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   _lowerCAmelCase		:
							_UpperCAmelCase						    =     PegasusConfig
							_UpperCAmelCase						    =     {}
							_UpperCAmelCase						    =     'gelu'







							def __init__(				self     :    Optional[Any]	,					__snake_case     :    Tuple	,					__snake_case     :    Tuple=13	,					__snake_case     :    Optional[Any]=7	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=False	,					__snake_case     :    Dict=99	,					__snake_case     :    List[str]=32	,					__snake_case     :    List[str]=2	,					__snake_case     :    Any=4	,					__snake_case     :    Union[str, Any]=37	,					__snake_case     :    Dict=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Optional[Any]=40	,					__snake_case     :    str=2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Union[str, Any]=0	,					):
										lowerCamelCase						:str						  =     parent
										lowerCamelCase						:Any						  =     batch_size
										lowerCamelCase						:Tuple						  =     seq_length
										lowerCamelCase						:Optional[int]						  =     is_training
										lowerCamelCase						:Optional[Any]						  =     use_labels
										lowerCamelCase						:int						  =     vocab_size
										lowerCamelCase						:Optional[int]						  =     hidden_size
										lowerCamelCase						:Any						  =     num_hidden_layers
										lowerCamelCase						:Optional[int]						  =     num_attention_heads
										lowerCamelCase						:List[str]						  =     intermediate_size

										lowerCamelCase						:Any						  =     hidden_dropout_prob
										lowerCamelCase						:int						  =     attention_probs_dropout_prob
										lowerCamelCase						:str						  =     max_position_embeddings
										lowerCamelCase						:Union[str, Any]						  =     eos_token_id
										lowerCamelCase						:List[str]						  =     pad_token_id
										lowerCamelCase						:Union[str, Any]						  =     bos_token_id







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[int]						  =     ids_tensor([self.batch_size, self.seq_length - 1]	,					self.vocab_size		)
										lowerCamelCase						:str						  =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)	,					1		)
										lowerCamelCase						:List[Any]						  =     tf.concat([input_ids, eos_tensor]	,					axis=1		)

										lowerCamelCase						:str						  =     ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size		)

										lowerCamelCase						:Optional[int]						  =     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	,					)
										lowerCamelCase						:int						  =     prepare_pegasus_inputs_dict(__snake_case	,					__snake_case	,					__snake_case		)
										return config, inputs_dict







							def 				snake_case  (				self     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    Dict		):
										lowerCamelCase						:List[Any]						  =     TFPegasusModel(config=__snake_case		).get_decoder()
										lowerCamelCase						:Optional[int]						  =     inputs_dict['''input_ids''']

										lowerCamelCase						:str						  =     input_ids[:1, :]
										lowerCamelCase						:Any						  =     inputs_dict['''attention_mask'''][:1, :]
										lowerCamelCase						:Tuple						  =     inputs_dict['''head_mask''']
										lowerCamelCase						:str						  =     1

										# first forward pass
										lowerCamelCase						:Optional[Any]						  =     model(__snake_case	,					attention_mask=__snake_case	,					head_mask=__snake_case	,					use_cache=__snake_case		)

										lowerCamelCase  ,    lowerCamelCase						:Any						  =     outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										lowerCamelCase						:int						  =     ids_tensor((self.batch_size, 3)	,					config.vocab_size		)
										lowerCamelCase						:Optional[int]						  =     tf.cast(ids_tensor((self.batch_size, 3)	,					2		)	,					tf.inta		)

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

										lowerCamelCase						:int						  =     model(__snake_case	,					attention_mask=__snake_case		)[0]
										lowerCamelCase						:int						  =     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
										lowerCamelCase						:List[str]						  =     int(ids_tensor((1,)	,					output_from_past.shape[-1]		)		)
										lowerCamelCase						:Optional[Any]						  =     output_from_no_past[:, -3:, random_slice_idx]
										lowerCamelCase						:str						  =     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  _lowerCamelCase					(   a_				: Dict						,			a_				: Optional[Any]						,			a_				: str						,			a_				: int=None						,			a_				: str=None						,			a_				: Any=None						,			a_				: Tuple=None						,			a_				: Union[str, Any]=None						,			):
			if attention_mask is None:
						lowerCamelCase						:Optional[int]						  =     tf.cast(tf.math.not_equal(a_						,			config.pad_token_id)						,			tf.inta)
			if decoder_attention_mask is None:
						lowerCamelCase						:int						  =     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:
						lowerCamelCase						:List[str]						  =     tf.ones((config.encoder_layers, config.encoder_attention_heads))
			if decoder_head_mask is None:
						lowerCamelCase						:Optional[Any]						  =     tf.ones((config.decoder_layers, config.decoder_attention_heads))
			if cross_attn_head_mask is None:
						lowerCamelCase						:Optional[Any]						  =     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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							_UpperCAmelCase						    =     (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							_UpperCAmelCase						    =     (
							    {
							        'conversational': TFPegasusForConditionalGeneration,
							        'feature-extraction': TFPegasusModel,
							        'summarization': TFPegasusForConditionalGeneration,
							        'text2text-generation': TFPegasusForConditionalGeneration,
							        'translation': TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Any						  =     TFPegasusModelTester(self		)
										lowerCamelCase						:Union[str, Any]						  =     ConfigTester(self	,					config_class=__snake_case		)







							def 				snake_case  (				self     :    Any		):
										self.config_tester.run_common_tests()







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:str						  =     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   _lowerCAmelCase		(     unittest.TestCase				):
							_UpperCAmelCase						    =     [
							    ' 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						    =     [
							    '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						    =     'google/pegasus-xsum'







							@cached_property
							def 				snake_case  (				self     :    List[str]		):
										return AutoTokenizer.from_pretrained(self.model_name		)







							@cached_property
							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Tuple						  =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
										return model







							def 				snake_case  (				self     :    int	,					**__snake_case     :    Optional[int]		):
										lowerCamelCase						:List[str]						  =     self.translate_src_text(**__snake_case		)
										assert self.expected_text == generated_words







							def 				snake_case  (				self     :    Any	,					**__snake_case     :    Union[str, Any]		):
										lowerCamelCase						:Tuple						  =     self.tokenizer(self.src_text	,					**__snake_case	,					padding=__snake_case	,					return_tensors='''tf'''		)
										lowerCamelCase						:List[Any]						  =     self.model.generate(
										    model_inputs.input_ids	,					attention_mask=model_inputs.attention_mask	,					num_beams=2	,					use_cache=__snake_case	,					)
										lowerCamelCase						:Tuple						  =     self.tokenizer.batch_decode(generated_ids.numpy()	,					skip_special_tokens=__snake_case		)
										return generated_words







							@slow
							def 				snake_case  (				self     :    Any		):
										self._assert_generated_batch_equal_expected()




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
			raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
			raise Exception("""requires fairseq < v2""")

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

A__           =							"""Hello, World!"""
A__           =							"""en_XX"""





def  _lowerCamelCase					(   a_				: str						,			a_				: str						,			a_				: bool):
			lowerCamelCase						:int						  =     Path('''data_bin''')
			lowerCamelCase						:Union[str, Any]						  =     FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(a_).parent)						,			checkpoint_file=Path(a_).name						,			_name='''xmod_base'''						,			arch='''xmod_base'''						,			task='''multilingual_masked_lm'''						,			data_name_or_path=str(a_)						,			bpe='''sentencepiece'''						,			sentencepiece_model=str(Path(a_).parent / '''sentencepiece.bpe.model''')						,			src_dict=str(data_dir / '''dict.txt''')						,			)
			xmod.eval()  # disable dropout
			print(a_)

			lowerCamelCase						:Any						  =     xmod.model.encoder.sentence_encoder
			lowerCamelCase						:List[str]						  =     XmodConfig(
			    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings						,			hidden_size=xmod.cfg.model.encoder_embed_dim						,			num_hidden_layers=xmod.cfg.model.encoder_layers						,			num_attention_heads=xmod.cfg.model.encoder_attention_heads						,			intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim						,			max_position_embeddings=5_14						,			type_vocab_size=1						,			layer_norm_eps=1e-5						,			pre_norm=xmod.cfg.model.encoder_normalize_before						,			adapter_reduction_factor=getattr(xmod.cfg.model						,			'''bottleneck'''						,			2)						,			adapter_layer_norm=xmod.cfg.model.adapter_layer_norm						,			adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm						,			ln_before_adapter=xmod.cfg.model.ln_before_adapter						,			languages=xmod.cfg.model.languages						,			)
			if classification_head:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

			print('''Our X-MOD config:'''						,			a_)

			lowerCamelCase						:List[Any]						  =     XmodForSequenceClassification(a_) if classification_head else XmodForMaskedLM(a_)
			model.eval()

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

			lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layernorm_embedding.weight
			lowerCamelCase						:Optional[int]						  =     xmod_sent_encoder.layernorm_embedding.bias

			for i in range(config.num_hidden_layers):
						# Encoder: start of layer
						lowerCamelCase						:Union[str, Any]						  =     model.roberta.encoder.layer[i]
						lowerCamelCase						:List[str]						  =     xmod_sent_encoder.layers[i]

						# self attention
						lowerCamelCase						:Optional[int]						  =     layer.attention.self
						if not (
						    xmod_layer.self_attn.k_proj.weight.data.shape
						    == xmod_layer.self_attn.q_proj.weight.data.shape
						    == xmod_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size))
						):
									raise AssertionError('''Dimensions of self-attention weights do not match.''')

						lowerCamelCase						:Optional[int]						  =     xmod_layer.self_attn.q_proj.weight
						lowerCamelCase						:List[str]						  =     xmod_layer.self_attn.q_proj.bias
						lowerCamelCase						:str						  =     xmod_layer.self_attn.k_proj.weight
						lowerCamelCase						:Optional[Any]						  =     xmod_layer.self_attn.k_proj.bias
						lowerCamelCase						:Dict						  =     xmod_layer.self_attn.v_proj.weight
						lowerCamelCase						:Optional[int]						  =     xmod_layer.self_attn.v_proj.bias

						# self-attention output
						lowerCamelCase						:Optional[int]						  =     layer.attention.output
						if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
									raise AssertionError('''Dimensions of self-attention output weights do not match.''')
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn.out_proj.weight
						lowerCamelCase						:Union[str, Any]						  =     xmod_layer.self_attn.out_proj.bias
						lowerCamelCase						:str						  =     xmod_layer.self_attn_layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn_layer_norm.bias

						# intermediate
						lowerCamelCase						:Optional[int]						  =     layer.intermediate
						if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError('''Dimensions of intermediate weights do not match.''')
						lowerCamelCase						:int						  =     xmod_layer.fca.weight
						lowerCamelCase						:Union[str, Any]						  =     xmod_layer.fca.bias

						# output
						lowerCamelCase						:List[str]						  =     layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError('''Dimensions of feed-forward weights do not match.''')
						lowerCamelCase						:str						  =     xmod_layer.fca.weight
						lowerCamelCase						:int						  =     xmod_layer.fca.bias
						lowerCamelCase						:List[Any]						  =     xmod_layer.final_layer_norm.weight
						lowerCamelCase						:List[str]						  =     xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									lowerCamelCase						:List[str]						  =     xmod_layer.adapter_layer_norm.weight
									lowerCamelCase						:int						  =     xmod_layer.adapter_layer_norm.bias

						if sorted(bert_output.adapter_modules.keys()) != sorted(xmod_layer.adapter_modules.keys()):
									raise AssertionError('''Lists of language adapters do not match.''')
						for lang_code, adapter in xmod_layer.adapter_modules.items():
									lowerCamelCase						:Optional[int]						  =     bert_output.adapter_modules[lang_code]
									lowerCamelCase						:Dict						  =     xmod_layer.adapter_modules[lang_code]
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.weight
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.bias
									lowerCamelCase						:Dict						  =     from_adapter.fca.weight
									lowerCamelCase						:Optional[Any]						  =     from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						lowerCamelCase						:Dict						  =     xmod_sent_encoder.layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layer_norm.bias

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

			# Let's check that we get the same results.
			lowerCamelCase						:str						  =     xmod.encode(a_).unsqueeze(0)  # batch of size 1
			model.roberta.set_default_language(a_)

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

			Path(a_).mkdir(parents=a_						,			exist_ok=a_)
			print(F"Saving model to {pytorch_dump_folder_path}")
			model.save_pretrained(a_)


if __name__ == "__main__":
			A__           =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--xmod_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__           =							parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



