



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 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							BlenderbotConfig
					_a             =							{}
					_a             =							'gelu'



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

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



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

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

								lowerCamelCase__		:		Tuple		     =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__		:		Optional[int]		     =prepare_blenderbot_inputs_dict(lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								return config, inputs_dict




					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  Any,  lowerCamelCase							:  Tuple					)-> Union[str, Any]:
								lowerCamelCase__		:		str		     =TFBlenderbotModel(config=lowerCamelCase					).get_decoder()
								lowerCamelCase__		:		Dict		     =inputs_dict['''input_ids''']

								lowerCamelCase__		:		Tuple		     =input_ids[:1, :]
								lowerCamelCase__		:		Optional[int]		     =inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__		:		Dict		     =inputs_dict['''head_mask''']
								lowerCamelCase__		:		Tuple		     =1

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

								lowerCamelCase__		:		List[Any]		     =outputs.to_tuple()

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

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

								lowerCamelCase__		:		Dict		     =model(lowerCamelCase,  attention_mask=lowerCamelCase					)[0]
								lowerCamelCase__		:		Optional[Any]		     =model(lowerCamelCase,  attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase					)[0]

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

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

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






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   str=None							,		__lowerCamelCase    :   str=None							,		__lowerCamelCase    :   int=None							,		__lowerCamelCase    :   Tuple=None							,		__lowerCamelCase    :   Dict=None							,		):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__		:		Union[str, Any]		     =tf.cast(tf.math.not_equal(__lowerCamelCase							,		config.pad_token_id     )							,		tf.inta     )
			if decoder_attention_mask is None:
						lowerCamelCase__		:		Dict		     =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__		:		Any		     =tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						lowerCamelCase__		:		Optional[int]		     =tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						lowerCamelCase__		:		str		     =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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_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 		snake_case	(	self							:  Optional[Any]					)-> Tuple:
								lowerCamelCase__		:		Tuple		     =TFBlenderbotModelTester(self					)
								lowerCamelCase__		:		Optional[int]		     =ConfigTester(self,  config_class=lowerCamelCase					)



					def 		snake_case	(	self							:  str					)-> List[str]:
								self.config_tester.run_common_tests()




					def 		snake_case	(	self							:  str					)-> Union[str, Any]:
								lowerCamelCase__		:		Union[str, Any]		     =self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase					)





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


					'''simple docstring'''





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



					@cached_property
					def 		snake_case	(	self							:  List[str]					)-> List[Any]:
								return BlenderbotTokenizer.from_pretrained(self.model_name					)



					@cached_property
					def 		snake_case	(	self							:  Any					)-> Dict:
								lowerCamelCase__		:		List[str]		     =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
								return model




					@slow
					def 		snake_case	(	self							:  Optional[int]					)-> Union[str, Any]:
								lowerCamelCase__		:		Any		     =self.tokenizer(self.src_text,  return_tensors='''tf'''					)
								lowerCamelCase__		:		int		     =self.model.generate(
								    model_inputs.input_ids,  )
								lowerCamelCase__		:		List[Any]		     =self.tokenizer.batch_decode(generated_ids.numpy(),  skip_special_tokens=lowerCamelCase					)[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 transformers import YosoConfig, YosoForMaskedLM






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			if "model" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''model.'''							,		''''''     )
			if "norm1" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''norm1'''							,		'''attention.output.LayerNorm'''     )
			if "norm2" in orig_key:
						lowerCamelCase__		:		List[Any]		     =orig_key.replace('''norm2'''							,		'''output.LayerNorm'''     )
			if "norm" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''norm'''							,		'''LayerNorm'''     )
			if "transformer" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.split('''.'''     )[0].split('''_'''     )[-1]
						lowerCamelCase__		:		Dict		     =orig_key.replace(f'''transformer_{layer_num}'''							,		f'''encoder.layer.{layer_num}'''     )
			if "mha.attn" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''mha.attn'''							,		'''attention.self'''     )
			if "mha" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''mha'''							,		'''attention'''     )
			if "W_q" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''W_q'''							,		'''self.query'''     )
			if "W_k" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''W_k'''							,		'''self.key'''     )
			if "W_v" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''W_v'''							,		'''self.value'''     )
			if "ff1" in orig_key:
						lowerCamelCase__		:		Dict		     =orig_key.replace('''ff1'''							,		'''intermediate.dense'''     )
			if "ff2" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''ff2'''							,		'''output.dense'''     )
			if "ff" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''ff'''							,		'''output.dense'''     )
			if "mlm_class" in orig_key:
						lowerCamelCase__		:		Tuple		     =orig_key.replace('''mlm.mlm_class'''							,		'''cls.predictions.decoder'''     )
			if "mlm" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''mlm'''							,		'''cls.predictions.transform'''     )
			if "cls" not in orig_key:
						lowerCamelCase__		:		Optional[int]		     ='''yoso.''' + orig_key

			return orig_key






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__		:		Optional[Any]		     =orig_state_dict.pop(__lowerCamelCase     )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									lowerCamelCase__		:		List[str]		     =val

			lowerCamelCase__		:		Optional[int]		     =orig_state_dict['''cls.predictions.decoder.bias''']
			lowerCamelCase__		:		str		     =torch.arange(__lowerCamelCase     ).expand((1, -1)     ) + 2

			return orig_state_dict






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =torch.load(__lowerCamelCase							,		map_location='''cpu'''     )['''model_state_dict''']
			lowerCamelCase__		:		List[Any]		     =YosoConfig.from_json_file(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =YosoForMaskedLM(__lowerCamelCase     )

			lowerCamelCase__		:		Tuple		     =convert_checkpoint_helper(config.max_position_embeddings							,		__lowerCamelCase     )

			print(model.load_state_dict(__lowerCamelCase     )     )
			model.eval()
			model.save_pretrained(__lowerCamelCase     )

			print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''     )


if __name__ == "__main__":
		_lowercase			:			int										=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase			:			Optional[Any]										=						parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




