer_norm.weight
				lowerCAmelCase_     :	List[Any]							   =					roberta.model.encoder.lm_head.layer_norm.bias
				lowerCAmelCase_     :	str							   =					roberta.model.encoder.lm_head.weight
				lowerCAmelCase_     :	Union[str, Any]							   =					roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		lowerCAmelCase_     :	Tuple							   =					roberta.encode(__UpperCamelCase					).unsqueeze(0					)  # batch of size 1

		lowerCAmelCase_     :	Dict							   =					model(__UpperCamelCase					)[0]
		if classification_head:
				lowerCAmelCase_     :	List[str]							   =					roberta.model.classification_heads['''mnli'''](roberta.extract_features(__UpperCamelCase					)					)
		else:
				lowerCAmelCase_     :	List[Any]							   =					roberta.model(__UpperCamelCase					)[0]
		print(our_output.shape	,their_output.shape					)
		lowerCAmelCase_     :	Tuple							   =					torch.max(torch.abs(our_output - their_output					)					).item()
		print(f"""max_absolute_diff = {max_absolute_diff}"""					)  # ~ 1e-7
		lowerCAmelCase_     :	List[Any]							   =					torch.allclose(__UpperCamelCase	,__UpperCamelCase	,atol=1e-3					)
		print('''Do both models output the same tensors?'''	,'''🔥''' if success else '''💩'''					)
		if not success:
				raise Exception('''Something went wRoNg'''					)

		pathlib.Path(__UpperCamelCase					).mkdir(parents=__UpperCamelCase	,exist_ok=__UpperCamelCase					)
		print(f"""Saving model to {pytorch_dump_folder_path}"""					)
		model.save_pretrained(__UpperCamelCase					)


if __name__ == "__main__":
						A__    :    Union[str, Any]        =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
						)
						A__    :    List[str]        =   parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)







'''simple docstring'''
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


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_ = None						,				A_ = None						,				A_=None						,				A_=None     )->		Optional[Any]:







											'''simple docstring'''






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

											UpperCamelCase   			=     conversation_id
											UpperCamelCase   			=     past_user_inputs
											UpperCamelCase   			=     generated_responses
											UpperCamelCase   			=     text







				def __eq__(       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											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						,				A_						,				A_ = 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}".'''     )
																									UpperCamelCase   			=     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:
																		UpperCamelCase   			=     text







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											self.generated_responses.append(A_     )







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''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:







											'''simple docstring'''






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


@add_end_docstrings(
    snake_case_							,       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 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				*A_						,				**A_     )->		Any:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_=None						,				A_=None						,				A_=None						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}

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

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

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







				def __call__(       self						,				A_						,				A_=0						,				**A_     )->		Any:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=32     )->		Dict[str, Any]:







											'''simple docstring'''






											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'     ):
																		UpperCamelCase   			=     self.tokenizer._build_conversation_input_ids(A_     )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		UpperCamelCase   			=     self._legacy_parse_and_tokenize(A_     )

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=10						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     generate_kwargs.get('max_length'						,				self.model.config.max_length     )

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=True     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     model_outputs['output_ids']
											UpperCamelCase   			=     self.tokenizer.decode(
											    output_ids[0]						,				skip_special_tokens=A_						,				clean_up_tokenization_spaces=A_						,				)
											UpperCamelCase   			=     model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(A_     )
											return conversation







				def 						UpperCAmelCase_    (       self						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer.eos_token_id
											UpperCamelCase   			=     []
											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:
																		UpperCamelCase   			=     input_ids[-self.tokenizer.model_max_length :]
											return input_ids




