
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


lowercase  :					int													=       logging.get_logger(__name__)

lowercase  :					Union[str, Any]													=       {
    '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  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'owlvit_text_model'







	def __init__(					self		:Optional[Any]     ,   a		:List[str]=4_9_4_0_8     ,   a		:Any=5_1_2     ,   a		:int=2_0_4_8     ,   a		:Tuple=1_2     ,   a		:List[str]=8     ,   a		:Optional[int]=1_6     ,   a		:Optional[Any]="quick_gelu"     ,   a		:str=1E-5     ,   a		:Optional[Any]=0.0     ,   a		:Optional[int]=0.02     ,   a		:Any=1.0     ,   a		:Optional[Any]=0     ,   a		:int=4_9_4_0_6     ,   a		:Union[str, Any]=4_9_4_0_7     ,   **a		:List[str]     ,   )		-> Union[str, Any]:
								super().__init__(pad_token_id=a     ,   bos_token_id=a     ,   eos_token_id=a     ,   **a						)

								__UpperCamelCase	:   Tuple			   =					vocab_size
								__UpperCamelCase	:   int			   =					hidden_size
								__UpperCamelCase	:   Optional[int]			   =					intermediate_size
								__UpperCamelCase	:   Dict			   =					num_hidden_layers
								__UpperCamelCase	:   Tuple			   =					num_attention_heads
								__UpperCamelCase	:   Optional[int]			   =					max_position_embeddings
								__UpperCamelCase	:   str			   =					hidden_act
								__UpperCamelCase	:   Union[str, Any]			   =					layer_norm_eps
								__UpperCamelCase	:   Union[str, Any]			   =					attention_dropout
								__UpperCamelCase	:   Tuple			   =					initializer_range
								__UpperCamelCase	:   Any			   =					initializer_factor







	@classmethod
	def    _lowerCamelCase				(					cls		:int     ,   a		:Union[str, os.PathLike]     ,   **a		:Tuple						)		-> "PretrainedConfig":
								cls._set_token_in_kwargs(a						)

								__UpperCamelCase   ,						__UpperCamelCase	:   Tuple			   =					cls.get_config_dict(a     ,   **a						)

								# get the text config dict if we are loading from OwlViTConfig
								if config_dict.get("model_type"						) == "owlvit":
															__UpperCamelCase	:   List[Any]			   =					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(a     ,   **a						)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'owlvit_vision_model'







	def __init__(					self		:List[Any]     ,   a		:Optional[Any]=7_6_8     ,   a		:Dict=3_0_7_2     ,   a		:Dict=1_2     ,   a		:List[Any]=1_2     ,   a		:Any=3     ,   a		:List[Any]=7_6_8     ,   a		:Optional[Any]=3_2     ,   a		:Dict="quick_gelu"     ,   a		:Union[str, Any]=1E-5     ,   a		:str=0.0     ,   a		:Optional[Any]=0.02     ,   a		:int=1.0     ,   **a		:List[str]     ,   )		-> Dict:
								super().__init__(**a						)

								__UpperCamelCase	:   int			   =					hidden_size
								__UpperCamelCase	:   Optional[int]			   =					intermediate_size
								__UpperCamelCase	:   str			   =					num_hidden_layers
								__UpperCamelCase	:   Tuple			   =					num_attention_heads
								__UpperCamelCase	:   Optional[int]			   =					num_channels
								__UpperCamelCase	:   str			   =					image_size
								__UpperCamelCase	:   Any			   =					patch_size
								__UpperCamelCase	:   List[Any]			   =					hidden_act
								__UpperCamelCase	:   Optional[int]			   =					layer_norm_eps
								__UpperCamelCase	:   Optional[Any]			   =					attention_dropout
								__UpperCamelCase	:   Optional[int]			   =					initializer_range
								__UpperCamelCase	:   Optional[Any]			   =					initializer_factor







	@classmethod
	def    _lowerCamelCase				(					cls		:List[str]     ,   a		:Union[str, os.PathLike]     ,   **a		:Optional[int]						)		-> "PretrainedConfig":
								cls._set_token_in_kwargs(a						)

								__UpperCamelCase   ,						__UpperCamelCase	:   Dict			   =					cls.get_config_dict(a     ,   **a						)

								# get the vision config dict if we are loading from OwlViTConfig
								if config_dict.get("model_type"						) == "owlvit":
															__UpperCamelCase	:   List[Any]			   =					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(a     ,   **a						)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'owlvit'
	_A								=     True







	def __init__(					self		:List[Any]     ,   a		:Union[str, Any]=None     ,   a		:Optional[int]=None     ,   a		:Optional[int]=5_1_2     ,   a		:Optional[int]=2.6592     ,   a		:int=True     ,   **a		:Tuple     ,   )		-> int:
								super().__init__(**a						)

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

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

								__UpperCamelCase	:   str			   =					OwlViTTextConfig(**a						)
								__UpperCamelCase	:   Dict			   =					OwlViTVisionConfig(**a						)

								__UpperCamelCase	:   Tuple			   =					projection_dim
								__UpperCamelCase	:   Optional[int]			   =					logit_scale_init_value
								__UpperCamelCase	:   Union[str, Any]			   =					return_dict
								__UpperCamelCase	:   List[str]			   =					1.0







	@classmethod
	def    _lowerCamelCase				(					cls		:str     ,   a		:Union[str, os.PathLike]     ,   **a		:Optional[Any]						)		-> "PretrainedConfig":
								cls._set_token_in_kwargs(a						)

								__UpperCamelCase   ,						__UpperCamelCase	:   Optional[int]			   =					cls.get_config_dict(a     ,   **a						)

								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(a     ,   **a						)







	@classmethod
	def    _lowerCamelCase				(					cls		:int     ,   a		:Dict     ,   a		:Dict     ,   **a		:Dict						)		-> List[Any]:
								__UpperCamelCase	:   Optional[int]			   =					{}
								__UpperCamelCase	:   str			   =					text_config
								__UpperCamelCase	:   Optional[Any]			   =					vision_config

								return cls.from_dict(a     ,   **a						)







	def    _lowerCamelCase				(					self		:str						)		-> Tuple:
								__UpperCamelCase	:   Tuple			   =					copy.deepcopy(self.__dict__						)
								__UpperCamelCase	:   Optional[Any]			   =					self.text_config.to_dict()
								__UpperCamelCase	:   Optional[int]			   =					self.vision_config.to_dict()
								__UpperCamelCase	:   List[str]			   =					self.__class__.model_type
								return output




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Mapping[str, Mapping[int, str]]:
								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    _lowerCamelCase				(					self		:List[str]						)		-> Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ("logits_per_image", {0: "batch"}),
								        ("logits_per_text", {0: "batch"}),
								        ("text_embeds", {0: "batch"}),
								        ("image_embeds", {0: "batch"}),
								    ]						)







	@property
	def    _lowerCamelCase				(					self		:int						)		-> float:
								return 1E-4







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:"ProcessorMixin"     ,   a		:int = -1     ,   a		:int = -1     ,   a		:Optional["TensorType"] = None     ,   )		-> Mapping[str, Any]:
								__UpperCamelCase	:   Tuple			   =					super().generate_dummy_inputs(
								    processor.tokenizer     ,   batch_size=a     ,   seq_length=a     ,   framework=a						)
								__UpperCamelCase	:   Dict			   =					super().generate_dummy_inputs(
								    processor.image_processor     ,   batch_size=a     ,   framework=a						)
								return {**text_input_dict, **image_input_dict}







	@property
	def    _lowerCamelCase				(					self		:List[Any]						)		-> int:
								return 1_4

# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase  :					Optional[int]													=       Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase  :					Optional[Any]													=       {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowercase  :					str													=       'zero2'
lowercase  :					Optional[int]													=       'zero3'
lowercase  :					Optional[Any]													=       [ZEROa, ZEROa]




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			List[str])					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					parameterized.to_safe_name("_".join(str(_lowerCamelCase) for x in param.args))
							return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase  :					List[str]													=       list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[Any]     ,   a		:str						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str						)		-> List[Any]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]     ,   a		:int						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[Any]     ,   a		:Dict						)		-> int:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[str]						)		-> Optional[Any]:
								# XXX: run_asr is premature and doesn't save any results
								# so all we check for now is that the process didn't fail
								pass







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:bool = True     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Any:
								__UpperCamelCase	:   Optional[Any]			   =					models[model]

								__UpperCamelCase	:   List[Any]			   =					self.run_trainer(
								    stage=a     ,   model_name=a     ,   eval_steps=a     ,   num_train_epochs=1     ,   distributed=a     ,   fpaa=a     ,   )

								self.do_checks(a						)

								return output_dir







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:int = 1     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Dict:
								__UpperCamelCase	:   int			   =					self.get_auto_remove_tmp_dir("./xxx"     ,   after=a						)
								__UpperCamelCase	:   int			   =					f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(a						)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

								if fpaa:
															args.extend(["--fp16"]						)

								# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
								# hence the separate config files
								__UpperCamelCase	:   Dict			   =					f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
								__UpperCamelCase	:   int			   =					[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
								__UpperCamelCase	:   Optional[Any]			   =					self.get_launcher(a						)

								__UpperCamelCase	:   Optional[int]			   =					launcher + script + args + ds_args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(a     ,   env=self.get_env()						)

								return output_dir







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]=False						)		-> List[Any]:
								# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
								# - it won't be able to handle that
								# 2. for now testing with just 2 gpus max (since some quality tests may give different
								# results with mode gpus because we use very little data)
								__UpperCamelCase	:   List[Any]			   =					min(2     ,   get_gpu_count()						) if distributed else 1
								return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()