






'''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	:
							a				      =       BlenderbotConfig
							a				      =       {}
							a				      =       """gelu"""




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

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




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

														lowerCamelCase__					:	Dict         =       ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size     )

														lowerCamelCase__					:	Dict         =       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_blenderbot_inputs_dict(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )
														return config, inputs_dict




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Dict     ):
														lowerCamelCase__					:	Optional[int]         =       TFBlenderbotModel(config=UpperCamelCase__     ).get_decoder()
														lowerCamelCase__					:	Optional[int]         =       inputs_dict["""input_ids"""]

														lowerCamelCase__					:	Any         =       input_ids[:1, :]
														lowerCamelCase__					:	Optional[Any]         =       inputs_dict["""attention_mask"""][:1, :]
														lowerCamelCase__					:	str         =       inputs_dict["""head_mask"""]
														lowerCamelCase__					:	List[str]         =       1

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

														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[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__					:	Any         =       tf.cast(ids_tensor((self.batch_size, 3)			,							2     )			,							tf.inta     )

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

														lowerCamelCase__					:	str         =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__     )[0]
														lowerCamelCase__					:	Tuple         =       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
														lowerCamelCase__					:	Any         =       int(ids_tensor((1,)			,							output_from_past.shape[-1]     )     )
														lowerCamelCase__					:	Union[str, Any]         =       output_from_no_past[:, -3:, random_slice_idx]
														lowerCamelCase__					:	Union[str, Any]         =       output_from_past[:, :, random_slice_idx]

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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase=None				,			UpperCamelCase=None				,			UpperCamelCase=None				,			UpperCamelCase=None				,			UpperCamelCase=None				,			)      ->       Any:







							if attention_mask is None:
														lowerCamelCase__					:	Dict         =       tf.cast(tf.math.not_equal(UpperCamelCase				,			config.pad_token_id      )				,			tf.inta      )
							if decoder_attention_mask is None:
														lowerCamelCase__					:	Optional[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__					:	Optional[int]         =       tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
							if decoder_head_mask is None:
														lowerCamelCase__					:	Dict         =       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
							if cross_attn_head_mask is None:
														lowerCamelCase__					:	Dict         =       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	(				lowercase_				,      lowercase_				,      unittest.TestCase		):
							a				      =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							a				      =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							a				      =       (
							    {
							        """conversational""": TFBlenderbotForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotModel,
							        """summarization""": TFBlenderbotForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotForConditionalGeneration,
							        """translation""": TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							a				      =       True
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Any         =       TFBlenderbotModelTester(self     )
														lowerCamelCase__					:	Tuple         =       ConfigTester(self			,							config_class=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														self.config_tester.run_common_tests()




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	Any         =       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		):
							a				      =       ["""My friends are cool but they eat too many carbs."""]
							a				      =       """facebook/blenderbot-400M-distill"""




							@cached_property
							def 		lowerCamelCase_				(							self:				List[str]     ):
														return BlenderbotTokenizer.from_pretrained(self.model_name     )




							@cached_property
							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[int]         =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
														return model




							@slow
							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.tokenizer(self.src_text			,							return_tensors="""tf"""     )
														lowerCamelCase__					:	Tuple         =       self.model.generate(
														    model_inputs.input_ids			,							)
														lowerCamelCase__					:	Any         =       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

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	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='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




