





"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel




@require_tf
class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  BlenderbotConfig
					__lowerCAmelCase  =  {}
					__lowerCAmelCase  =  '''gelu'''







					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=13	,			_UpperCAmelCase=7	,			_UpperCAmelCase=True	,			_UpperCAmelCase=False	,			_UpperCAmelCase=99	,			_UpperCAmelCase=32	,			_UpperCAmelCase=2	,			_UpperCAmelCase=4	,			_UpperCAmelCase=37	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=20	,			_UpperCAmelCase=2	,			_UpperCAmelCase=1	,			_UpperCAmelCase=0	,			):
									__a :						Optional[Any]			       =  parent
									__a :						Union[str, Any]			       =  batch_size
									__a :						List[str]			       =  seq_length
									__a :						List[Any]			       =  is_training
									__a :						Optional[int]			       =  use_labels
									__a :						Optional[Any]			       =  vocab_size
									__a :						str			       =  hidden_size
									__a :						Optional[int]			       =  num_hidden_layers
									__a :						List[str]			       =  num_attention_heads
									__a :						Optional[Any]			       =  intermediate_size

									__a :						List[str]			       =  hidden_dropout_prob
									__a :						Optional[Any]			       =  attention_probs_dropout_prob
									__a :						Dict			       =  max_position_embeddings
									__a :						List[Any]			       =  eos_token_id
									__a :						List[Any]			       =  pad_token_id
									__a :						Any			       =  bos_token_id







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

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

									__a :						str			       =  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	,			)
									__a :						List[str]			       =  prepare_blenderbot_inputs_dict(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )
									return config, inputs_dict







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Any			       =  TFBlenderbotModel(config=_UpperCAmelCase     ).get_decoder()
									__a :						Any			       =  inputs_dict['''input_ids''']

									__a :						Dict			       =  input_ids[:1, :]
									__a :						Union[str, Any]			       =  inputs_dict['''attention_mask'''][:1, :]
									__a :						Union[str, Any]			       =  inputs_dict['''head_mask''']
									__a :						Optional[int]			       =  1

									# first forward pass
									__a :						int			       =  model(_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			head_mask=_UpperCAmelCase	,			use_cache=_UpperCAmelCase     )

									__a       ,						__a :						List[Any]			       =  outputs.to_tuple()

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

									# append to next input_ids and
									__a :						List[str]			       =  tf.concat([input_ids, next_tokens]	,			axis=-1     )
									__a :						Tuple			       =  tf.concat([attention_mask, next_attn_mask]	,			axis=-1     )

									__a :						str			       =  model(_UpperCAmelCase	,			attention_mask=_UpperCAmelCase     )[0]
									__a :						Union[str, Any]			       =  model(_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			past_key_values=_UpperCAmelCase     )[0]

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

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

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






def     __A      (			a_						:Tuple   ,       a_						:Dict   ,       a_						:int   ,       a_						:Any=None   ,       a_						:List[Any]=None   ,       a_						:Union[str, Any]=None   ,       a_						:Any=None   ,       a_						:Any=None   ,       )		->		int:
				if attention_mask is None:
								__a :						Union[str, Any]			       =  tf.cast(tf.math.not_equal(a_   ,       config.pad_token_id)   ,       tf.inta)
				if decoder_attention_mask is None:
								__a :						Optional[Any]			       =  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:
								__a :						Any			       =  tf.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								__a :						int			       =  tf.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								__a :						Optional[int]			       =  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			(			_UpperCamelCase   ,      _UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					__lowerCAmelCase  =  (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					__lowerCAmelCase  =  (
					    {
					        '''conversational''': TFBlenderbotForConditionalGeneration,
					        '''feature-extraction''': TFBlenderbotModel,
					        '''summarization''': TFBlenderbotForConditionalGeneration,
					        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
					        '''translation''': TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  TFBlenderbotModelTester(self     )
									__a :						List[Any]			       =  ConfigTester(self	,			config_class=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									self.config_tester.run_common_tests()







					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase     )






@require_tokenizers
@require_tf
class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  ['''My friends are cool but they eat too many carbs.''']
					__lowerCAmelCase  =  '''facebook/blenderbot-400M-distill'''







					@cached_property
					def  _lowerCamelCase		(						self     ):
									return BlenderbotTokenizer.from_pretrained(self.model_name     )







					@cached_property
					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
									return model







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  self.tokenizer(self.src_text	,			return_tensors='''tf'''     )
									__a :						Tuple			       =  self.model.generate(
									    model_inputs.input_ids	,			)
									__a :						List[str]			       =  self.tokenizer.batch_decode(generated_ids.numpy()	,			skip_special_tokens=_UpperCAmelCase     )[0]
									assert (
									    generated_words
									    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
									)






"""simple docstring"""






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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

def     __A      (			a_						:str   ,       a_						:str)		->		List[Any]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								__a :						List[str]			       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						int			       =  XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)
				else:
								__a :						Optional[int]			       =  ProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						Optional[Any]			       =  ProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)

				__a :						Tuple			       =  ['''key_proj''', '''value_proj''', '''query_proj''']

				__a :						List[str]			       =  {
				    '''self_attn''': '''ngram_self_attn''',
				    '''cross_attn''': '''encoder_attn''',
				    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
				    '''feed_forward_layer_norm''': '''final_layer_norm''',
				    '''feed_forward''': '''''',
				    '''intermediate''': '''fc1''',
				    '''output''': '''fc2''',
				    '''key_proj''': '''k_proj''',
				    '''query_proj''': '''q_proj''',
				    '''value_proj''': '''v_proj''',
				    '''word_embeddings''': '''embed_tokens''',
				    '''embeddings_layer_norm''': '''emb_layer_norm''',
				    '''relative_pos_embeddings''': '''relative_linear''',
				    '''ngram_embeddings''': '''ngram_input_embed''',
				    '''position_embeddings''': '''embed_positions''',
				}

				for key in loading_info["missing_keys"]:
								__a :						List[str]			       =  key.split('''.''')

								if attributes[0] == "lm_head":
												__a :						Dict			       =  prophet
												__a :						Optional[Any]			       =  prophet_old
								else:
												__a :						Union[str, Any]			       =  prophet.prophetnet
												__a :						int			       =  prophet_old.model

								__a :						Optional[int]			       =  False
								for attribute in attributes:
												if attribute in mapping:
																__a :						str			       =  mapping[attribute]
																if not hasattr(a_   ,       a_) and len(a_) > 0:
																				__a :						List[str]			       =  attribute
												elif hasattr(a_   ,       a_):
																__a :						int			       =  attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																__a :						Optional[int]			       =  old_model.weight
																logger.info(F"""{attribute} is initialized.""")
																__a :						List[str]			       =  True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																__a :						Dict			       =  old_model.bias
																logger.info(F"""{attribute} is initialized""")
																__a :						Tuple			       =  True
																break
												elif attribute in special_keys and hasattr(a_   ,       '''in_proj_weight'''):
																__a :						List[Any]			       =  old_model.in_proj_weight.shape[0] // 3
																__a :						Tuple			       =  getattr(a_   ,       a_)
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				__a :						Any			       =  nn.Parameter(old_model.in_proj_weight[:embed_dim, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[:embed_dim])

																elif attribute == "key_proj":
																				__a :						Union[str, Any]			       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
																				__a :						int			       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
																elif attribute == "value_proj":
																				__a :						Dict			       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
																__a :						int			       =  True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
																__a :						Optional[Any]			       =  nn.Parameter(old_model.embed_positions.weight[:5_12, :])
																__a :						List[Any]			       =  True
																break

												if attribute.isdigit():
																__a :						Optional[Any]			       =  model[int(a_)]
																__a :						str			       =  old_model[int(a_)]
												else:
																__a :						Optional[int]			       =  getattr(a_   ,       a_)

																if old_attribute == "":
																				__a :						str			       =  old_model
																else:
																				if not hasattr(a_   ,       a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a :						Union[str, Any]			       =  getattr(a_   ,       a_)

								if not is_key_init:
												raise ValueError(F"""{key} was not correctly initialized!""")

				print(F"""Saving model to {pytorch_dump_folder_path}""")
				prophet.save_pretrained(a_)


if __name__ == "__main__":
		A 			=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--prophetnet_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.'''
		)
		A 			=  parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)