






'''simple docstring'''




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class    A_	:
							"""simple docstring"""




							def __init__(		self			:Dict       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:int=99       ,							lowerCAmelCase__			:Tuple=13       ,							lowerCAmelCase__			:Any=7       ,							lowerCAmelCase__			:List[Any]=9       ,							lowerCAmelCase__			:List[Any]=True       ,							lowerCAmelCase__			:str=True       ,							lowerCAmelCase__			:Dict=False       ,							lowerCAmelCase__			:int=32       ,							lowerCAmelCase__			:Union[str, Any]=5       ,							lowerCAmelCase__			:Optional[int]=4       ,							lowerCAmelCase__			:Tuple=37       ,							lowerCAmelCase__			:Dict=8       ,							lowerCAmelCase__			:str=0.1       ,							lowerCAmelCase__			:str=0.0_0_2       ,							lowerCAmelCase__			:str=1       ,							lowerCAmelCase__			:str=0       ,							lowerCAmelCase__			:Optional[int]=0       ,							lowerCAmelCase__			:Optional[int]=None       ,							lowerCAmelCase__			:Union[str, Any]=None       ,							)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Dict   =       parent
										snake_case_				:					Union[str, Any]   =       batch_size
										snake_case_				:					Dict   =       encoder_seq_length
										snake_case_				:					Dict   =       decoder_seq_length
										# For common tests
										snake_case_				:					Any   =       self.decoder_seq_length
										snake_case_				:					Tuple   =       is_training
										snake_case_				:					Optional[int]   =       use_attention_mask
										snake_case_				:					Any   =       use_labels
										snake_case_				:					Any   =       vocab_size
										snake_case_				:					Any   =       hidden_size
										snake_case_				:					Tuple   =       num_hidden_layers
										snake_case_				:					Union[str, Any]   =       num_attention_heads
										snake_case_				:					Any   =       d_ff
										snake_case_				:					Dict   =       relative_attention_num_buckets
										snake_case_				:					Any   =       dropout_rate
										snake_case_				:					int   =       initializer_factor
										snake_case_				:					Union[str, Any]   =       eos_token_id
										snake_case_				:					List[Any]   =       pad_token_id
										snake_case_				:					List[Any]   =       decoder_start_token_id
										snake_case_				:					Tuple   =       None
										snake_case_				:					Tuple   =       decoder_layers

							def 							_A	(		self			:Optional[int]				)				->  Tuple:


										'''simple docstring'''




										return TaConfig.from_pretrained("google/umt5-base"				)

							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Tuple=None       ,							lowerCAmelCase__			:List[Any]=None       ,							lowerCAmelCase__			:str=None       ,							lowerCAmelCase__			:Dict=None       ,							lowerCAmelCase__			:Optional[int]=None       ,							)				->  Union[str, Any]:


										'''simple docstring'''




										if attention_mask is None:
													snake_case_				:					List[Any]   =       input_ids.ne(config.pad_token_id				)
										if decoder_attention_mask is None:
													snake_case_				:					Any   =       decoder_input_ids.ne(config.pad_token_id				)
										if head_mask is None:
													snake_case_				:					Optional[int]   =       torch.ones(config.num_hidden_layers       ,							config.num_attention_heads       ,							device=lowerCAmelCase__				)
										if decoder_head_mask is None:
													snake_case_				:					Optional[int]   =       torch.ones(config.num_decoder_layers       ,							config.num_attention_heads       ,							device=lowerCAmelCase__				)
										if cross_attn_head_mask is None:
													snake_case_				:					Dict   =       torch.ones(
													    config.num_decoder_layers       ,							config.num_attention_heads       ,							device=lowerCAmelCase__				)
										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,
										}

							def 							_A	(		self			:List[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       ids_tensor([self.batch_size, self.encoder_seq_length]       ,							self.vocab_size				)
										snake_case_				:					Optional[int]   =       ids_tensor([self.batch_size, self.decoder_seq_length]       ,							self.vocab_size				)

										# we need to clamp the input ids here to avoid having pad token in between
										# this is because for NllbMoe the position_ids are prepared such that
										# all pad tokens have pos id = 2 and rest are between 2..seq_length
										# and the seq_length here is seq_length - num_pad_tokens
										# but when using past, there is no way of knowing if the past input ids had
										# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
										# position_ids being off by num_pad_tokens in past input
										snake_case_				:					Any   =       input_ids.clamp(self.pad_token_id + 1				)
										snake_case_				:					Optional[Any]   =       decoder_input_ids.clamp(self.pad_token_id + 1				)

										snake_case_				:					Tuple   =       self.get_config()
										snake_case_				:					Dict   =       config.num_attention_heads
										snake_case_				:					Optional[Any]   =       self.prepare_inputs_dict(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)
										return config, input_dict

							def 							_A	(		self			:Tuple				)				->  Tuple:


										'''simple docstring'''




										snake_case_,	snake_case_				:					List[str]   =       self.prepare_config_and_inputs()
										return config, inputs_dict

							def 							_A	(		self			:Tuple				)				->  List[str]:


										'''simple docstring'''




										return TaConfig(
										    vocab_size=166       ,							d_model=self.hidden_size       ,							d_ff=self.d_ff       ,							d_kv=self.hidden_size // self.num_attention_heads       ,							num_layers=self.num_hidden_layers       ,							num_decoder_layers=self.decoder_layers       ,							num_heads=self.num_attention_heads       ,							relative_attention_num_buckets=self.relative_attention_num_buckets       ,							dropout_rate=self.dropout_rate       ,							initializer_factor=self.initializer_factor       ,							eos_token_id=self.eos_token_id       ,							bos_token_id=self.pad_token_id       ,							pad_token_id=self.pad_token_id       ,							decoder_start_token_id=self.decoder_start_token_id       ,							)

							def 							_A	(		self			:List[Any]				)				->  int:


										'''simple docstring'''




										return TaConfig(
										    vocab_size=self.vocab_size       ,							d_model=self.hidden_size       ,							d_ff=self.d_ff       ,							d_kv=self.hidden_size // self.num_attention_heads       ,							num_layers=self.num_hidden_layers       ,							num_decoder_layers=self.decoder_layers       ,							num_heads=self.num_attention_heads       ,							relative_attention_num_buckets=self.relative_attention_num_buckets       ,							dropout_rate=self.dropout_rate       ,							initializer_factor=self.initializer_factor       ,							eos_token_id=self.eos_token_id       ,							bos_token_id=self.pad_token_id       ,							pad_token_id=self.pad_token_id       ,							decoder_start_token_id=self.decoder_start_token_id       ,							)

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:int       ,							)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       UMTaModel(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										snake_case_				:					Optional[Any]   =       model(
										    input_ids=lowerCAmelCase__       ,							decoder_input_ids=lowerCAmelCase__       ,							attention_mask=lowerCAmelCase__       ,							decoder_attention_mask=lowerCAmelCase__       ,							)
										snake_case_				:					Dict   =       model(input_ids=lowerCAmelCase__       ,							decoder_input_ids=lowerCAmelCase__				)
										snake_case_				:					Dict   =       result.last_hidden_state
										snake_case_				:					Dict   =       result.past_key_values
										snake_case_				:					List[Any]   =       result.encoder_last_hidden_state

										self.parent.assertEqual(encoder_output.size()       ,							(self.batch_size, self.encoder_seq_length, self.hidden_size)				)
										self.parent.assertEqual(decoder_output.size()       ,							(self.batch_size, self.decoder_seq_length, self.hidden_size)				)
										# There should be `num_layers` key value embeddings stored in decoder_past
										self.parent.assertEqual(len(lowerCAmelCase__				)       ,							config.num_layers				)
										# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
										self.parent.assertEqual(len(decoder_past[0]				)       ,							4				)

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:Optional[int]       ,							)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       UMTaModel(config=lowerCAmelCase__				).get_decoder().to(lowerCAmelCase__				).eval()
										# first forward pass
										snake_case_				:					int   =       model(lowerCAmelCase__       ,							use_cache=lowerCAmelCase__				)
										snake_case_				:					Optional[int]   =       model(lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       model(lowerCAmelCase__       ,							use_cache=lowerCAmelCase__				)

										self.parent.assertTrue(len(lowerCAmelCase__				) == len(lowerCAmelCase__				)				)
										self.parent.assertTrue(len(lowerCAmelCase__				) == len(lowerCAmelCase__				) + 1				)

										snake_case_,	snake_case_				:					Optional[Any]   =       outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										snake_case_				:					List[str]   =       ids_tensor((self.batch_size, 1)       ,							config.vocab_size				)

										# append to next input_ids and
										snake_case_				:					Union[str, Any]   =       torch.cat([input_ids, next_tokens]       ,							dim=-1				)

										snake_case_				:					List[str]   =       model(lowerCAmelCase__				)["last_hidden_state"]
										snake_case_				:					str   =       model(lowerCAmelCase__       ,							past_key_values=lowerCAmelCase__				)["last_hidden_state"]

										# select random slice
										snake_case_				:					int   =       ids_tensor((1,)       ,							output_from_past.shape[-1]				).item()
										snake_case_				:					List[str]   =       output_from_no_past[:, -1, random_slice_idx].detach()
										snake_case_				:					Optional[Any]   =       output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase__       ,							lowerCAmelCase__       ,							atol=1E-3				)				)






							def 							_A	(		self			:str       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:int       ,							)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Any   =       UMTaModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).half().eval()
										snake_case_				:					Union[str, Any]   =       model(**lowerCAmelCase__				)["last_hidden_state"]
										self.parent.assertFalse(torch.isnan(lowerCAmelCase__				).any().item()				)







@require_torch
class    A_	(a_						,					a_						,					a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	(
							    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
							)
							a__   		=	(UMTaForConditionalGeneration,) if is_torch_available() else ()
							a__   		=	(
							    {
							        '''conversational''': UMTaForConditionalGeneration,
							        '''feature-extraction''': UMTaModel,
							        '''summarization''': UMTaForConditionalGeneration,
							        '''text2text-generation''': UMTaForConditionalGeneration,
							        '''translation''': UMTaForConditionalGeneration,
							        '''question-answering''': UMTaForQuestionAnswering,
							    }
							    if is_torch_available()
							    else {}
							)
							a__   		=	True
							a__   		=	False
							a__   		=	False
							a__   		=	True
							a__   		=	True
							# The small UMT5 model needs higher percentages for CPU/MP tests
							a__   		=	[0.8, 0.9]

							def 							_A	(		self			:List[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       UMTaModelTester(self				)

							@unittest.skip("Test has a segmentation fault on torch 1.8.0"				)
							def 							_A	(		self			:Union[str, Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.model_tester.prepare_config_and_inputs()
										snake_case_				:					Optional[int]   =       UMTaModel(config_and_inputs[0]				).to(lowerCAmelCase__				)
										with tempfile.TemporaryDirectory() as tmpdirname:
													torch.onnx.export(
													    lowerCAmelCase__       ,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])       ,							F'''{tmpdirname}/t5_test.onnx'''       ,							export_params=lowerCAmelCase__       ,							opset_version=9       ,							input_names=["input_ids", "decoder_input_ids"]       ,							)

							@unittest.skipIf(torch_device == "cpu"       ,							"Cant do half precision"				)
							def 							_A	(		self			:Optional[Any]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Any   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Tuple   =       ["encoder_attentions", "decoder_attentions", "cross_attentions"]
										snake_case_				:					int   =       self.model_tester.prepare_config_and_inputs()
										snake_case_				:					List[Any]   =       config_and_inputs[0]
										snake_case_				:					Any   =       UMTaForConditionalGeneration(lowerCAmelCase__				).eval()
										model.to(lowerCAmelCase__				)

										snake_case_				:					Optional[int]   =       {
										    "head_mask": torch.zeros(config.num_layers       ,							config.num_heads       ,							device=lowerCAmelCase__				),
										    "decoder_head_mask": torch.zeros(config.num_decoder_layers       ,							config.num_heads       ,							device=lowerCAmelCase__				),
										    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers       ,							config.num_heads       ,							device=lowerCAmelCase__				),
										}

										for attn_name, (name, mask) in zip(lowerCAmelCase__       ,							head_masking.items()				):
													snake_case_				:					Dict   =       {name: mask}
													# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
													if name == "head_mask":
																snake_case_				:					List[Any]   =       torch.ones(
																    config.num_decoder_layers       ,							config.num_heads       ,							device=lowerCAmelCase__				)

													snake_case_				:					Any   =       model.generate(
													    config_and_inputs[1]["input_ids"]       ,							num_beams=1       ,							max_length=3       ,							output_attentions=lowerCAmelCase__       ,							return_dict_in_generate=lowerCAmelCase__       ,							**lowerCAmelCase__       ,							)
													# We check the state of decoder_attentions and cross_attentions just from the last step
													snake_case_				:					List[Any]   =       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
													self.assertEqual(sum([w.sum().item() for w in attn_weights]				)       ,							0.0				)






							@unittest.skip("Does not work on the tiny model as we keep hitting edge cases."				)
							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										pass







@require_torch
@require_sentencepiece
@require_tokenizers
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							@slow
							@unittest.skip(
							    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"				)
							def 							_A	(		self			:str				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       UMTaForConditionalGeneration.from_pretrained("google/umt5-small"       ,							return_dict=lowerCAmelCase__				).to(lowerCAmelCase__				)
										snake_case_				:					Dict   =       AutoTokenizer.from_pretrained("google/umt5-small"       ,							use_fast=lowerCAmelCase__       ,							legacy=lowerCAmelCase__				)
										snake_case_				:					List[str]   =       [
										    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
										    "No se como puedo <extra_id_0>.",
										    "This is the reason why we <extra_id_0> them.",
										    "The <extra_id_0> walks in <extra_id_1>, seats",
										    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
										]
										snake_case_				:					Any   =       tokenizer(lowerCAmelCase__       ,							return_tensors="pt"       ,							padding=lowerCAmelCase__				).input_ids
										# fmt: off
										snake_case_				:					List[Any]   =       torch.tensor(
										    [
										        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
										        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
										    ]				)
										# fmt: on
										torch.testing.assert_allclose(lowerCAmelCase__       ,							lowerCAmelCase__				)

										snake_case_				:					Tuple   =       model.generate(input_ids.to(lowerCAmelCase__				)				)
										snake_case_				:					str   =       [
										    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
										    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										]
										snake_case_				:					List[Any]   =       tokenizer.batch_decode(lowerCAmelCase__				)
										self.assertEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


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




