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 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   int							):
   for model_result in results.values():
    for batch_size, sequence_length in zip(model_result["bs"]  ,				model_result["ss"]							):
     __a							=	model_result["result"][batch_size][sequence_length]
     self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"sgugger/tiny-distilbert-classification"
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				only_pretrain_model=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				torchscript=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	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 		_UpperCAmelCase (		self			:   int							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				fpaa=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   # set architectures equal to `None`
   __a							=	None
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE  ,				configs=[config]							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	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 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				fpaa=__SCREAMING_SNAKE_CASE  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result							)
   self.check_results_dict_not_empty(results.memory_train_result							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE  ,				configs=[config]							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	"sshleifer/tinier_bart"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE  ,				configs=[config]							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE  ,				configs=[config]							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result							)
   self.check_results_dict_not_empty(results.memory_train_result							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	"sshleifer/tinier_bart"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	PyTorchBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE  ,				configs=[config]							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result							)
   self.check_results_dict_not_empty(results.memory_train_result							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	"sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
    __a							=	PyTorchBenchmarkArguments(
        models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				save_to_csv=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				inference_time_csv_file=os.path.join(__SCREAMING_SNAKE_CASE  ,				"inf_time.csv"							)  ,				train_memory_csv_file=os.path.join(__SCREAMING_SNAKE_CASE  ,				"train_mem.csv"							)  ,				inference_memory_csv_file=os.path.join(__SCREAMING_SNAKE_CASE  ,				"inf_mem.csv"							)  ,				train_time_csv_file=os.path.join(__SCREAMING_SNAKE_CASE  ,				"train_time.csv"							)  ,				env_info_csv_file=os.path.join(__SCREAMING_SNAKE_CASE  ,				"env.csv"							)  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
    __a							=	PyTorchBenchmark(__SCREAMING_SNAKE_CASE							)
    benchmark.run()
    self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE  ,				"inf_time.csv"							)							).exists()							)
    self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE  ,				"train_time.csv"							)							).exists()							)
    self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE  ,				"inf_mem.csv"							)							).exists()							)
    self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE  ,				"train_mem.csv"							)							).exists()							)
    self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE  ,				"env.csv"							)							).exists()							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	"sshleifer/tiny-gpt2"

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

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



import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			:						Dict				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Tuple				  =		{
    """google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
    """google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
    """google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_text_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=4_94_08  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=20_48  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=8  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   Tuple="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Any=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.02  ,				__SCREAMING_SNAKE_CASE			:   int=1.0  ,				__SCREAMING_SNAKE_CASE			:   int=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_06  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_07  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	max_position_embeddings
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	attention_dropout
   __a							=	initializer_range
   __a							=	initializer_factor

  @classmethod
  def 		_UpperCAmelCase (		cls			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the text config dict if we are loading from OwlViTConfig
   if config_dict.get("model_type"							) == "owlvit":
    __a							=	config_dict["text_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_vision_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=30_72  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   str=3  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=32  ,				__SCREAMING_SNAKE_CASE			:   Dict="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   int=0.0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Dict=1.0  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	num_channels
   __a							=	image_size
   __a							=	patch_size
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	attention_dropout
   __a							=	initializer_range
   __a							=	initializer_factor

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Tuple							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the vision config dict if we are loading from OwlViTConfig
   if config_dict.get("model_type"							) == "owlvit":
    __a							=	config_dict["vision_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				__SCREAMING_SNAKE_CASE			:   str=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2.65_92  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   if text_config is None:
    __a							=	{}
    logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."							)

   if vision_config is None:
    __a							=	{}
    logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."							)

   __a							=	OwlViTTextConfig(**__SCREAMING_SNAKE_CASE							)
   __a							=	OwlViTVisionConfig(**__SCREAMING_SNAKE_CASE							)

   __a							=	projection_dim
   __a							=	logit_scale_init_value
   __a							=	return_dict
   __a							=	1.0

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	{}
   __a							=	text_config
   __a							=	vision_config

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.text_config.to_dict()
   __a							=	self.vision_config.to_dict()
   __a							=	self.__class__.model_type
   return output
class  A_			(							a_			):

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   return OrderedDict(
       [
           ("input_ids", {0: "batch", 1: "sequence"}),
           ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ("attention_mask", {0: "batch", 1: "sequence"}),
       ]							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return OrderedDict(
       [
           ("logits_per_image", {0: "batch"}),
           ("logits_per_text", {0: "batch"}),
           ("text_embeds", {0: "batch"}),
           ("image_embeds", {0: "batch"}),
       ]							)

  @property
  def 		_UpperCAmelCase (		self			:   str							):
   return 1E-4

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   "ProcessorMixin"  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   Optional["TensorType"] = None  ,				):
   __a							=	super().generate_dummy_inputs(
       processor.tokenizer  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				seq_length=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)
   __a							=	super().generate_dummy_inputs(
       processor.image_processor  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)
   return {**text_input_dict, **image_input_dict}

  @property
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   return 14


