




import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch






class 					__lowerCAmelCase				( unittest.TestCase				):







			@patch("sys.argv"					,					["fakeprogrampath", "env"]   )
			def UpperCamelCase    ( self					:    List[str]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
									self.assertIn("Python version"					,					cs.out   )
									self.assertIn("Platform"					,					cs.out   )
									self.assertIn("Using distributed or parallel set-up in script?"					,					cs.out   )







			@is_pt_tf_cross_test
			@patch(
			    "sys.argv"					,					["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"					,					ignore_errors=snake_case__   )  # cleans potential past runs
									transformers.commands.transformers_cli.main()

									# The original repo has no TF weights -- if they exist, they were created by the CLI
									self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )







			@require_torch
			@patch("sys.argv"					,					["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )







			@require_torch
			@patch(
			    "sys.argv"					,					[
			        "fakeprogrampath",
			        "download",
			        "hf-internal-testing/test_dynamic_model_with_tokenizer",
			        "--trust-remote-code",
			        "--cache-dir",
			        "/tmp",
			    ]					,					)
			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
									self.assertTrue(
									    os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )










import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
				import torch

				from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
				import pyanvml.pyanvml as nvml


lowercase_			:       int						       =				logging.get_logger(__name__)






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				PyTorchBenchmarkArguments
			snake_case_   :				PretrainedConfig
			snake_case_   :				str				  =  "PyTorch"







			@property
			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									return torch.__version__







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_inference_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_speed(_inference   )







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_inference_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_memory(_inference   )







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_train_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_speed(_train   )







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_train_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_memory(_train   )







			def UpperCamelCase    ( self					:    Dict					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

									if self.args.torchscript:
															_UpperCAmelCase									=  True

									_UpperCAmelCase									=  (
									    hasattr(snake_case__					,					"architectures"   )
									    and isinstance(config.architectures					,					snake_case__   )
									    and len(config.architectures   ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_UpperCAmelCase									=  config.architectures[0]
																					_UpperCAmelCase									=  __import__("transformers"					,					fromlist=[model_class]   )
																					_UpperCAmelCase									=  getattr(snake_case__					,					snake_case__   )
																					_UpperCAmelCase									=  model_cls(snake_case__   )
															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:
															_UpperCAmelCase									=  MODEL_MAPPING[config.__class__](snake_case__   )

									model.eval()
									model.to(self.args.device   )

									# encoder-decoder has vocab size saved differently
									_UpperCAmelCase									=  config.vocab_size if hasattr(snake_case__					,					"vocab_size"   ) else config.encoder.vocab_size
									_UpperCAmelCase									=  torch.randint(snake_case__					,					(batch_size, sequence_length)					,					dtype=torch.long					,					device=self.args.device   )

									if self.args.fpaa:
															logger.info("Running training in Mixed Precision..."   )
															if not self.args.is_gpu:
																					raise ValueError("Mixed precision is possible only for GPU."   )
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									if self.args.torchscript:
															with torch.no_grad():
																					_UpperCAmelCase									=  torch.jit.trace(snake_case__					,					snake_case__   )
									else:
															_UpperCAmelCase									=  model

									def encoder_decoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__					,					decoder_input_ids=snake_case__   )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__   )
															return outputs

									_UpperCAmelCase									=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

									_UpperCAmelCase									=  (
									    hasattr(snake_case__					,					"architectures"   )
									    and isinstance(config.architectures					,					snake_case__   )
									    and len(config.architectures   ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_UpperCAmelCase									=  config.architectures[0]
																					_UpperCAmelCase									=  __import__("transformers"					,					fromlist=[model_class]   )
																					_UpperCAmelCase									=  getattr(snake_case__					,					snake_case__   )
																					_UpperCAmelCase									=  model_cls(snake_case__   )
															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:
															_UpperCAmelCase									=  MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case__   )

									if self.args.torchscript:
															raise NotImplementedError("Training for torchscript is currently not implemented"   )
									else:
															_UpperCAmelCase									=  model

									model.train()
									model.to(self.args.device   )

									# encoder-decoder has vocab size saved differently
									_UpperCAmelCase									=  config.vocab_size if hasattr(snake_case__					,					"vocab_size"   ) else config.encoder.vocab_size
									_UpperCAmelCase									=  torch.randint(snake_case__					,					(batch_size, sequence_length)					,					dtype=torch.long					,					device=self.args.device   )

									if self.args.fpaa:
															logger.info("Running training in Mixed Precision..."   )
															if not self.args.is_gpu:
																					raise ValueError("Mixed precision is possible only for GPU."   )

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									def compute_loss_and_backprob_encoder():
															_UpperCAmelCase									=  train_model(snake_case__					,					labels=snake_case__   )[0]
															loss.backward()
															return loss

									def compute_loss_and_backprob_encoder_decoder():
															_UpperCAmelCase									=  train_model(snake_case__					,					decoder_input_ids=snake_case__					,					labels=snake_case__   )[0]
															loss.backward()
															return loss

									_UpperCAmelCase									=  (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    Tuple   ):




									"""simple docstring"""


									try:
															if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"   )
																					timeit.repeat(
																					    snake_case__					,					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
															_UpperCAmelCase									=  timeit.repeat(
															    snake_case__					,					repeat=self.args.repeat					,					number=10					,					)

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report()   )

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







			def UpperCamelCase    ( self					:    Tuple					,					snake_case__					:    Callable[[], None]   ):




									"""simple docstring"""


									try:
															if self.args.trace_memory_line_by_line:
																					_UpperCAmelCase									=  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"
																					    " `--no-memory` or `args.memory=False`"   )
															elif self.args.is_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."   )
																											_UpperCAmelCase									=  "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()
																											_UpperCAmelCase									=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
																											_UpperCAmelCase									=  nvml.nvmlDeviceGetMemoryInfo(snake_case__   )
																											_UpperCAmelCase									=  meminfo.used
																											_UpperCAmelCase									=  Memory(snake_case__   )
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					_UpperCAmelCase									=  measure_peak_memory_cpu(snake_case__   )
																					_UpperCAmelCase									=  Memory(snake_case__   ) if isinstance(snake_case__					,					snake_case__   ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					_UpperCAmelCase									=  stop_memory_tracing(snake_case__   )
															else:
																					_UpperCAmelCase									=  None

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




