





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class    UpperCAmelCase			:
		UpperCAmelCase       :						Optional[int]			      =		PegasusConfig
		UpperCAmelCase       :						Any			      =		{}
		UpperCAmelCase       :						Union[str, Any]			      =		'''gelu'''




		def __init__(self   :						Optional[Any]				,      A__   :						Dict				,      A__   :						List[Any]=1_3				,      A__   :						List[Any]=7				,      A__   :						List[str]=True				,      A__   :						Optional[int]=False				,      A__   :						str=9_9				,      A__   :						List[Any]=3_2				,      A__   :						str=2				,      A__   :						str=4				,      A__   :						Tuple=3_7				,      A__   :						List[Any]=0.1				,      A__   :						Dict=0.1				,      A__   :						Dict=4_0				,      A__   :						Optional[int]=2				,      A__   :						List[str]=1				,      A__   :						str=0				,      )       ->			Dict:
						lowercase    =			parent
						lowercase    =			batch_size
						lowercase    =			seq_length
						lowercase    =			is_training
						lowercase    =			use_labels
						lowercase    =			vocab_size
						lowercase    =			hidden_size
						lowercase    =			num_hidden_layers
						lowercase    =			num_attention_heads
						lowercase    =			intermediate_size

						lowercase    =			hidden_dropout_prob
						lowercase    =			attention_probs_dropout_prob
						lowercase    =			max_position_embeddings
						lowercase    =			eos_token_id
						lowercase    =			pad_token_id
						lowercase    =			bos_token_id




		def 					UpperCAmelCase__ (self   :						Any					)       ->			List[Any]:
						lowercase    =			ids_tensor([self.batch_size, self.seq_length - 1]				,      self.vocab_size					)
						lowercase    =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)				,      1					)
						lowercase    =			tf.concat([input_ids, eos_tensor]				,      axis=1					)

						lowercase    =			ids_tensor([self.batch_size, self.seq_length]				,      self.vocab_size					)

						lowercase    =			self.config_cls(
						    vocab_size=self.vocab_size				,      d_model=self.hidden_size				,      encoder_layers=self.num_hidden_layers				,      decoder_layers=self.num_hidden_layers				,      encoder_attention_heads=self.num_attention_heads				,      decoder_attention_heads=self.num_attention_heads				,      encoder_ffn_dim=self.intermediate_size				,      decoder_ffn_dim=self.intermediate_size				,      dropout=self.hidden_dropout_prob				,      attention_dropout=self.attention_probs_dropout_prob				,      max_position_embeddings=self.max_position_embeddings				,      eos_token_ids=[2]				,      bos_token_id=self.bos_token_id				,      pad_token_id=self.pad_token_id				,      decoder_start_token_id=self.pad_token_id				,      **self.config_updates				,      )
						lowercase    =			prepare_pegasus_inputs_dict(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__					)
						return config, inputs_dict





		def 					UpperCAmelCase__ (self   :						Any				,      A__   :						Optional[Any]				,      A__   :						Union[str, Any]					)       ->			int:
						lowercase    =			TFPegasusModel(config=lowerCamelCase__					).get_decoder()
						lowercase    =			inputs_dict["""input_ids"""]

						lowercase    =			input_ids[:1, :]
						lowercase    =			inputs_dict["""attention_mask"""][:1, :]
						lowercase    =			inputs_dict["""head_mask"""]
						lowercase    =			1

						# first forward pass
						lowercase    =			model(lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      head_mask=lowerCamelCase__				,      use_cache=lowerCamelCase__					)

						lowercase    =			outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						lowercase    =			ids_tensor((self.batch_size, 3)				,      config.vocab_size					)
						lowercase    =			tf.cast(ids_tensor((self.batch_size, 3)				,      2					)				,      tf.inta					)

						# append to next input_ids and
						lowercase    =			tf.concat([input_ids, next_tokens]				,      axis=-1					)
						lowercase    =			tf.concat([attention_mask, next_attn_mask]				,      axis=-1					)

						lowercase    =			model(lowerCamelCase__				,      attention_mask=lowerCamelCase__					)[0]
						lowercase    =			model(lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      past_key_values=lowerCamelCase__					)[0]

						self.parent.assertEqual(next_tokens.shape[1]				,      output_from_past.shape[1]					)

						# select random slice
						lowercase    =			int(ids_tensor((1,)				,      output_from_past.shape[-1]					)					)
						lowercase    =			output_from_no_past[:, -3:, random_slice_idx]
						lowercase    =			output_from_past[:, :, random_slice_idx]

						# test that outputs are equal for slice
						tf.debugging.assert_near(lowerCamelCase__				,      lowerCamelCase__				,      rtol=1e-3					)







def 				UpperCAmelCase_     (						lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				):

				"""simple docstring"""






				if attention_mask is None:
								lowercase    =			tf.cast(tf.math.not_equal(lowerCamelCase__		,				config.pad_token_id				)		,				tf.inta				)
				if decoder_attention_mask is None:
								lowercase    =			tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape		,				dtype=tf.inta				),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,				config.pad_token_id				)		,				tf.inta				),
								    ]		,				axis=-1		,				)
				if head_mask is None:
								lowercase    =			tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
				if decoder_head_mask is None:
								lowercase    =			tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
				if cross_attn_head_mask is None:
								lowercase    =			tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}

@require_tf
class    UpperCAmelCase			(    __UpperCAmelCase      ,							__UpperCAmelCase      ,							unittest.TestCase	):
		UpperCAmelCase       :						List[str]			      =		(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
		UpperCAmelCase       :						int			      =		(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
		UpperCAmelCase       :						Tuple			      =		(
		    {
		        '''conversational''': TFPegasusForConditionalGeneration,
		        '''feature-extraction''': TFPegasusModel,
		        '''summarization''': TFPegasusForConditionalGeneration,
		        '''text2text-generation''': TFPegasusForConditionalGeneration,
		        '''translation''': TFPegasusForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		UpperCAmelCase       :						int			      =		True
		UpperCAmelCase       :						List[str]			      =		False
		UpperCAmelCase       :						Any			      =		False




		def 					UpperCAmelCase__ (self   :						Any					)       ->			Union[str, Any]:
						lowercase    =			TFPegasusModelTester(self					)
						lowercase    =			ConfigTester(self				,      config_class=lowerCamelCase__					)




		def 					UpperCAmelCase__ (self   :						Optional[Any]					)       ->			Union[str, Any]:
						self.config_tester.run_common_tests()





		def 					UpperCAmelCase__ (self   :						str					)       ->			Any:
						lowercase    =			self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__					)




@require_sentencepiece
@require_tokenizers
@require_tf
class    UpperCAmelCase			(    unittest.TestCase	):
		UpperCAmelCase       :						Any			      =		[
		    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
		    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
		]
		UpperCAmelCase       :						Dict			      =		[
		    '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
		    ''' reduce the risk of wildfires.''',
		    '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
		]  # differs slightly from pytorch, likely due to numerical differences in linear layers
		UpperCAmelCase       :						Any			      =		'''google/pegasus-xsum'''




		@cached_property
		def 					UpperCAmelCase__ (self   :						Dict					)       ->			Optional[Any]:
						return AutoTokenizer.from_pretrained(self.model_name					)




		@cached_property
		def 					UpperCAmelCase__ (self   :						Optional[Any]					)       ->			Union[str, Any]:
						lowercase    =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
						return model




		def 					UpperCAmelCase__ (self   :						List[str]				,      **A__   :						List[Any]					)       ->			List[Any]:
						lowercase    =			self.translate_src_text(**lowerCamelCase__					)
						assert self.expected_text == generated_words




		def 					UpperCAmelCase__ (self   :						List[str]				,      **A__   :						Union[str, Any]					)       ->			Tuple:
						lowercase    =			self.tokenizer(self.src_text				,      **lowerCamelCase__				,      padding=lowerCamelCase__				,      return_tensors="tf"					)
						lowercase    =			self.model.generate(
						    model_inputs.input_ids				,      attention_mask=model_inputs.attention_mask				,      num_beams=2				,      use_cache=lowerCamelCase__				,      )
						lowercase    =			self.tokenizer.batch_decode(generated_ids.numpy()				,      skip_special_tokens=lowerCamelCase__					)
						return generated_words





		@slow
		def 					UpperCAmelCase__ (self   :						List[Any]					)       ->			Tuple:
						self._assert_generated_batch_equal_expected()








"""simple docstring"""


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()
__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					'Hello world! cécé herlolip'


def    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							bool  ):
 lowercase__       :       int				=						FairseqRobertaModel.from_pretrained(lowerCamelCase__  )
 roberta.eval()  # disable dropout
 lowercase__       :       Tuple				=						roberta.model.encoder.sentence_encoder
 lowercase__       :       Tuple				=						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=5_14       ,		type_vocab_size=1       ,		layer_norm_eps=1E-5       ,		)
 if classification_head:
  lowercase__       :       Any				=						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

 print("""Our RoBERTa config:"""       ,		lowerCamelCase__  )

 lowercase__       :       List[Any]				=						XLMRobertaXLForSequenceClassification(lowerCamelCase__  ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase__  )
 model.eval()

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

 lowercase__       :       int				=						roberta_sent_encoder.layer_norm.weight
 lowercase__       :       List[Any]				=						roberta_sent_encoder.layer_norm.bias

 for i in range(config.num_hidden_layers  ):
  # Encoder: start of layer
  lowercase__       :       BertLayer				=						model.roberta.encoder.layer[i]
  lowercase__       :       TransformerSentenceEncoderLayer				=						roberta_sent_encoder.layers[i]

  lowercase__       :       RobertaAttention				=						layer.attention
  lowercase__       :       str				=						roberta_layer.self_attn_layer_norm.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn_layer_norm.bias

  # self attention
  lowercase__       :       BertSelfAttention				=						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)  )
  )

  lowercase__       :       Optional[Any]				=						roberta_layer.self_attn.q_proj.weight
  lowercase__       :       str				=						roberta_layer.self_attn.q_proj.bias
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.weight
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.bias
  lowercase__       :       int				=						roberta_layer.self_attn.v_proj.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn.v_proj.bias

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

  # this one is final layer norm
  lowercase__       :       Optional[Any]				=						roberta_layer.final_layer_norm.weight
  lowercase__       :       Any				=						roberta_layer.final_layer_norm.bias

  # intermediate
  lowercase__       :       BertIntermediate				=						layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Dict				=						roberta_layer.fca.weight
  lowercase__       :       Any				=						roberta_layer.fca.bias

  # output
  lowercase__       :       BertOutput				=						layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Union[str, Any]				=						roberta_layer.fca.weight
  lowercase__       :       Optional[Any]				=						roberta_layer.fca.bias
  # end of layer

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

 # Let's check that we get the same results.
 lowercase__       :       torch.Tensor				=						roberta.encode(lowerCamelCase__  ).unsqueeze(0  )  # batch of size 1

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

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


if __name__ == "__main__":
   __snake_case    					=					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.'
   )
   __snake_case    					=					parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )