




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    lowercase_		:
						lowerCAmelCase__			      =PegasusConfig
						lowerCAmelCase__			      ={}
						lowerCAmelCase__			      ="gelu"



						def __init__(			self	:   Tuple					,							snake_case__	:   Union[str, Any]					,							snake_case__	:   List[Any]=13					,							snake_case__	:   int=7					,							snake_case__	:   Any=True					,							snake_case__	:   Any=False					,							snake_case__	:   Dict=99					,							snake_case__	:   Any=32					,							snake_case__	:   Dict=2					,							snake_case__	:   List[str]=4					,							snake_case__	:   Union[str, Any]=37					,							snake_case__	:   int=0.1					,							snake_case__	:   Any=0.1					,							snake_case__	:   Tuple=40					,							snake_case__	:   List[str]=2					,							snake_case__	:   Dict=1					,							snake_case__	:   Optional[int]=0					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							parent
								SCREAMING_SNAKE_CASE_					      =							batch_size
								SCREAMING_SNAKE_CASE_					      =							seq_length
								SCREAMING_SNAKE_CASE_					      =							is_training
								SCREAMING_SNAKE_CASE_					      =							use_labels
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							hidden_size
								SCREAMING_SNAKE_CASE_					      =							num_hidden_layers
								SCREAMING_SNAKE_CASE_					      =							num_attention_heads
								SCREAMING_SNAKE_CASE_					      =							intermediate_size

								SCREAMING_SNAKE_CASE_					      =							hidden_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							eos_token_id
								SCREAMING_SNAKE_CASE_					      =							pad_token_id
								SCREAMING_SNAKE_CASE_					      =							bos_token_id



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							ids_tensor([self.batch_size, self.seq_length - 1]					,							self.vocab_size  )
								SCREAMING_SNAKE_CASE_					      =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )					,							1  )
								SCREAMING_SNAKE_CASE_					      =							tf.concat([input_ids, eos_tensor]					,							axis=1  )

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

								SCREAMING_SNAKE_CASE_					      =							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					,							)
								SCREAMING_SNAKE_CASE_					      =							prepare_pegasus_inputs_dict(snake_case__					,							snake_case__					,							snake_case__  )
								return config, inputs_dict






						def 	__a					(			self	:   Tuple					,							snake_case__	:   Optional[Any]					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFPegasusModel(config=snake_case__  ).get_decoder()
								SCREAMING_SNAKE_CASE_					      =							inputs_dict['input_ids']

								SCREAMING_SNAKE_CASE_					      =							input_ids[:1, :]
								SCREAMING_SNAKE_CASE_					      =							inputs_dict['attention_mask'][:1, :]
								SCREAMING_SNAKE_CASE_					      =							inputs_dict['head_mask']
								SCREAMING_SNAKE_CASE_					      =							1

								# first forward pass
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							attention_mask=snake_case__					,							head_mask=snake_case__					,							use_cache=snake_case__  )

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							outputs.to_tuple()

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

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

								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							attention_mask=snake_case__  )[0]
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							attention_mask=snake_case__					,							past_key_values=snake_case__  )[0]

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

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

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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None    ,						)->	List[Any]:
		if attention_mask is None:
				SCREAMING_SNAKE_CASE_					      =							tf.cast(tf.math.not_equal(lowerCAmelCase    ,						config.pad_token_id )    ,						tf.inta )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE_					      =							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:
				SCREAMING_SNAKE_CASE_					      =							tf.ones((config.encoder_layers, config.encoder_attention_heads) )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE_					      =							tf.ones((config.decoder_layers, config.decoder_attention_heads) )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE_					      =							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    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
						lowerCAmelCase__			      =(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
						lowerCAmelCase__			      =(
						    {
						        "conversational": TFPegasusForConditionalGeneration,
						        "feature-extraction": TFPegasusModel,
						        "summarization": TFPegasusForConditionalGeneration,
						        "text2text-generation": TFPegasusForConditionalGeneration,
						        "translation": TFPegasusForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						lowerCAmelCase__			      =True
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFPegasusModelTester(self  )
								SCREAMING_SNAKE_CASE_					      =							ConfigTester(self					,							config_class=snake_case__  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								self.config_tester.run_common_tests()






						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*snake_case__  )



@require_sentencepiece
@require_tokenizers
@require_tf
class    lowercase_		(unittest.TestCase ):
						lowerCAmelCase__			      =[
						    " 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!\" ",
						]
						lowerCAmelCase__			      =[
						    "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
						lowerCAmelCase__			      ="google/pegasus-xsum"



						@cached_property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return AutoTokenizer.from_pretrained(self.model_name  )



						@cached_property
						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model



						def 	__a					(			self	:   Tuple					,							**snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.translate_src_text(**snake_case__  )
								assert self.expected_text == generated_words



						def 	__a					(			self	:   int					,							**snake_case__	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.tokenizer(self.src_text					,							**snake_case__					,							padding=snake_case__					,							return_tensors='tf'  )
								SCREAMING_SNAKE_CASE_					      =							self.model.generate(
								    model_inputs.input_ids					,							attention_mask=model_inputs.attention_mask					,							num_beams=2					,							use_cache=snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer.batch_decode(generated_ids.numpy()					,							skip_special_tokens=snake_case__  )
								return generated_words






						@slow
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= 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=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)