


"""simple docstring"""






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


UpperCamelCase__							:Dict       =  -1 if torch_device == """cpu""" else 0






@is_pipeline_test
class     A(     unittest.TestCase		):

		"""simple docstring"""





		A         =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		A         =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  SummarizationPipeline(model=SCREAMING_SNAKE_CASE__				,							tokenizer=SCREAMING_SNAKE_CASE__      )
					return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  summarizer.model

					_UpperCamelCase				:Optional[int]				    =  summarizer('''(CNN)The Palestinian Authority officially became'''      )
					self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{'''summary_text''': ANY(SCREAMING_SNAKE_CASE__      )}]      )

					_UpperCamelCase				:Optional[Any]				    =  summarizer(
					    '''(CNN)The Palestinian Authority officially became '''				,							num_beams=2				,							min_length=2				,							max_length=5				,							)
					self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{'''summary_text''': ANY(SCREAMING_SNAKE_CASE__      )}]      )

					# Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
					_UpperCamelCase				:str				    =  [
					    '''SwitchTransformersConfig''',
					    '''T5Config''',
					    '''LongT5Config''',
					    '''LEDConfig''',
					    '''PegasusXConfig''',
					    '''FSMTConfig''',
					    '''M2M100Config''',
					    '''ProphetNetConfig''',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
					]
					if model.config.__class__.__name__ not in model_can_handle_longer_seq:
								# Too long and exception is expected.
								# For TF models, if the weights are initialized in GPU context, we won't get expected index error from
								# the embedding layer.
								if not (
								    isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
								    and get_gpu_count() > 0
								    and len(summarizer.model.trainable_weights      ) > 0
								):
											with self.assertRaises(SCREAMING_SNAKE_CASE__      ):
														_UpperCamelCase				:Any				    =  summarizer('''This ''' * 10_00      )
					_UpperCamelCase				:Tuple				    =  summarizer('''This ''' * 10_00				,							truncation=TruncationStrategy.ONLY_FIRST      )
		@require_torch
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  pipeline(task='''summarization'''				,							model='''sshleifer/tiny-mbart'''				,							framework='''pt'''      )
					_UpperCamelCase				:Tuple				    =  summarizer('''This is a small test'''      )
					self.assertEqual(
					    SCREAMING_SNAKE_CASE__				,							[
					        {
					            '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
					        }
					    ]				,							)
		@require_tf
		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  pipeline(task='''summarization'''				,							model='''sshleifer/tiny-mbart'''				,							framework='''tf'''      )
					_UpperCamelCase				:Union[str, Any]				    =  summarizer('''This is a small test'''      )
					self.assertEqual(
					    SCREAMING_SNAKE_CASE__				,							[
					        {
					            '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
					        }
					    ]				,							)





		@require_torch
		@slow
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  pipeline(task='''summarization'''				,							device=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Dict				    =  (
					    ''' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'''
					    ''' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'''
					    ''' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'''
					    ''' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'''
					    ''' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'''
					    ''' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'''
					    ''' situation in Palestinian territories, paving the way for possible war crimes investigations against'''
					    ''' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'''
					    ''' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'''
					    ''' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'''
					    ''' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'''
					    ''' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'''
					    ''' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'''
					    ''' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'''
					    ''' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'''
					    ''' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'''
					    ''' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'''
					    ''' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'''
					    ''' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'''
					    ''' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'''
					    ''' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'''
					    ''' decision to join a treaty to which over 100 countries around the world are members." In January, when'''
					    ''' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'''
					    ''' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'''
					    ''' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'''
					    ''' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'''
					    ''' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'''
					    ''' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'''
					    ''' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'''
					    ''' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'''
					    ''' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'''
					    ''' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'''
					    ''' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'''
					    ''' will include alleged war crimes committed since June. The International Criminal Court was set up in'''
					    ''' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'''
					    ''' and Faith Karimi contributed to this report.'''
					)
					_UpperCamelCase				:List[Any]				    =  (
					    ''' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'''
					    ''' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'''
					    ''' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'''
					    ''' says governments seeking to penalize Palestine should end pressure .'''
					)
					_UpperCamelCase				:int				    =  summarizer(SCREAMING_SNAKE_CASE__      )
					self.assertEqual(result[0]['''summary_text''']				,							SCREAMING_SNAKE_CASE__      )





"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
					from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
					raise ImportError("""You need to install the version of fairseq from the Mega repo!""")






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:List[str]				    =  mega_args
					_UpperCamelCase				:Dict				    =  nn.Embedding(SCREAMING_SNAKE_CASE__				,							self.mega_args.encoder_embed_dim      )
					_UpperCamelCase				:Optional[Any]				    =  nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(SCREAMING_SNAKE_CASE__      )]      )
					_UpperCamelCase				:Tuple				    =  depth





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  Dict:

					"""simple docstring"""



					if batch_first:
								_UpperCamelCase				:Tuple				    =  input_ids.T

					# to make things more confusing, Mega expects the attention mask to
					# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
					# which is the opposite of what HF returns
					if ignore_mask_value == 0:
								_UpperCamelCase				:str				    =  1 - attention_mask

					# get token embeddings from IDs
					_UpperCamelCase				:Union[str, Any]				    =  self.embedding_layer(SCREAMING_SNAKE_CASE__      )

					# pass through the Mega layers
					# input is (time, batch, encoder dim) and output is the same
					for encoder in self.encoders:
								_UpperCamelCase				:Union[str, Any]				    =  encoder(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					# return according to the shape specified
					if batch_first:
								# (T, B, H) --> (B, T, H)
								return torch.transpose(SCREAMING_SNAKE_CASE__				,							0				,							1      )
					else:
								return embeds






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Any:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:Dict				    =  MegaLM(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  nn.Linear(mega_args.encoder_embed_dim				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  nn.Dropout(p=0.1      )





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.mega(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					return self.mlm_head(self.dropout(SCREAMING_SNAKE_CASE__      )      )





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     Union[str, Any]:
			with open(os.path.join(snake_case__					,				'''model_args.pkl'''		)					,				'''rb'''		) as f:
						_UpperCamelCase				:List[str]				    =  pkl.load(snake_case__		)

			# load the original encoder
			_UpperCamelCase				:Tuple				    =  OriginalMegaForMaskedLM(**snake_case__		).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''					,				original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''encoder_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)
			print(
			    '''Original Mega MLM layer:'''					,				original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# create a new config from the old one
			_UpperCamelCase				:Optional[int]				    =  MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']					,				vocab_size=mega_original_args['''vocab_size''']					,				hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim					,				shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim					,				intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim					,				ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim					,				dropout_prob=mega_original_args['''mega_args'''].dropout					,				attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout					,				hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout					,				activation=mega_original_args['''mega_args'''].activation_fn					,				attention_activation=mega_original_args['''mega_args'''].attention_activation_fn					,				bidirectional=mega_original_args['''mega_args'''].bidirectional					,				use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0					,				chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size					,				truncation=mega_original_args['''mega_args'''].truncation_length					,				normalization_type=mega_original_args['''mega_args'''].normalization_type					,				normalize_before_mega=snake_case__					,				norm_affine=snake_case__					,				use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout					,				relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias					,				max_positions=mega_original_args['''mega_args'''].max_source_positions					,				nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim					,				normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before					,				nffn_activation_dropout_prob=0.0					,				add_token_type_embeddings=snake_case__					,				add_lm_hidden_dense_layer=snake_case__					,				)

			_UpperCamelCase				:Optional[Any]				    =  MegaForMaskedLM(snake_case__		).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			_UpperCamelCase				:Dict				    =  original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			_UpperCamelCase				:Union[str, Any]				    =  original_mlm.mega.encoders.state_dict()
			_UpperCamelCase				:List[str]				    =  {}
			for module_name in original_state_dict.keys():
						_UpperCamelCase				:Any				    =  None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												_UpperCamelCase				:List[Any]				    =  module_name.replace('''move.beta'''					,				'''ema_gate.ema_expansion_matrix'''		)
									elif "mega_layer.beta" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''beta'''					,				'''qk_bias'''		)
									else:
												_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''beta'''					,				'''b_param'''		)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''move.gamma'''					,				'''ema_gate.kernel_projection_matrix'''		)
									elif "mega_layer.gamma" in module_name:
												_UpperCamelCase				:str				    =  module_name.replace('''gamma'''					,				'''qk_weight'''		)
									else:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''gamma'''					,				'''g_param'''		)
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									_UpperCamelCase				:Optional[Any]				    =  module_name.replace('''move.alpha'''					,				'''ema_gate.decay_factor'''		)
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''move.delta'''					,				'''ema_gate.damping_factor'''		)
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									_UpperCamelCase				:int				    =  module_name.replace('''move.omega'''					,				'''ema_gate.residual_weight'''		)

						if new_module_name:
									_UpperCamelCase				:List[Any]				    =  new_module_name

			if len(snake_case__		) != 0:
						print(f"Renaming these keys: {updated_keys.keys()}"		)
			else:
						print('''No need to rename state dict entries'''		)
			for old, new in updated_keys.items():
						_UpperCamelCase				:List[str]				    =  original_state_dict.pop(snake_case__		)

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''					,				hf_mlm.mega.layers.load_state_dict(snake_case__		)		)

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''					,				hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# test on a randomly generated input sequence
			_UpperCamelCase				:Dict				    =  torch.randint(0					,				hf_config.vocab_size					,				size=(4, 2_56)		)
			_UpperCamelCase				:Tuple				    =  torch.ones_like(snake_case__		)
			# mask a few tokens to make sure masking is applied appropriately :)
			_UpperCamelCase				:str				    =  0

			# run forward passes
			_UpperCamelCase				:str				    =  original_mlm(snake_case__					,				snake_case__					,				batch_first=snake_case__					,				ignore_mask_value=0		)
			_UpperCamelCase				:str				    =  hf_mlm(snake_case__					,				snake_case__		)[0]

			# print shapes and diff
			print(f"original output {original_output.shape}"		)
			print(f"hf output {hf_output.shape}"		)
			print(f"max diff: {(original_output - hf_output).max()}"		)  # 0.0
			_UpperCamelCase				:str				    =  torch.allclose(snake_case__					,				snake_case__					,				atol=1E-3		)

			if success:
						print('''Yay!'''		)
						hf_mlm.save_pretrained(snake_case__		)
			else:
						raise RuntimeError(f"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"		)

			if includes_tokenizer:
						print('''Transferring tokenizer'''		)
						_UpperCamelCase				:Optional[Any]				    =  AutoTokenizer.from_pretrained(snake_case__		)
						tokenizer.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__							:Optional[int]       =  argparse.ArgumentParser()

					parser.add_argument(
					    """--pretrained_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Point to the directory containing your model weights using the official Mega repo""",
					)

					parser.add_argument(
					    """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
					)

					parser.add_argument(
					    """--includes_tokenizer""",
					    action="""store_true""",
					    help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
					)

					UpperCamelCase__							:Dict       =  parser.parse_args()

					convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)

