






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class     lowerCamelCase_					:
				a__       							=	BlenderbotSmallConfig
				a__       							=	{}
				a__       							=	'''gelu'''







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=1_3							,       __lowerCAmelCase=7							,       __lowerCAmelCase=True							,       __lowerCAmelCase=False							,       __lowerCAmelCase=9_9							,       __lowerCAmelCase=3_2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=4							,       __lowerCAmelCase=3_7							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=2_0							,       __lowerCAmelCase=2							,       __lowerCAmelCase=1							,       __lowerCAmelCase=0							,       ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   parent
								__magic_name__		:Optional[int]   =   batch_size
								__magic_name__		:str   =   seq_length
								__magic_name__		:int   =   is_training
								__magic_name__		:Tuple   =   use_labels
								__magic_name__		:str   =   vocab_size
								__magic_name__		:Tuple   =   hidden_size
								__magic_name__		:List[Any]   =   num_hidden_layers
								__magic_name__		:Tuple   =   num_attention_heads
								__magic_name__		:List[str]   =   intermediate_size

								__magic_name__		:List[Any]   =   hidden_dropout_prob
								__magic_name__		:str   =   attention_probs_dropout_prob
								__magic_name__		:Tuple   =   max_position_embeddings
								__magic_name__		:int   =   eos_token_id
								__magic_name__		:int   =   pad_token_id
								__magic_name__		:List[Any]   =   bos_token_id







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   ids_tensor([self.batch_size, self.seq_length - 1]							,       self.vocab_size  )
								__magic_name__		:Optional[int]   =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )							,       1  )
								__magic_name__		:Dict   =   tf.concat([input_ids, eos_tensor]							,       axis=1  )

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

								__magic_name__		:Optional[Any]   =   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							,       )
								__magic_name__		:Dict   =   prepare_blenderbot_small_inputs_dict(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )
								return config, inputs_dict







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   TFBlenderbotSmallModel(config=__lowerCAmelCase  ).get_decoder()
								__magic_name__		:Optional[int]   =   inputs_dict['''input_ids''']

								__magic_name__		:Optional[Any]   =   input_ids[:1, :]
								__magic_name__		:Dict   =   inputs_dict['''attention_mask'''][:1, :]
								__magic_name__		:List[Any]   =   inputs_dict['''head_mask''']
								__magic_name__		:Dict   =   1

								# first forward pass
								__magic_name__		:int   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       head_mask=__lowerCAmelCase							,       use_cache=__lowerCAmelCase  )

								__magic_name__		:int   =   outputs.to_tuple()

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

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

								__magic_name__		:str   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase  )[0]
								__magic_name__		:int   =   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
								__magic_name__		:List[Any]   =   int(ids_tensor((1,)							,       output_from_past.shape[-1]  )  )
								__magic_name__		:Optional[Any]   =   output_from_no_past[:, -3:, random_slice_idx]
								__magic_name__		:str   =   output_from_past[:, :, random_slice_idx]

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







def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case=None,	snake_case=None,	snake_case=None,	snake_case=None,	snake_case=None,	):







				"""simple docstring"""






				if attention_mask is None:
								__magic_name__		:List[str]   =   tf.cast(tf.math.not_equal(snake_case__,	config.pad_token_id			),	tf.inta			)
				if decoder_attention_mask is None:
								__magic_name__		: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:
								__magic_name__		:Optional[Any]   =   tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
				if decoder_head_mask is None:
								__magic_name__		:Dict   =   tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
				if cross_attn_head_mask is None:
								__magic_name__		: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_					(   __snake_case      ,					__snake_case      ,					unittest.TestCase			):
				a__       							=	(
				    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
				)
				a__       							=	(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
				a__       							=	(
				    {
				        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
				        '''feature-extraction''': TFBlenderbotSmallModel,
				        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
				        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
				        '''translation''': TFBlenderbotSmallForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				a__       							=	True
				a__       							=	False
				a__       							=	False







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   TFBlenderbotSmallModelTester(self  )
								__magic_name__		:Optional[int]   =   ConfigTester(self							,       config_class=__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								self.config_tester.run_common_tests()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		: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     lowerCamelCase_					(   unittest.TestCase			):
				a__       							=	[
				    '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
				    '''  i\'m going to throw up.\nand why is that?'''
				]
				a__       							=	'''facebook/blenderbot_small-90M'''







				@cached_property
				def       A						(    self  ):
								"""simple docstring"""
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )







				@cached_property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.tokenizer(self.src_text							,       return_tensors='''tf'''  )
								__magic_name__		:Optional[int]   =   self.model.generate(
								    model_inputs.input_ids							,       attention_mask=model_inputs.attention_mask							,       num_beams=2							,       use_cache=__lowerCAmelCase							,       )
								__magic_name__		:Any   =   self.tokenizer.batch_decode(generated_ids.numpy()							,       skip_special_tokens=__lowerCAmelCase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)








'''simple docstring'''




import fire

from utils import calculate_rouge, save_json







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__=None						,							**snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		[x.strip() for x in open(snake_case__		).readlines()]
    A			:  Tuple   =		[x.strip() for x in open(snake_case__		).readlines()][: len(snake_case__		)]
    A			:  Union[str, Any]   =		calculate_rouge(snake_case__						,							snake_case__						,							**snake_case__		)
    if save_path is not None:
        save_json(snake_case__						,							snake_case__						,							indent=snake_case__		)
    return metrics  # these print nicely


if __name__ == "__main__":
      fire.Fire(calculate_rouge_path)




