
"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

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


if is_torch_available():
   from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

@require_torch
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self	,    _a				):
          for model_result in results.values():
                 for batch_size, sequence_length in zip(model_result["bs"]	,    model_result["ss"]				):
                        lowercase    :				List[str]       		=							model_result["result"][batch_size][sequence_length]
                        self.assertIsNotNone(_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							"sshleifer/tiny-gpt2"
          lowercase    :				Dict       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				List[Any]       		=							PyTorchBenchmark(_a				)
          lowercase    :				Dict       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							"sgugger/tiny-distilbert-classification"
          lowercase    :				Optional[int]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    only_pretrain_model=_a	,    )
          lowercase    :				Dict       		=							PyTorchBenchmark(_a				)
          lowercase    :				Optional[int]       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							"sshleifer/tiny-gpt2"
          lowercase    :				List[Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    torchscript=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				Tuple       		=							PyTorchBenchmark(_a				)
          lowercase    :				str       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   @unittest.skipIf(torch_device == "cpu"	,    "Cant do half precision"				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							"sshleifer/tiny-gpt2"
          lowercase    :				Union[str, Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    fpaa=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				str       		=							PyTorchBenchmark(_a				)
          lowercase    :				str       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							"sshleifer/tiny-gpt2"
          lowercase    :				List[Any]       		=							AutoConfig.from_pretrained(_a				)
          # set architectures equal to `None`
          lowercase    :				int       		=							None
          lowercase    :				str       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				List[str]       		=							PyTorchBenchmark(_a	,    configs=[config]				)
          lowercase    :				str       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							"sshleifer/tiny-gpt2"
          lowercase    :				List[Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				Tuple       		=							PyTorchBenchmark(_a				)
          lowercase    :				List[Any]       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)
   @unittest.skipIf(torch_device == "cpu"	,    "Can't do half precision"				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							"sshleifer/tiny-gpt2"
          lowercase    :				List[str]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    fpaa=_a	,    multi_process=_a	,    )
          lowercase    :				List[Any]       		=							PyTorchBenchmark(_a				)
          lowercase    :				List[Any]       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							"sshleifer/tiny-gpt2"
          lowercase    :				str       		=							AutoConfig.from_pretrained(_a				)
          lowercase    :				Union[str, Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				Dict       		=							PyTorchBenchmark(_a	,    configs=[config]				)
          lowercase    :				Any       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							"sshleifer/tinier_bart"
          lowercase    :				Optional[int]       		=							AutoConfig.from_pretrained(_a				)
          lowercase    :				Union[str, Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				List[Any]       		=							PyTorchBenchmark(_a	,    configs=[config]				)
          lowercase    :				Dict       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							"sshleifer/tiny-gpt2"
          lowercase    :				Dict       		=							AutoConfig.from_pretrained(_a				)
          lowercase    :				int       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				Optional[Any]       		=							PyTorchBenchmark(_a	,    configs=[config]				)
          lowercase    :				str       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							"sshleifer/tinier_bart"
          lowercase    :				Union[str, Any]       		=							AutoConfig.from_pretrained(_a				)
          lowercase    :				Optional[Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=_a	,    )
          lowercase    :				Dict       		=							PyTorchBenchmark(_a	,    configs=[config]				)
          lowercase    :				Any       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"sshleifer/tiny-gpt2"
          with tempfile.TemporaryDirectory() as tmp_dir:
                 lowercase    :				Union[str, Any]       		=							PyTorchBenchmarkArguments(
                     models=[MODEL_ID]	,    training=_a	,    inference=_a	,    save_to_csv=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    inference_time_csv_file=os.path.join(_a	,    "inf_time.csv"				)	,    train_memory_csv_file=os.path.join(_a	,    "train_mem.csv"				)	,    inference_memory_csv_file=os.path.join(_a	,    "inf_mem.csv"				)	,    train_time_csv_file=os.path.join(_a	,    "train_time.csv"				)	,    env_info_csv_file=os.path.join(_a	,    "env.csv"				)	,    multi_process=_a	,    )
                 lowercase    :				Optional[int]       		=							PyTorchBenchmark(_a				)
                 benchmark.run()
                 self.assertTrue(Path(os.path.join(_a	,    "inf_time.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(_a	,    "train_time.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(_a	,    "inf_mem.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(_a	,    "train_mem.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(_a	,    "env.csv"				)				).exists()				)

   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							"sshleifer/tiny-gpt2"

          def _check_summary_is_not_empty(_a				):
                 self.assertTrue(hasattr(_a	,    "sequential"				)				)
                 self.assertTrue(hasattr(_a	,    "cumulative"				)				)
                 self.assertTrue(hasattr(_a	,    "current"				)				)
                 self.assertTrue(hasattr(_a	,    "total"				)				)

          with tempfile.TemporaryDirectory() as tmp_dir:
                 lowercase    :				Union[str, Any]       		=							PyTorchBenchmarkArguments(
                     models=[MODEL_ID]	,    training=_a	,    inference=_a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    log_filename=os.path.join(_a	,    "log.txt"				)	,    log_print=_a	,    trace_memory_line_by_line=_a	,    multi_process=_a	,    )
                 lowercase    :				Optional[int]       		=							PyTorchBenchmark(_a				)
                 lowercase    :				Optional[int]       		=							benchmark.run()
                 _check_summary_is_not_empty(result.inference_summary				)
                 _check_summary_is_not_empty(result.train_summary				)
                 self.assertTrue(Path(os.path.join(_a	,    "log.txt"				)				).exists()				)




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A			:       Optional[int]   		=       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Union[str, Any]   		=       ["""LlamaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Dict   		=       ["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       int   		=       [
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   _A			:       str   		=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


