'''simple docstring'''


import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
 import torch

 from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM






@require_torch
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=9_9				,		snake_case_=1_3				,		snake_case_=1_6				,		snake_case_=7				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=2				,		snake_case_=3_2				,		snake_case_=4				,		snake_case_=4				,		snake_case_=3_0				,		snake_case_=0				,		snake_case_=1				,		snake_case_=2				,		snake_case_=None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				parent
       UpperCAmelCase_							:       Union[str, Any]       =				batch_size
       UpperCAmelCase_							:       Any       =				decoder_seq_length
       # For common tests
       UpperCAmelCase_							:       Tuple       =				self.decoder_seq_length
       UpperCAmelCase_							:       Dict       =				is_training
       UpperCAmelCase_							:       List[str]       =				use_attention_mask
       UpperCAmelCase_							:       Optional[Any]       =				use_labels

       UpperCAmelCase_							:       Dict       =				vocab_size
       UpperCAmelCase_							:       Tuple       =				d_model
       UpperCAmelCase_							:       Tuple       =				d_model
       UpperCAmelCase_							:       List[str]       =				decoder_layers
       UpperCAmelCase_							:       str       =				decoder_layers
       UpperCAmelCase_							:       List[str]       =				decoder_ffn_dim
       UpperCAmelCase_							:       Tuple       =				decoder_attention_heads
       UpperCAmelCase_							:       str       =				decoder_attention_heads
       UpperCAmelCase_							:       Optional[Any]       =				eos_token_id
       UpperCAmelCase_							:       int       =				bos_token_id
       UpperCAmelCase_							:       Optional[int]       =				pad_token_id
       UpperCAmelCase_							:       Optional[int]       =				decoder_start_token_id
       UpperCAmelCase_							:       int       =				use_cache
       UpperCAmelCase_							:       Dict       =				max_position_embeddings

       UpperCAmelCase_							:       Tuple       =				None
       UpperCAmelCase_							:       Any       =				decoder_seq_length
       UpperCAmelCase_							:       int       =				2
       UpperCAmelCase_							:       Dict       =				1


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				ids_tensor([self.batch_size, self.decoder_seq_length]				,		self.vocab_size						)

       UpperCAmelCase_							:       Union[str, Any]       =				None
       if self.use_attention_mask:
            UpperCAmelCase_							:       str       =				ids_tensor([self.batch_size, self.decoder_seq_length]				,		vocab_size=2						)

       UpperCAmelCase_							:       Any       =				None
       if self.use_labels:
            UpperCAmelCase_							:       Dict       =				ids_tensor([self.batch_size, self.decoder_seq_length]				,		self.vocab_size						)

       UpperCAmelCase_							:       List[str]       =				TrOCRConfig(
           vocab_size=self.vocab_size				,		d_model=self.d_model				,		decoder_layers=self.decoder_layers				,		decoder_ffn_dim=self.decoder_ffn_dim				,		decoder_attention_heads=self.decoder_attention_heads				,		eos_token_id=self.eos_token_id				,		bos_token_id=self.bos_token_id				,		use_cache=self.use_cache				,		pad_token_id=self.pad_token_id				,		decoder_start_token_id=self.decoder_start_token_id				,		max_position_embeddings=self.max_position_embeddings				,		)

       return (config, input_ids, attention_mask, lm_labels)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				True
       UpperCAmelCase_							:       List[Any]       =				TrOCRDecoder(config=_UpperCAmelCase						).to(_UpperCAmelCase						).eval()
       UpperCAmelCase_							:       List[Any]       =				input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       UpperCAmelCase_							:       List[str]       =				model(_UpperCAmelCase				,		use_cache=_UpperCAmelCase						)
       UpperCAmelCase_							:       Union[str, Any]       =				model(_UpperCAmelCase						)
       UpperCAmelCase_							:       Optional[int]       =				model(_UpperCAmelCase				,		use_cache=_UpperCAmelCase						)

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

       UpperCAmelCase_							:       List[Any]       =				outputs['''past_key_values''']

       # create hypothetical next token and extent to next_input_ids
       UpperCAmelCase_							:       Optional[Any]       =				ids_tensor((2, 1)				,		config.vocab_size - 1						) + 1

       # append to next input_ids and
       UpperCAmelCase_							:       Optional[int]       =				torch.cat([input_ids, next_tokens]				,		dim=-1						)

       UpperCAmelCase_							:       int       =				model(_UpperCAmelCase						)['''last_hidden_state''']
       UpperCAmelCase_							:       List[str]       =				model(_UpperCAmelCase				,		past_key_values=_UpperCAmelCase						)['''last_hidden_state''']

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

       # test that outputs are equal for slice
       assert torch.allclose(_UpperCAmelCase				,		_UpperCAmelCase				,		atol=1E-3						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.prepare_config_and_inputs()
       UpperCAmelCase_							:       int       =				config_and_inputs

       UpperCAmelCase_							:       Tuple       =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:List[Any]						   =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
  lowerCamelCase_		:Optional[Any]						   =		(TrOCRForCausalLM,) if is_torch_available() else ()
  lowerCamelCase_		:int						   =		{"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
  lowerCamelCase_		:int						   =		True
  lowerCamelCase_		:Any						   =		False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				TrOCRStandaloneDecoderModelTester(self				,		is_training=_UpperCAmelCase						)
       UpperCAmelCase_							:       Optional[int]       =				ConfigTester(self				,		config_class=_UpperCAmelCase						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*_UpperCAmelCase						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return






  @unittest.skip('The model doesn\'t support left padding'						)  # and it's not used enough to be worth fixing :)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

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

snake_case__					:      List[Any]				      =  128
snake_case__					:      Union[str, Any]				      =  1
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Union[str, Any]				      =  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...''')
snake_case__					:      Union[str, Any]				      =  time.time()
snake_case__					:      str				      =  2000
snake_case__					:      Tuple				      =  {}
for iter in range(max_iters):
 snake_case__					:      str				      =  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))






