
"""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   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ ,							lowercase_=9_9 ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=9 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=False ,							lowercase_=3_2 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=3_7 ,							lowercase_=8 ,							lowercase_=0.1 ,							lowercase_=0.0_0_2 ,							lowercase_=1 ,							lowercase_=0 ,							lowercase_=0 ,							lowercase_=None ,							lowercase_=None ,							)    ->       str:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       encoder_seq_length
          UpperCAmelCase          =       decoder_seq_length
          # For common tests
          UpperCAmelCase          =       self.decoder_seq_length
          UpperCAmelCase          =       is_training
          UpperCAmelCase          =       use_attention_mask
          UpperCAmelCase          =       use_labels
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       d_ff
          UpperCAmelCase          =       relative_attention_num_buckets
          UpperCAmelCase          =       dropout_rate
          UpperCAmelCase          =       initializer_factor
          UpperCAmelCase          =       eos_token_id
          UpperCAmelCase          =       pad_token_id
          UpperCAmelCase          =       decoder_start_token_id
          UpperCAmelCase          =       None
          UpperCAmelCase          =       decoder_layers




      def      a_   (				self				)    ->       Union[str, Any]:
          return TaConfig.from_pretrained('google/umt5-base'				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							)    ->       Optional[int]:
          if attention_mask is None:
              UpperCAmelCase          =       input_ids.ne(config.pad_token_id				)
          if decoder_attention_mask is None:
              UpperCAmelCase          =       decoder_input_ids.ne(config.pad_token_id				)
          if head_mask is None:
              UpperCAmelCase          =       torch.ones(config.num_hidden_layers ,							config.num_attention_heads ,							device=lowercase_				)
          if decoder_head_mask is None:
              UpperCAmelCase          =       torch.ones(config.num_decoder_layers ,							config.num_attention_heads ,							device=lowercase_				)
          if cross_attn_head_mask is None:
              UpperCAmelCase          =       torch.ones(
                  config.num_decoder_layers ,							config.num_attention_heads ,							device=lowercase_				)
          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				)    ->       Union[str, Any]:
          UpperCAmelCase          =       ids_tensor([self.batch_size, self.encoder_seq_length] ,							self.vocab_size				)
          UpperCAmelCase          =       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
          UpperCAmelCase          =       input_ids.clamp(self.pad_token_id + 1				)
          UpperCAmelCase          =       decoder_input_ids.clamp(self.pad_token_id + 1				)

          UpperCAmelCase          =       self.get_config()
          UpperCAmelCase          =       config.num_attention_heads
          UpperCAmelCase          =       self.prepare_inputs_dict(lowercase_ ,							lowercase_ ,							lowercase_				)
          return config, input_dict




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.prepare_config_and_inputs()
          return config, inputs_dict




      def      a_   (				self				)    ->       Union[str, Any]:
          return TaConfig(
              vocab_size=1_6_6 ,							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				)    ->       str:
          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 ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Tuple:
          UpperCAmelCase          =       UMTaModel(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(
              input_ids=lowercase_ ,							decoder_input_ids=lowercase_ ,							attention_mask=lowercase_ ,							decoder_attention_mask=lowercase_ ,							)
          UpperCAmelCase          =       model(input_ids=lowercase_ ,							decoder_input_ids=lowercase_				)
          UpperCAmelCase          =       result.last_hidden_state
          UpperCAmelCase          =       result.past_key_values
          UpperCAmelCase          =       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(lowercase_				) ,							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 ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[Any]:
          UpperCAmelCase          =       UMTaModel(config=lowercase_				).get_decoder().to(lowercase_				).eval()
          # first forward pass
          UpperCAmelCase          =       model(lowercase_ ,							use_cache=lowercase_				)
          UpperCAmelCase          =       model(lowercase_				)
          UpperCAmelCase          =       model(lowercase_ ,							use_cache=lowercase_				)

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

          UpperCAmelCase      ,				UpperCAmelCase          =       outputs.to_tuple()

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

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

          UpperCAmelCase          =       model(lowercase_				)['last_hidden_state']
          UpperCAmelCase          =       model(lowercase_ ,							past_key_values=lowercase_				)['last_hidden_state']

          # select random slice
          UpperCAmelCase          =       ids_tensor((1,) ,							output_from_past.shape[-1]				).item()
          UpperCAmelCase          =       output_from_no_past[:, -1, random_slice_idx].detach()
          UpperCAmelCase          =       output_from_past[:, 0, random_slice_idx].detach()

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




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							)    ->       Union[str, Any]:
          UpperCAmelCase          =       UMTaModel(config=lowercase_				).to(lowercase_				).half().eval()
          UpperCAmelCase          =       model(**lowercase_				)['last_hidden_state']
          self.parent.assertFalse(torch.isnan(lowercase_				).any().item()				)

@require_torch
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				str				   =						(
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      __SCREAMING_SNAKE_CASE :				Dict				   =						(UMTaForConditionalGeneration,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE :				List[str]				   =						(
          {
              "conversational": UMTaForConditionalGeneration,
              "feature-extraction": UMTaModel,
              "summarization": UMTaForConditionalGeneration,
              "text2text-generation": UMTaForConditionalGeneration,
              "translation": UMTaForConditionalGeneration,
              "question-answering": UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						True
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						False
      __SCREAMING_SNAKE_CASE :				Tuple				   =						False
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						True
      __SCREAMING_SNAKE_CASE :				int				   =						True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      __SCREAMING_SNAKE_CASE :				Any				   =						[0.8, 0.9]




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       UMTaModelTester(self				)




      @unittest.skip('Test has a segmentation fault on torch 1.8.0'				)
      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          UpperCAmelCase          =       UMTaModel(config_and_inputs[0]				).to(lowercase_				)
          with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  lowercase_ ,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) ,							F"{tmpdirname}/t5_test.onnx" ,							export_params=lowercase_ ,							opset_version=9 ,							input_names=['input_ids', 'decoder_input_ids'] ,							)




      @unittest.skipIf(torch_device == 'cpu' ,							'Cant do half precision'				)
      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          UpperCAmelCase          =       config_and_inputs[0]
          UpperCAmelCase          =       UMTaForConditionalGeneration(lowercase_				).eval()
          model.to(lowercase_				)

          UpperCAmelCase          =       {
              'head_mask': torch.zeros(config.num_layers ,							config.num_heads ,							device=lowercase_				),
              'decoder_head_mask': torch.zeros(config.num_decoder_layers ,							config.num_heads ,							device=lowercase_				),
              'cross_attn_head_mask': torch.zeros(config.num_decoder_layers ,							config.num_heads ,							device=lowercase_				),
          }

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

              UpperCAmelCase          =       model.generate(
                  config_and_inputs[1]['input_ids'] ,							num_beams=1 ,							max_length=3 ,							output_attentions=lowercase_ ,							return_dict_in_generate=lowercase_ ,							**lowercase_ ,							)
              # We check the state of decoder_attentions and cross_attentions just from the last step
              UpperCAmelCase          =       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				)    ->       Any:
          pass

@require_torch
@require_sentencepiece
@require_tokenizers
class   _UpperCAmelCase				(		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      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       UMTaForConditionalGeneration.from_pretrained('google/umt5-small' ,							return_dict=lowercase_				).to(lowercase_				)
          UpperCAmelCase          =       AutoTokenizer.from_pretrained('google/umt5-small' ,							use_fast=lowercase_ ,							legacy=lowercase_				)
          UpperCAmelCase          =       [
              '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>.',
          ]
          UpperCAmelCase          =       tokenizer(lowercase_ ,							return_tensors='pt' ,							padding=lowercase_				).input_ids
          # fmt: off
          UpperCAmelCase          =       torch.tensor(
              [
                  [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
              ]				)
          # fmt: on
          torch.testing.assert_allclose(lowercase_ ,							lowercase_				)

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





"""simple docstring"""

import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE_            =    '''1'''
SCREAMING_SNAKE_CASE_            =    '''0'''
SCREAMING_SNAKE_CASE_            =    '''1'''

SCREAMING_SNAKE_CASE_            =    ort.SessionOptions()
SCREAMING_SNAKE_CASE_            =    ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
SCREAMING_SNAKE_CASE_            =    ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
SCREAMING_SNAKE_CASE_            =    ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE_            =    ort.RunOptions()

SCREAMING_SNAKE_CASE_            =    128
SCREAMING_SNAKE_CASE_            =    1
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
SCREAMING_SNAKE_CASE_            =    time.time()
SCREAMING_SNAKE_CASE_            =    2000
SCREAMING_SNAKE_CASE_            =    {}
for iter in range(max_iters):
   SCREAMING_SNAKE_CASE_            =    sess.run(
       None,
       {
           sess.get_inputs()[0].name: input_ids,
           sess.get_inputs()[1].name: attention_mask,
           sess.get_inputs()[2].name: token_type_ids,
       },
       run_options=run_opt,
   )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))



