





import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           LayoutLMvaConfig,
           LayoutLMvaForQuestionAnswering,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaModel,
       )
       from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import LayoutLMvaImageProcessor


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     Union[str, Any]		, A					:     Dict		, A					:     Optional[Any]=2		, A					:     List[str]=3		, A					:     Union[str, Any]=4		, A					:     List[Any]=2		, A					:     Optional[int]=7		, A					:     int=True		, A					:     Optional[int]=True		, A					:     str=True		, A					:     List[str]=True		, A					:     int=9_9		, A					:     List[str]=3_6		, A					:     Dict=3		, A					:     Union[str, Any]=4		, A					:     Any=3_7		, A					:     str="gelu"		, A					:     Dict=0.1		, A					:     str=0.1		, A					:     Dict=5_1_2		, A					:     Union[str, Any]=1_6		, A					:     Any=2		, A					:     Any=0.02		, A					:     str=6		, A					:     Union[str, Any]=6		, A					:     str=3		, A					:     Any=4		, A					:     Tuple=None		, A					:     Optional[Any]=1_0_0_0		, )       ->Tuple:
          lowerCamelCase__       :						Dict        =							parent
          lowerCamelCase__       :						int        =							batch_size
          lowerCamelCase__       :						Dict        =							num_channels
          lowerCamelCase__       :						str        =							image_size
          lowerCamelCase__       :						List[str]        =							patch_size
          lowerCamelCase__       :						Optional[Any]        =							text_seq_length
          lowerCamelCase__       :						int        =							is_training
          lowerCamelCase__       :						Optional[Any]        =							use_input_mask
          lowerCamelCase__       :						Union[str, Any]        =							use_token_type_ids
          lowerCamelCase__       :						Union[str, Any]        =							use_labels
          lowerCamelCase__       :						List[str]        =							vocab_size
          lowerCamelCase__       :						Optional[Any]        =							hidden_size
          lowerCamelCase__       :						str        =							num_hidden_layers
          lowerCamelCase__       :						Optional[Any]        =							num_attention_heads
          lowerCamelCase__       :						Dict        =							intermediate_size
          lowerCamelCase__       :						Tuple        =							hidden_act
          lowerCamelCase__       :						Any        =							hidden_dropout_prob
          lowerCamelCase__       :						Union[str, Any]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						int        =							max_position_embeddings
          lowerCamelCase__       :						Tuple        =							type_vocab_size
          lowerCamelCase__       :						Optional[int]        =							type_sequence_label_size
          lowerCamelCase__       :						Union[str, Any]        =							initializer_range
          lowerCamelCase__       :						str        =							coordinate_size
          lowerCamelCase__       :						Optional[int]        =							shape_size
          lowerCamelCase__       :						int        =							num_labels
          lowerCamelCase__       :						List[str]        =							num_choices
          lowerCamelCase__       :						Dict        =							scope
          lowerCamelCase__       :						Dict        =							range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          lowerCamelCase__       :						Union[str, Any]        =							text_seq_length
          lowerCamelCase__       :						Dict        =							(image_size // patch_size) ** 2 + 1
          lowerCamelCase__       :						Union[str, Any]        =							self.text_seq_length + self.image_seq_length







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[Any]:
          lowerCamelCase__       :						str        =							ids_tensor([self.batch_size, self.text_seq_length]		, self.vocab_size      )

          lowerCamelCase__       :						Tuple        =							ids_tensor([self.batch_size, self.text_seq_length, 4]		, self.range_bbox      )
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]      ):
             for j in range(bbox.shape[1]      ):
                if bbox[i, j, 3] < bbox[i, j, 1]:
                   lowerCamelCase__       :						List[Any]        =							bbox[i, j, 3]
                   lowerCamelCase__       :						Optional[Any]        =							bbox[i, j, 1]
                   lowerCamelCase__       :						Tuple        =							t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                   lowerCamelCase__       :						Union[str, Any]        =							bbox[i, j, 2]
                   lowerCamelCase__       :						Dict        =							bbox[i, j, 0]
                   lowerCamelCase__       :						List[str]        =							t

          lowerCamelCase__       :						List[str]        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          lowerCamelCase__       :						Any        =							None
          if self.use_input_mask:
             lowerCamelCase__       :						Dict        =							random_attention_mask([self.batch_size, self.text_seq_length]      )

          lowerCamelCase__       :						Tuple        =							None
          if self.use_token_type_ids:
             lowerCamelCase__       :						Tuple        =							ids_tensor([self.batch_size, self.text_seq_length]		, self.type_vocab_size      )

          lowerCamelCase__       :						str        =							None
          lowerCamelCase__       :						int        =							None
          if self.use_labels:
             lowerCamelCase__       :						List[str]        =							ids_tensor([self.batch_size]		, self.type_sequence_label_size      )
             lowerCamelCase__       :						str        =							ids_tensor([self.batch_size, self.text_seq_length]		, self.num_labels      )

          lowerCamelCase__       :						Any        =							LayoutLMvaConfig(
              vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, initializer_range=self.initializer_range		, coordinate_size=self.coordinate_size		, shape_size=self.shape_size		, input_size=self.image_size		, patch_size=self.patch_size		, )

          return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







       def 						__lowerCamelCase   (				self					:     str		, A					:     Dict		, A					:     Union[str, Any]		, A					:     Tuple		, A					:     Tuple		, A					:     List[Any]		, A					:     List[str]		, A					:     List[Any]		, A					:     str      )       ->Any:
          lowerCamelCase__       :						Optional[int]        =							LayoutLMvaModel(config=A      )
          model.to(A      )
          model.eval()

          # text + image
          lowerCamelCase__       :						str        =							model(A		, pixel_values=A      )
          lowerCamelCase__       :						Optional[int]        =							model(
              A		, bbox=A		, pixel_values=A		, attention_mask=A		, token_type_ids=A      )
          lowerCamelCase__       :						str        =							model(A		, bbox=A		, pixel_values=A		, token_type_ids=A      )
          lowerCamelCase__       :						Any        =							model(A		, bbox=A		, pixel_values=A      )

          self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)      )

          # text only
          lowerCamelCase__       :						Union[str, Any]        =							model(A      )
          self.parent.assertEqual(
              result.last_hidden_state.shape		, (self.batch_size, self.text_seq_length, self.hidden_size)      )

          # image only
          lowerCamelCase__       :						Union[str, Any]        =							model(pixel_values=A      )
          self.parent.assertEqual(
              result.last_hidden_state.shape		, (self.batch_size, self.image_seq_length, self.hidden_size)      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     int		, A					:     Union[str, Any]		, A					:     List[Any]		, A					:     Optional[Any]		, A					:     Tuple		, A					:     str		, A					:     str		, A					:     Optional[Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						int        =							self.num_labels
          lowerCamelCase__       :						int        =							LayoutLMvaForSequenceClassification(A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						List[str]        =							model(
              A		, bbox=A		, pixel_values=A		, attention_mask=A		, token_type_ids=A		, labels=A		, )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Union[str, Any]		, A					:     List[Any]		, A					:     Any		, A					:     Dict		, A					:     List[str]		, A					:     List[Any]		, A					:     List[Any]		, A					:     Any      )       ->List[str]:
          lowerCamelCase__       :						Optional[Any]        =							self.num_labels
          lowerCamelCase__       :						Union[str, Any]        =							LayoutLMvaForTokenClassification(config=A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						List[str]        =							model(
              A		, bbox=A		, pixel_values=A		, attention_mask=A		, token_type_ids=A		, labels=A		, )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.text_seq_length, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     List[Any]		, A					:     List[Any]		, A					:     Union[str, Any]		, A					:     Any		, A					:     Optional[int]		, A					:     List[Any]		, A					:     str		, A					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						Union[str, Any]        =							LayoutLMvaForQuestionAnswering(config=A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Optional[int]        =							model(
              A		, bbox=A		, pixel_values=A		, attention_mask=A		, token_type_ids=A		, start_positions=A		, end_positions=A		, )
          self.parent.assertEqual(result.start_logits.shape		, (self.batch_size, self.seq_length)      )
          self.parent.assertEqual(result.end_logits.shape		, (self.batch_size, self.seq_length)      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Tuple:
          lowerCamelCase__       :						Tuple        =							self.prepare_config_and_inputs()
          (
              (
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							
          )       :						Any        =							config_and_inputs
          lowerCamelCase__       :						List[Any]        =							{
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''pixel_values''': pixel_values,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict


@require_torch
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Tuple			   =   False
       _UpperCAmelCase				:  Union[str, Any]			   =   False
       _UpperCAmelCase				:  List[str]			   =   False

       _UpperCAmelCase				:  Dict			   =   (
           (
               LayoutLMvaModel,
               LayoutLMvaForSequenceClassification,
               LayoutLMvaForTokenClassification,
               LayoutLMvaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       _UpperCAmelCase				:  int			   =   (
           {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
           if is_torch_available()
           else {}
       )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     List[Any]		, A					:     Optional[int]		, A					:     List[str]		, A					:     Optional[int]		, A					:     List[str]      )       ->List[Any]:
          # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
          # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
          # the sequence dimension of the text embedding only.
          # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
          return True







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Dict:
          lowerCamelCase__       :						int        =							LayoutLMvaModelTester(self      )
          lowerCamelCase__       :						Optional[Any]        =							ConfigTester(self		, config_class=A		, hidden_size=3_7      )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Optional[Any]		, A					:     Optional[int]		, A					:     Optional[Any]=False      )       ->Dict:
          lowerCamelCase__       :						Optional[Any]        =							copy.deepcopy(A      )
          if model_class in get_values(A      ):
             lowerCamelCase__       :						int        =							{
                 k: v.unsqueeze(1      ).expand(-1		, self.model_tester.num_choices		, -1      ).contiguous()
                 if isinstance(A		, torch.Tensor      ) and v.ndim > 1
                 else v
                 for k, v in inputs_dict.items()
             }
          if return_labels:
             if model_class in get_values(A      ):
                lowerCamelCase__       :						Tuple        =							torch.ones(self.model_tester.batch_size		, dtype=torch.long		, device=A      )
             elif model_class in get_values(A      ):
                lowerCamelCase__       :						int        =							torch.zeros(
                    self.model_tester.batch_size		, dtype=torch.long		, device=A      )
                lowerCamelCase__       :						List[str]        =							torch.zeros(
                    self.model_tester.batch_size		, dtype=torch.long		, device=A      )
             elif model_class in [
                 *get_values(A      ),
             ]:
                lowerCamelCase__       :						str        =							torch.zeros(
                    self.model_tester.batch_size		, dtype=torch.long		, device=A      )
             elif model_class in [
                 *get_values(A      ),
             ]:
                lowerCamelCase__       :						Tuple        =							torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.text_seq_length)		, dtype=torch.long		, device=A		, )

          return inputs_dict







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Tuple:
          self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     int      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						str        =							self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
             lowerCamelCase__       :						List[Any]        =							type
             self.model_tester.create_and_check_model(*A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->List[str]:
          lowerCamelCase__       :						str        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->int:
          lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A      )







       @slow
       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[str]:
          for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						str        =							LayoutLMvaModel.from_pretrained(A      )
             self.assertIsNotNone(A      )



def 	_a						(   )    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
   return image




@require_torch
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @cached_property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->int:
          return LayoutLMvaImageProcessor(apply_ocr=A      ) if is_vision_available() else None







       @slow
       def 						__lowerCamelCase   (				self					:     Any      )       ->str:
          lowerCamelCase__       :						List[str]        =							LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''      ).to(A      )

          lowerCamelCase__       :						int        =							self.default_image_processor
          lowerCamelCase__       :						Optional[int]        =							prepare_img()
          lowerCamelCase__       :						Tuple        =							image_processor(images=A		, return_tensors='''pt'''      ).pixel_values.to(A      )

          lowerCamelCase__       :						Tuple        =							torch.tensor([[1, 2]]      )
          lowerCamelCase__       :						Optional[int]        =							torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]      ).unsqueeze(0      )

          # forward pass
          lowerCamelCase__       :						List[Any]        =							model(
              input_ids=input_ids.to(A      )		, bbox=bbox.to(A      )		, pixel_values=pixel_values.to(A      )		, )

          # verify the logits
          lowerCamelCase__       :						List[Any]        =							torch.Size((1, 1_9_9, 7_6_8)      )
          self.assertEqual(outputs.last_hidden_state.shape		, A      )

          lowerCamelCase__       :						Tuple        =							torch.tensor(
              [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]      ).to(A      )

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		, A		, atol=1e-4      )      )










import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
       import tensorflow as tf
       from tensorflow.python.framework.errors_impl import ResourceExhaustedError

       from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
       import pyanvml.pyanvml as nvml

_A :   Any  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   def run_func(UpperCAmelCase					):
      @wraps(UpperCAmelCase					)
      def run_in_eager_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      @wraps(UpperCAmelCase					)
      @tf.function(experimental_compile=UpperCAmelCase					)
      def run_in_graph_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      if do_eager_mode is True:
         if use_xla is not False:
            raise ValueError(
                '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''					)
         return run_in_eager_mode
      else:
         return run_in_graph_mode

   return run_func



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    ["tf.Tensor"]:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							random.Random()
   lowerCamelCase__       :						str        =							[rng.randint(0      ,  vocab_size - 1					) for i in range(batch_size * sequence_length					)]
   return tf.constant(UpperCAmelCase      ,  shape=(batch_size, sequence_length)      ,  dtype=tf.intaa					)




class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  TensorFlowBenchmarkArguments
       _UpperCAmelCase				:  PretrainedConfig
       _UpperCAmelCase				:  str			   =   "TensorFlow"







       @property
       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          return tf.__version__







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          # initialize GPU on separate process
          lowerCamelCase__       :						Dict        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						int        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_speed(_inference      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          lowerCamelCase__       :						Optional[int]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						List[Any]        =							self._prepare_train_func(A		, A		, A      )
          return self._measure_speed(_train      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     str		, A					:     int		, A					:     int      )       ->[Memory, Optional[MemorySummary]]:
          # initialize GPU on separate process
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						int        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						str        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_memory(_inference      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->[Memory, Optional[MemorySummary]]:
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						List[Any]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )

          lowerCamelCase__       :						str        =							self._prepare_train_func(A		, A		, A      )
          return self._measure_memory(_train      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Tuple        =							(
              hasattr(A		, '''architectures'''      )
              and isinstance(config.architectures		, A      )
              and len(config.architectures      ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[Any]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						int        =							getattr(A		, A      )
                lowerCamelCase__       :						int        =							model_cls(A      )
             except ImportError:
                raise ImportError(
                    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''      )
          else:
             lowerCamelCase__       :						Union[str, Any]        =							TF_MODEL_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Tuple        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Optional[Any]        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_forward():
             return model(A		, decoder_input_ids=A		, training=A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_forward():
             return model(A		, training=A      )

          lowerCamelCase__       :						int        =							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

          return _inference







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.eager_mode is not False:
             raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''      )

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Optional[int]        =							(
              hasattr(A		, '''architectures'''      )
              and isinstance(config.architectures		, A      )
              and len(config.architectures      ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[str]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						Optional[int]        =							getattr(A		, A      )
                lowerCamelCase__       :						Optional[Any]        =							model_cls(A      )
             except ImportError:
                raise ImportError(
                    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''      )
          else:
             lowerCamelCase__       :						List[str]        =							TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Optional[int]        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Dict        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_train():
             lowerCamelCase__       :						int        =							model(A		, decoder_input_ids=A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[Any]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_train():
             lowerCamelCase__       :						Optional[int]        =							model(A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[str]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          lowerCamelCase__       :						Tuple        =							encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Any      )       ->float:
          with self.args.strategy.scope():
             try:
                if self.args.is_tpu or self.args.use_xla:
                   # run additional 10 times to stabilize compilation for tpu
                   logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation'''      )
                   timeit.repeat(A		, repeat=1		, number=5      )

                # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                lowerCamelCase__       :						Optional[Any]        =							timeit.repeat(
                    A		, repeat=self.args.repeat		, number=1_0		, )

                return min(A      ) / 10.0
             except ResourceExhaustedError as e:
                self.print_fn(F"Doesn't fit on GPU. {e}"      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Callable[[], None]      )       ->[Memory, MemorySummary]:
          logger.info(
              '''Note that TensorFlow allocates more memory than '''
              '''it might need to speed up computation. '''
              '''The memory reported here corresponds to the memory '''
              '''reported by `nvidia-smi`, which can vary depending '''
              '''on total available memory on the GPU that is used.'''      )
          with self.args.strategy.scope():
             try:
                if self.args.trace_memory_line_by_line:
                   if not self.args.eager_mode:
                      raise ValueError(
                          '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
                          ''' consumption line by line.'''      )
                   lowerCamelCase__       :						Union[str, Any]        =							start_memory_tracing('''transformers'''      )

                if self.args.is_tpu:
                   # tpu
                   raise NotImplementedError(
                       '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
                       ''' with `args.memory=False`'''      )
                elif self.args.is_gpu:
                   # gpu
                   if not is_pyanvml_available():
                      logger.warning(
                          '''py3nvml not installed, we won\'t log GPU memory usage. '''
                          '''Install py3nvml (pip install py3nvml) to log information about GPU.'''      )
                      lowerCamelCase__       :						Union[str, Any]        =							'''N/A'''
                   else:
                      logger.info(
                          '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
                          ''' running on the same GPU.'''      )
                      # init nvml
                      nvml.nvmlInit()
                      func()
                      lowerCamelCase__       :						Any        =							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
                      lowerCamelCase__       :						Optional[int]        =							nvml.nvmlDeviceGetMemoryInfo(A      )
                      lowerCamelCase__       :						List[Any]        =							meminfo.used
                      lowerCamelCase__       :						Union[str, Any]        =							Memory(A      )
                      # shutdown nvml
                      nvml.nvmlShutdown()
                else:
                   # cpu
                   if self.args.trace_memory_line_by_line:
                      logger.info(
                          '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
                          ''' TensorFlow.'''      )
                      lowerCamelCase__       :						Tuple        =							None
                   else:
                      lowerCamelCase__       :						Dict        =							measure_peak_memory_cpu(A      )
                      lowerCamelCase__       :						Optional[Any]        =							Memory(A      ) if isinstance(A		, A      ) else memory_bytes
                if self.args.trace_memory_line_by_line:
                   lowerCamelCase__       :						Union[str, Any]        =							stop_memory_tracing(A      )
                   if memory is None:
                      lowerCamelCase__       :						Dict        =							summary.total
                else:
                   lowerCamelCase__       :						Optional[int]        =							None

                return memory, summary
             except ResourceExhaustedError as e:
                self.print_fn(F"Doesn't fit on GPU. {e}"      )
                return "N/A", None



