





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
     import optax
     from flax.training.common_utils import onehot

     from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
     from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
   lowerCamelCase__:      Optional[int]			  =AutoTokenizer.from_pretrained("google/mt5-small")

   lowerCamelCase__:      List[Any]			  =tokenizer("Hello there"       , return_tensors="np").input_ids
   lowerCamelCase__:      Dict			  =tokenizer("Hi I am"       , return_tensors="np").input_ids

   lowerCamelCase__:      Tuple			  =shift_tokens_right(UpperCAmelCase_       , model.config.pad_token_id       , model.config.decoder_start_token_id)

   lowerCamelCase__:      Optional[Any]			  =model(UpperCAmelCase_       , decoder_input_ids=UpperCAmelCase_).logits
   lowerCamelCase__:      Optional[Any]			  =optax.softmax_cross_entropy(UpperCAmelCase_       , onehot(UpperCAmelCase_       , logits.shape[-1])).mean()

   lowerCamelCase__:      Dict			  =-(labels.shape[-1] * loss.item())

   lowerCamelCase__:      List[str]			  =-84.9127
   self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)



"""simple docstring"""



import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase__          = '1'
UpperCAmelCase__          = '0'
UpperCAmelCase__          = '1'

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

UpperCAmelCase__          = 128
UpperCAmelCase__          = 1
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = 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...')
UpperCAmelCase__          = time.time()
UpperCAmelCase__          = 2000
UpperCAmelCase__          = {}
for iter in range(max_iters):
 UpperCAmelCase__          = 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))



