





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        SCREAMING_SNAKE_CASE__					:



 def __init__(				self,__lowerCamelCase,__lowerCamelCase=99,__lowerCamelCase=13,__lowerCamelCase=7,__lowerCamelCase=9,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=32,__lowerCamelCase=5,__lowerCamelCase=4,__lowerCamelCase=37,__lowerCamelCase=8,__lowerCamelCase=0.1,__lowerCamelCase=0.002,__lowerCamelCase=1,__lowerCamelCase=0,__lowerCamelCase=0,__lowerCamelCase=None,__lowerCamelCase=None,):
        A__		   =		parent
        A__		   =		batch_size
        A__		   =		encoder_seq_length
        A__		   =		decoder_seq_length
        # For common tests
        A__		   =		self.decoder_seq_length
        A__		   =		is_training
        A__		   =		use_attention_mask
        A__		   =		use_labels
        A__		   =		vocab_size
        A__		   =		hidden_size
        A__		   =		num_hidden_layers
        A__		   =		num_attention_heads
        A__		   =		d_ff
        A__		   =		relative_attention_num_buckets
        A__		   =		dropout_rate
        A__		   =		initializer_factor
        A__		   =		eos_token_id
        A__		   =		pad_token_id
        A__		   =		decoder_start_token_id
        A__		   =		None
        A__		   =		decoder_layers



 def 							UpperCamelCase     (				self					):
        return TaConfig.from_pretrained('''google/umt5-base'''					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,):
        if attention_mask is None:
               A__		   =		input_ids.ne(config.pad_token_id					)
        if decoder_attention_mask is None:
               A__		   =		decoder_input_ids.ne(config.pad_token_id					)
        if head_mask is None:
               A__		   =		torch.ones(config.num_hidden_layers,config.num_attention_heads,device=__lowerCamelCase					)
        if decoder_head_mask is None:
               A__		   =		torch.ones(config.num_decoder_layers,config.num_attention_heads,device=__lowerCamelCase					)
        if cross_attn_head_mask is None:
               A__		   =		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 							UpperCamelCase     (				self					):
        A__		   =		ids_tensor([self.batch_size, self.encoder_seq_length],self.vocab_size					)
        A__		   =		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
        A__		   =		input_ids.clamp(self.pad_token_id + 1					)
        A__		   =		decoder_input_ids.clamp(self.pad_token_id + 1					)

        A__		   =		self.get_config()
        A__		   =		config.num_attention_heads
        A__		   =		self.prepare_inputs_dict(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
        return config, input_dict



 def 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		self.prepare_config_and_inputs()
        return config, inputs_dict



 def 							UpperCamelCase     (				self					):
        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 							UpperCamelCase     (				self					):
        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 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        A__		   =		UMTaModel(config=__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(
            input_ids=__lowerCamelCase,decoder_input_ids=__lowerCamelCase,attention_mask=__lowerCamelCase,decoder_attention_mask=__lowerCamelCase,)
        A__		   =		model(input_ids=__lowerCamelCase,decoder_input_ids=__lowerCamelCase					)
        A__		   =		result.last_hidden_state
        A__		   =		result.past_key_values
        A__		   =		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 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        A__		   =		UMTaModel(config=__lowerCamelCase					).get_decoder().to(__lowerCamelCase					).eval()
        # first forward pass
        A__		   =		model(__lowerCamelCase,use_cache=__lowerCamelCase					)
        A__		   =		model(__lowerCamelCase					)
        A__		   =		model(__lowerCamelCase,use_cache=__lowerCamelCase					)

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

        A__   ,      A__		   =		outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        A__		   =		ids_tensor((self.batch_size, 1),config.vocab_size					)

        # append to next input_ids and
        A__		   =		torch.cat([input_ids, next_tokens],dim=-1					)

        A__		   =		model(__lowerCamelCase					)['''last_hidden_state''']
        A__		   =		model(__lowerCamelCase,past_key_values=__lowerCamelCase					)['''last_hidden_state''']

        # select random slice
        A__		   =		ids_tensor((1,),output_from_past.shape[-1]					).item()
        A__		   =		output_from_no_past[:, -1, random_slice_idx].detach()
        A__		   =		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 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,):
        A__		   =		UMTaModel(config=__lowerCamelCase					).to(__lowerCamelCase					).half().eval()
        A__		   =		model(**__lowerCamelCase					)['''last_hidden_state''']
        self.parent.assertFalse(torch.isnan(__lowerCamelCase					).any().item()					)




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



 def 							UpperCamelCase     (				self					):
        A__		   =		UMTaModelTester(self					)



 @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''					)
 def 							UpperCamelCase     (				self					):
        A__		   =		self.model_tester.prepare_config_and_inputs()
        A__		   =		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 							UpperCamelCase     (				self					):
        A__		   =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
        A__		   =		self.model_tester.prepare_config_and_inputs()
        A__		   =		config_and_inputs[0]
        A__		   =		UMTaForConditionalGeneration(__lowerCamelCase					).eval()
        model.to(__lowerCamelCase					)

        A__		   =		{
            '''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()					):
               A__		   =		{name: mask}
               # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
               if name == "head_mask":
                      A__		   =		torch.ones(
                          config.num_decoder_layers,config.num_heads,device=__lowerCamelCase					)

               A__		   =		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
               A__		   =		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 							UpperCamelCase     (				self					):
        pass




@require_torch
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 @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 							UpperCamelCase     (				self					):
        A__		   =		UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''',return_dict=__lowerCamelCase					).to(__lowerCamelCase					)
        A__		   =		AutoTokenizer.from_pretrained('''google/umt5-small''',use_fast=__lowerCamelCase,legacy=__lowerCamelCase					)
        A__		   =		[
            '''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>.''',
        ]
        A__		   =		tokenizer(__lowerCamelCase,return_tensors='''pt''',padding=__lowerCamelCase					).input_ids
        # fmt: off
        A__		   =		torch.tensor(
            [
                [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
            ]					)
        # fmt: on
        torch.testing.assert_allclose(__lowerCamelCase,__lowerCamelCase					)

        A__		   =		model.generate(input_ids.to(__lowerCamelCase					)					)
        A__		   =		[
            '''<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>''',
        ]
        A__		   =		tokenizer.batch_decode(__lowerCamelCase					)
        self.assertEqual(__lowerCamelCase,__lowerCamelCase					)












import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__							,   unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      CTRLTokenizer
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      False



 def 							UpperCamelCase     (				self					):
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        A__		   =		['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
        A__		   =		dict(zip(__lowerCamelCase,range(len(__lowerCamelCase					)					)					)					)
        A__		   =		['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
        A__		   =		{'''unk_token''': '''<unk>'''}

        A__		   =		os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['''vocab_file''']					)
        A__		   =		os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['''merges_file''']					)
        with open(self.vocab_file,'''w''',encoding='''utf-8'''					) as fp:
               fp.write(json.dumps(__lowerCamelCase					) + '''\n'''					)
        with open(self.merges_file,'''w''',encoding='''utf-8'''					) as fp:
               fp.write('''\n'''.join(__lowerCamelCase					)					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        kwargs.update(self.special_tokens_map					)
        return CTRLTokenizer.from_pretrained(self.tmpdirname,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__		   =		'''adapt react readapt apt'''
        A__		   =		'''adapt react readapt apt'''
        return input_text, output_text




 def 							UpperCamelCase     (				self					):
        A__		   =		CTRLTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map					)
        A__		   =		'''adapt react readapt apt'''
        A__		   =		'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        A__		   =		tokenizer.tokenize(__lowerCamelCase					)
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase					)

        A__		   =		tokens + [tokenizer.unk_token]

        A__		   =		[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase					),__lowerCamelCase					)





