





from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           AutoTokenizer,
           TFAutoModelForCausalLM,
           TFAutoModelForSeqaSeqLM,
           TFAutoModelForSpeechSeqaSeq,
           TFAutoModelForVisionaSeq,
           TFBartForConditionalGeneration,
           TFLogitsProcessorList,
           TFMinLengthLogitsProcessor,
           tf_top_k_top_p_filtering,
       )

if is_tensorflow_text_available():
       import tensorflow_text as text


@require_tf
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):






       """simple docstring"""




       def 						__lowerCamelCase   (				self					:     Any      )       ->List[str]:
          lowerCamelCase__       :						Tuple        =							tf.convert_to_tensor(
              [
                  [
                      8.2_22_09_91,  # 3rd highest value; idx. 0
                      -0.5_62_00_44,
                      5.23_22_97_52,
                      4.0_38_63_93,
                      -6.8_79_83_78,
                      -0.54_78_58_02,
                      -3.2_01_21_53,
                      2.92_77_71_76,
                      1.88_17_19_53,
                      7.35_34_12_76,  # 5th highest value; idx. 9
                      8.43_20_78_33,  # 2nd highest value; idx. 10
                      -9.85_71_18_36,
                      -5.96_20_92_36,
                      -1.13_03_91_61,
                      -7.1_11_52_94,
                      -0.8_36_96_33,
                      -5.3_18_64_08,
                      7.06_42_74_07,
                      0.81_36_93_44,
                      -0.82_02_38_17,
                      -5.9_17_97_96,
                      0.58_81_34_43,
                      -6.99_77_84_38,
                      4.71_55_11_89,
                      -0.18_77_16_37,
                      7.44_02_07_59,  # 4th highest value; idx. 25
                      9.38_45_09_87,  # 1st highest value; idx. 26
                      2.12_66_29_41,
                      -9.32_56_20_38,
                      2.35_65_25_22,
                  ],  # cummulative prob of 5 highest values <= 0.6
                  [
                      0.58_42_55_18,
                      4.53_13_92_38,
                      -5.57_51_04_64,
                      -6.28_03_06_99,
                      -7.19_52_95_03,
                      -4.02_12_25_51,
                      1.39_33_70_37,
                      -6.06_70_70_57,
                      1.59_48_05_17,
                      -9.64_31_19,
                      0.03_90_77_99,
                      0.67_23_17_62,
                      -8.88_20_67_26,
                      6.27_11_59_22,  # 4th highest value; idx. 13
                      2.28_52_07_23,
                      4.82_76_75_06,
                      4.30_42_13_68,
                      8.8_27_53_13,  # 2nd highest value; idx. 17
                      5.44_02_99_58,  # 5th highest value; idx. 18
                      -4.4_73_57_94,
                      7.38_57_95_36,  # 3rd highest value; idx. 20
                      -2.91_05_16_63,
                      2.61_94_60_77,
                      -2.5_67_47_62,
                      -9.48_95_93_02,
                      -4.02_92_26_45,
                      -1.35_41_69_18,
                      9.67_70_23_23,  # 1st highest value; idx. 27
                      -5.89_47_85_53,
                      1.85_37_04_67,
                  ],  # cummulative prob of 5 highest values <= 0.6
              ]		, dtype=tf.floataa		, )

          lowerCamelCase__       :						Optional[int]        =							tf.convert_to_tensor(
              [[0, 0], [0, 9], [0, 1_0], [0, 2_5], [0, 2_6], [1, 1_3], [1, 1_7], [1, 1_8], [1, 2_0], [1, 2_7]]		, dtype=tf.intaa		, )  # expected non filtered idx as noted above

          lowerCamelCase__       :						Any        =							tf.convert_to_tensor(
              [8.22_20_99, 7.3_53_41_26, 8.43_20_78, 7.4_40_20_75, 9.3_84_51, 6.27_11_59, 8.82_75_31, 5.4_40_29_95, 7.3_85_79_56, 9.67_70_23]		, dtype=tf.floataa		, )  # expected non filtered values as noted above

          lowerCamelCase__       :						List[Any]        =							tf_top_k_top_p_filtering(_SCREAMING_SNAKE_CASE		, top_k=1_0		, top_p=0.6		, min_tokens_to_keep=4      )

          lowerCamelCase__       :						Any        =							output[output != -float('''inf'''      )]
          lowerCamelCase__       :						Any        =							tf.cast(
              tf.where(tf.not_equal(_SCREAMING_SNAKE_CASE		, tf.constant(-float('''inf'''      )		, dtype=tf.floataa      )      )      )		, dtype=tf.intaa		, )

          tf.debugging.assert_near(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, rtol=1e-12      )
          tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE      )


@require_tf
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase					,SCREAMING_SNAKE_CASE__ ):






       """simple docstring"""




       if is_tf_available():
              _UpperCAmelCase				:  Any			   =   {
                  'AutoModelForCausalLM': TFAutoModelForCausalLM,
                  'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
                  'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
                  'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
                  'LogitsProcessorList': TFLogitsProcessorList,
                  'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
                  'create_tensor_fn': tf.convert_to_tensor,
                  'floats_tensor': floats_tensor,
                  'return_tensors': 'tf',
              }







       @slow
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Dict:
          # TF-only test: tf.saved_model export
          lowerCamelCase__       :						List[Any]        =							TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
          lowerCamelCase__       :						List[str]        =							2
          lowerCamelCase__       :						int        =							2


          class        __SCREAMING_SNAKE_CASE      (			tf.Module ):






                 """simple docstring"""




                 def __init__(				self					:     str		, A					:     List[Any]      )       ->Union[str, Any]:
                    super(_SCREAMING_SNAKE_CASE		, self      ).__init__()
                    lowerCamelCase__       :						Tuple        =							model







                 @tf.function(
                     input_signature=(
                         tf.TensorSpec((None, input_length)		, tf.intaa		, name='''input_ids'''      ),
                         tf.TensorSpec((None, input_length)		, tf.intaa		, name='''attention_mask'''      ),
                     )		, jit_compile=_SCREAMING_SNAKE_CASE		, )
                 def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     Any		, A					:     Union[str, Any]      )       ->int:
                    lowerCamelCase__       :						Tuple        =							self.model.generate(
                        input_ids=_SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, max_new_tokens=_SCREAMING_SNAKE_CASE		, return_dict_in_generate=_SCREAMING_SNAKE_CASE		, )
                    return {"sequences": outputs["sequences"]}


          lowerCamelCase__       :						Any        =							[[2, 0], [1_0_2, 1_0_3]]
          lowerCamelCase__       :						List[Any]        =							[[1, 0], [1, 1]]
          lowerCamelCase__       :						List[Any]        =							DummyModel(model=_SCREAMING_SNAKE_CASE      )
          with tempfile.TemporaryDirectory() as tmp_dir:
             tf.saved_model.save(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, signatures={'''serving_default''': dummy_model.serving}      )
             lowerCamelCase__       :						Tuple        =							tf.saved_model.load(_SCREAMING_SNAKE_CASE      ).signatures["serving_default"]
             for batch_size in range(1		, len(_SCREAMING_SNAKE_CASE      ) + 1      ):
                lowerCamelCase__       :						str        =							{
                    "input_ids": tf.constant(dummy_input_ids[:batch_size]      ),
                    "attention_mask": tf.constant(dummy_attention_masks[:batch_size]      ),
                }
                lowerCamelCase__       :						Union[str, Any]        =							serving_func(**_SCREAMING_SNAKE_CASE      )["sequences"]
                lowerCamelCase__       :						List[str]        =							test_model.generate(**_SCREAMING_SNAKE_CASE		, max_new_tokens=_SCREAMING_SNAKE_CASE      )
                tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE      )







       @slow
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->str:
          # TF-only test: tf.saved_model export
          lowerCamelCase__       :						Optional[Any]        =							TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
          lowerCamelCase__       :						List[str]        =							1
          lowerCamelCase__       :						Union[str, Any]        =							2


          class        __SCREAMING_SNAKE_CASE      (			tf.Module ):






                 """simple docstring"""




                 def __init__(				self					:     List[Any]		, A					:     Any      )       ->List[Any]:
                    super(_SCREAMING_SNAKE_CASE		, self      ).__init__()
                    lowerCamelCase__       :						Dict        =							model







                 @tf.function(
                     input_signature=(
                         tf.TensorSpec((batch_size, None)		, tf.intaa		, name='''input_ids'''      ),
                         tf.TensorSpec((batch_size, None)		, tf.intaa		, name='''attention_mask'''      ),
                     )		, jit_compile=_SCREAMING_SNAKE_CASE		, )
                 def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Union[str, Any]		, A					:     Optional[Any]      )       ->Optional[int]:
                    lowerCamelCase__       :						int        =							self.model.generate(
                        input_ids=_SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, max_new_tokens=_SCREAMING_SNAKE_CASE		, return_dict_in_generate=_SCREAMING_SNAKE_CASE		, )
                    return {"sequences": outputs["sequences"]}


          lowerCamelCase__       :						Union[str, Any]        =							[[2], [1_0_2, 1_0_3]]
          lowerCamelCase__       :						int        =							[[1], [1, 1]]
          lowerCamelCase__       :						List[str]        =							DummyModel(model=_SCREAMING_SNAKE_CASE      )
          with tempfile.TemporaryDirectory() as tmp_dir:
             tf.saved_model.save(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, signatures={'''serving_default''': dummy_model.serving}      )
             lowerCamelCase__       :						Any        =							tf.saved_model.load(_SCREAMING_SNAKE_CASE      ).signatures["serving_default"]
             for input_row in range(len(_SCREAMING_SNAKE_CASE      )      ):
                lowerCamelCase__       :						int        =							{
                    "input_ids": tf.constant([dummy_input_ids[input_row]]      ),
                    "attention_mask": tf.constant([dummy_attention_masks[input_row]]      ),
                }
                lowerCamelCase__       :						Optional[int]        =							serving_func(**_SCREAMING_SNAKE_CASE      )["sequences"]
                lowerCamelCase__       :						Tuple        =							test_model.generate(**_SCREAMING_SNAKE_CASE		, max_new_tokens=_SCREAMING_SNAKE_CASE      )
                tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE      )







       @slow
       @require_tensorflow_text
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->int:
          # TF-only test: tf.saved_model export
          with tempfile.TemporaryDirectory() as tmp_dir:
             # file needed to load the TF tokenizer
             hf_hub_download(repo_id='''google/flan-t5-small'''		, filename='''spiece.model'''		, local_dir=_SCREAMING_SNAKE_CASE      )


             class        __SCREAMING_SNAKE_CASE      (			tf.keras.layers.Layer ):






                    """simple docstring"""




                    def __init__(				self					:     Dict      )       ->Dict:
                       super().__init__()
                       lowerCamelCase__       :						List[str]        =							text.SentencepieceTokenizer(
                           model=tf.io.gfile.GFile(os.path.join(_SCREAMING_SNAKE_CASE		, '''spiece.model'''      )		, '''rb'''      ).read()      )
                       lowerCamelCase__       :						List[str]        =							TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''      )







                    def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     List[str]		, *A					:     str		, **A					:     Union[str, Any]      )       ->List[str]:
                       lowerCamelCase__       :						int        =							self.tokenizer.tokenize(_SCREAMING_SNAKE_CASE      )
                       lowerCamelCase__       :						Union[str, Any]        =							text.pad_model_inputs(
                           _SCREAMING_SNAKE_CASE		, max_seq_length=6_4		, pad_value=self.model.config.pad_token_id      )
                       lowerCamelCase__       :						Optional[Any]        =							self.model.generate(input_ids=_SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE      )
                       return self.tokenizer.detokenize(_SCREAMING_SNAKE_CASE      )


             lowerCamelCase__       :						int        =							CompleteSentenceTransformer()
             lowerCamelCase__       :						Dict        =							tf.keras.layers.Input(shape=(1,)		, dtype=tf.string		, name='''inputs'''      )
             lowerCamelCase__       :						Any        =							complete_model(_SCREAMING_SNAKE_CASE      )
             lowerCamelCase__       :						Optional[int]        =							tf.keras.Model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE      )
             keras_model.save(_SCREAMING_SNAKE_CASE      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          # Has PT equivalent: this test relies on random sampling
          lowerCamelCase__       :						int        =							{
              "do_sample": True,
              "num_beams": 1,
              "top_p": 0.7,
              "top_k": 1_0,
              "temperature": 0.7,
          }
          lowerCamelCase__       :						List[Any]        =							1_4

          lowerCamelCase__       :						Optional[int]        =							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
          lowerCamelCase__       :						int        =							"Hello, my dog is cute and"
          lowerCamelCase__       :						Dict        =							tokenizer(_SCREAMING_SNAKE_CASE		, return_tensors='''tf'''      )
          lowerCamelCase__       :						List[str]        =							TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )

          lowerCamelCase__       :						List[Any]        =							6_3_8
          # forces the generation to happen on CPU, to avoid GPU-related quirks
          with tf.device(''':/CPU:0'''      ):
             tf.random.set_seed(0      )
             lowerCamelCase__       :						Union[str, Any]        =							model.generate(**_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE      )
          self.assertTrue(expectation == len(generated_tokens[0]      )      )

          lowerCamelCase__       :						List[str]        =							[6_3_8, 1_9_8]
          with tf.device(''':/CPU:0'''      ):
             tf.random.set_seed(0      )
             lowerCamelCase__       :						Dict        =							model.generate(**_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE      )
          self.assertTrue(expectation == len(generated_tokens[0]      )      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[Any]:
          # Has PT equivalent: ample use of framework-specific code
          lowerCamelCase__       :						Optional[int]        =							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''      )
          lowerCamelCase__       :						List[str]        =							"Hugging Face is a technology company based in New York and Paris."
          lowerCamelCase__       :						List[Any]        =							bart_tokenizer(_SCREAMING_SNAKE_CASE		, return_tensors='''tf'''      ).input_ids
          lowerCamelCase__       :						List[Any]        =							TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''      )
          lowerCamelCase__       :						int        =							bart_model.generate(_SCREAMING_SNAKE_CASE      ).numpy()


          class        __SCREAMING_SNAKE_CASE      (			SCREAMING_SNAKE_CASE__ ):






                 """simple docstring"""




                 def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     int		, A					:     Any=None		, **A					:     Tuple      )       ->Any:
                    return super().call(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE      )


          lowerCamelCase__       :						Dict        =							FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''      )
          lowerCamelCase__       :						Tuple        =							bart_model.generate(_SCREAMING_SNAKE_CASE		, foo='''bar'''      ).numpy()
          self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE      )      )


          class        __SCREAMING_SNAKE_CASE      (			bart_model.model.encoder.__class__ ):






                 """simple docstring"""




                 def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Tuple		, **A					:     Dict      )       ->List[Any]:
                    return super().call(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE      )


          lowerCamelCase__       :						List[str]        =							FakeEncoder(bart_model.config		, bart_model.model.shared      )
          lowerCamelCase__       :						List[Any]        =							fake_encoder

          # Normal generation still works (the output will be different because the encoder weights are different)
          lowerCamelCase__       :						Optional[Any]        =							bart_model.generate(_SCREAMING_SNAKE_CASE      ).numpy()
          with self.assertRaises(_SCREAMING_SNAKE_CASE      ):
             # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
             bart_model.generate(_SCREAMING_SNAKE_CASE		, foo='''bar'''      )










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



