


'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class 	lowerCAmelCase__			(			unittest.TestCase	):




   def 							lowerCAmelCase__    (		self  :     Any )   ->Dict:



     '''simple docstring'''







     _UpperCAmelCase		:       Any								=      tempfile.mkdtemp()

     # fmt: off
     _UpperCAmelCase		:       int								=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
     # fmt: on
     _UpperCAmelCase		:       str								=      os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"] )
     with open(self.vocab_file	,  "w"	,  encoding="utf-8" ) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

     _UpperCAmelCase		:       Dict								=      {
         "do_resize": True,
         "size": {"height": 18, "width": 18},
         "do_normalize": True,
         "image_mean": [0.5, 0.5, 0.5],
         "image_std": [0.5, 0.5, 0.5],
     }
     _UpperCAmelCase		:       Optional[Any]								=      os.path.join(self.tmpdirname	,  __lowerCAmelCase )
     with open(self.image_processor_file	,  "w"	,  encoding="utf-8" ) as fp:
       json.dump(__lowerCAmelCase	,  __lowerCAmelCase )




   def 							lowerCAmelCase__    (		self  :     Dict	,  **lowerCamelCase__  :     Dict )   ->int:



     '''simple docstring'''







     return BertTokenizer.from_pretrained(self.tmpdirname	,  **__lowerCAmelCase )




   def 							lowerCAmelCase__    (		self  :     Dict	,  **lowerCamelCase__  :     List[Any] )   ->Union[str, Any]:



     '''simple docstring'''







     return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **__lowerCAmelCase )




   def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->int:



     '''simple docstring'''







     shutil.rmtree(self.tmpdirname )




   def 							lowerCAmelCase__    (		self  :     List[str] )   ->int:



     '''simple docstring'''







     _UpperCAmelCase		:       str								=      [np.random.randint(2_55	,  size=(3, 30, 4_00)	,  dtype=np.uinta )]

     _UpperCAmelCase		:       Optional[Any]								=      [Image.fromarray(np.moveaxis(__lowerCAmelCase	,  0	,  -1 ) ) for x in image_inputs]

     return image_inputs




   def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Optional[int]:



     '''simple docstring'''







     _UpperCAmelCase		:       Union[str, Any]								=      self.get_tokenizer()
     _UpperCAmelCase		:       Optional[Any]								=      self.get_image_processor()

     _UpperCAmelCase		:       List[str]								=      VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase	,  image_processor=__lowerCAmelCase )

     processor.save_pretrained(self.tmpdirname )
     _UpperCAmelCase		:       Any								=      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer.get_vocab() )
     self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor	,  __lowerCAmelCase )




   def 							lowerCAmelCase__    (		self  :     str )   ->List[Any]:



     '''simple docstring'''







     _UpperCAmelCase		:       Optional[Any]								=      VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     _UpperCAmelCase		:       Any								=      self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)" )
     _UpperCAmelCase		:       List[Any]								=      self.get_image_processor(do_normalize=__lowerCAmelCase	,  padding_value=1.0 )

     _UpperCAmelCase		:       int								=      VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=__lowerCAmelCase	,  padding_value=1.0 )

     self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor	,  __lowerCAmelCase )




   def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->str:



     '''simple docstring'''







     _UpperCAmelCase		:       List[str]								=      self.get_image_processor()
     _UpperCAmelCase		:       Tuple								=      self.get_tokenizer()

     _UpperCAmelCase		:       Tuple								=      VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase	,  image_processor=__lowerCAmelCase )

     _UpperCAmelCase		:       Tuple								=      self.prepare_image_inputs()

     _UpperCAmelCase		:       List[Any]								=      image_processor(__lowerCAmelCase	,  return_tensors="np" )
     _UpperCAmelCase		:       Optional[int]								=      processor(images=__lowerCAmelCase	,  return_tensors="np" )

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1E-2 )




   def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Union[str, Any]:



     '''simple docstring'''







     _UpperCAmelCase		:       Any								=      self.get_image_processor()
     _UpperCAmelCase		:       Optional[Any]								=      self.get_tokenizer()

     _UpperCAmelCase		:       str								=      VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase	,  image_processor=__lowerCAmelCase )

     _UpperCAmelCase		:       str								=      "lower newer"

     _UpperCAmelCase		:       Tuple								=      processor(text=__lowerCAmelCase )

     _UpperCAmelCase		:       List[str]								=      tokenizer(__lowerCAmelCase )

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]	,  encoded_processor[key] )




   def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Union[str, Any]:



     '''simple docstring'''







     _UpperCAmelCase		:       List[Any]								=      self.get_image_processor()
     _UpperCAmelCase		:       Union[str, Any]								=      self.get_tokenizer()

     _UpperCAmelCase		:       int								=      VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase	,  image_processor=__lowerCAmelCase )

     _UpperCAmelCase		:       Optional[int]								=      "lower newer"
     _UpperCAmelCase		:       Union[str, Any]								=      self.prepare_image_inputs()

     _UpperCAmelCase		:       List[str]								=      processor(text=__lowerCAmelCase	,  images=__lowerCAmelCase )

     self.assertListEqual(list(inputs.keys() )	,  ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )

     # test if it raises when no input is passed
     with self.assertRaises(__lowerCAmelCase ):
       processor()




   def 							lowerCAmelCase__    (		self  :     Tuple )   ->Dict:



     '''simple docstring'''







     _UpperCAmelCase		:       Union[str, Any]								=      self.get_image_processor()
     _UpperCAmelCase		:       int								=      self.get_tokenizer()

     _UpperCAmelCase		:       str								=      VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase	,  image_processor=__lowerCAmelCase )

     _UpperCAmelCase		:       Tuple								=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     _UpperCAmelCase		:       Dict								=      processor.batch_decode(__lowerCAmelCase )
     _UpperCAmelCase		:       List[str]								=      tokenizer.batch_decode(__lowerCAmelCase )

     self.assertListEqual(__lowerCAmelCase	,  __lowerCAmelCase )






   def 							lowerCAmelCase__    (		self  :     str )   ->Tuple:



     '''simple docstring'''







     _UpperCAmelCase		:       Union[str, Any]								=      self.get_image_processor()
     _UpperCAmelCase		:       List[str]								=      self.get_tokenizer()

     _UpperCAmelCase		:       List[str]								=      VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase	,  image_processor=__lowerCAmelCase )

     _UpperCAmelCase		:       str								=      "lower newer"
     _UpperCAmelCase		:       Optional[Any]								=      self.prepare_image_inputs()

     _UpperCAmelCase		:       Union[str, Any]								=      processor(text=__lowerCAmelCase	,  images=__lowerCAmelCase )

     self.assertListEqual(list(inputs.keys() )	,  processor.model_input_names )







'''simple docstring'''


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
						import transformers






def  __lowerCAmelCase (__lowerCAmelCase   ):
		random.seed(__lowerCAmelCase   )
		np.random.seed(__lowerCAmelCase   )
		torch.manual_seed(__lowerCAmelCase   )
		torch.cuda.manual_seed_all(__lowerCAmelCase   )
		# ^^ safe to call this function even if cuda is not available






class 	lowerCAmelCase__			:




			def __init__(		self  :     List[Any]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter]	,  lowerCamelCase__  :     float = 0.9_9_9_9	,  lowerCamelCase__  :     float = 0.0	,  lowerCamelCase__  :     int = 0	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     Union[float, int] = 1.0	,  lowerCamelCase__  :     Union[float, int] = 2 / 3	,  lowerCamelCase__  :     Optional[Any] = None	,  lowerCamelCase__  :     Dict[str, Any] = None	,  **lowerCamelCase__  :     Optional[int]	,  )   ->Optional[Any]:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       List[Any]								=      (
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       List[str]								=      parameters.parameters()

							# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
							_UpperCAmelCase		:       Optional[int]								=      True

					if kwargs.get("max_value"	,  lowerCamelCase__ ) is not None:
							_UpperCAmelCase		:       Tuple								=      "The `max_value` argument is deprecated. Please use `decay` instead."
							deprecate("max_value"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__ )
							_UpperCAmelCase		:       str								=      kwargs["max_value"]

					if kwargs.get("min_value"	,  lowerCamelCase__ ) is not None:
							_UpperCAmelCase		:       Optional[int]								=      "The `min_value` argument is deprecated. Please use `min_decay` instead."
							deprecate("min_value"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      kwargs["min_value"]

					_UpperCAmelCase		:       Optional[Any]								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      [p.clone().detach() for p in parameters]

					if kwargs.get("device"	,  lowerCamelCase__ ) is not None:
							_UpperCAmelCase		:       Any								=      "The `device` argument is deprecated. Please use `to` instead."
							deprecate("device"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__ )
							self.to(device=kwargs["device"] )

					_UpperCAmelCase		:       Union[str, Any]								=      None

					_UpperCAmelCase		:       int								=      decay
					_UpperCAmelCase		:       Any								=      min_decay
					_UpperCAmelCase		:       Optional[int]								=      update_after_step
					_UpperCAmelCase		:       str								=      use_ema_warmup
					_UpperCAmelCase		:       Union[str, Any]								=      inv_gamma
					_UpperCAmelCase		:       Union[str, Any]								=      power
					_UpperCAmelCase		:       List[str]								=      0
					_UpperCAmelCase		:       List[str]								=      None  # set in `step()`

					_UpperCAmelCase		:       Optional[int]								=      model_cls
					_UpperCAmelCase		:       Union[str, Any]								=      model_config




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     int	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     int )   ->"EMAModel":



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      model_cls.load_config(lowerCamelCase__	,  return_unused_kwargs=lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      model_cls.from_pretrained(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      cls(model.parameters()	,  model_cls=lowerCamelCase__	,  model_config=model.config )

					ema_model.load_state_dict(lowerCamelCase__ )
					return ema_model




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     int )   ->Dict:



					'''simple docstring'''







					if self.model_cls is None:
							raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__." )

					if self.model_config is None:
							raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__." )

					_UpperCAmelCase		:       int								=      self.model_cls.from_config(self.model_config )
					_UpperCAmelCase		:       Union[str, Any]								=      self.state_dict()
					state_dict.pop("shadow_params"	,  lowerCamelCase__ )

					model.register_to_config(**lowerCamelCase__ )
					self.copy_to(model.parameters() )
					model.save_pretrained(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     int )   ->float:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      max(0	,  optimization_step - self.update_after_step - 1 )

					if step <= 0:
							return 0.0

					if self.use_ema_warmup:
							_UpperCAmelCase		:       int								=      1 - (1 + step / self.inv_gamma) ** -self.power
					else:
							_UpperCAmelCase		:       Any								=      (1 + step) / (10 + step)

					_UpperCAmelCase		:       int								=      min(lowerCamelCase__	,  self.decay )
					# make sure decay is not smaller than min_decay
					_UpperCAmelCase		:       Union[str, Any]								=      max(lowerCamelCase__	,  self.min_decay )
					return cur_decay_value




			@torch.no_grad()
			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->Dict:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       Union[str, Any]								=      (
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       Any								=      parameters.parameters()

					_UpperCAmelCase		:       Dict								=      list(lowerCamelCase__ )

					self.optimization_step += 1

					# Compute the decay factor for the exponential moving average.
					_UpperCAmelCase		:       Tuple								=      self.get_decay(self.optimization_step )
					_UpperCAmelCase		:       Any								=      decay
					_UpperCAmelCase		:       Optional[Any]								=      1 - decay

					_UpperCAmelCase		:       Union[str, Any]								=      contextlib.nullcontext
					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							import deepspeed

					for s_param, param in zip(self.shadow_params	,  lowerCamelCase__ ):
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									_UpperCAmelCase		:       str								=      deepspeed.zero.GatheredParameters(lowerCamelCase__	,  modifier_rank=lowerCamelCase__ )

							with context_manager():
									if param.requires_grad:
											s_param.sub_(one_minus_decay * (s_param - param) )
									else:
											s_param.copy_(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      list(lowerCamelCase__ )
					for s_param, param in zip(self.shadow_params	,  lowerCamelCase__ ):
							param.data.copy_(s_param.to(param.device ).data )




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     Dict=None	,  lowerCamelCase__  :     Optional[int]=None )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      [
					    p.to(device=lowerCamelCase__	,  dtype=lowerCamelCase__ ) if p.is_floating_point() else p.to(device=lowerCamelCase__ )
					    for p in self.shadow_params
					]




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->dict:



					'''simple docstring'''







					return {
					    "decay": self.decay,
					    "min_decay": self.min_decay,
					    "optimization_step": self.optimization_step,
					    "update_after_step": self.update_after_step,
					    "use_ema_warmup": self.use_ema_warmup,
					    "inv_gamma": self.inv_gamma,
					    "power": self.power,
					    "shadow_params": self.shadow_params,
					}




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      [param.detach().cpu().clone() for param in parameters]




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					if self.temp_stored_params is None:
							raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`" )
					for c_param, param in zip(self.temp_stored_params	,  lowerCamelCase__ ):
							param.data.copy_(c_param.data )

					# Better memory-wise.
					_UpperCAmelCase		:       int								=      None






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     dict )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      copy.deepcopy(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      state_dict.get("decay"	,  self.decay )
					if self.decay < 0.0 or self.decay > 1.0:
							raise ValueError("Decay must be between 0 and 1" )

					_UpperCAmelCase		:       Union[str, Any]								=      state_dict.get("min_decay"	,  self.min_decay )
					if not isinstance(self.min_decay	,  lowerCamelCase__ ):
							raise ValueError("Invalid min_decay" )

					_UpperCAmelCase		:       List[str]								=      state_dict.get("optimization_step"	,  self.optimization_step )
					if not isinstance(self.optimization_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid optimization_step" )

					_UpperCAmelCase		:       List[Any]								=      state_dict.get("update_after_step"	,  self.update_after_step )
					if not isinstance(self.update_after_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid update_after_step" )

					_UpperCAmelCase		:       str								=      state_dict.get("use_ema_warmup"	,  self.use_ema_warmup )
					if not isinstance(self.use_ema_warmup	,  lowerCamelCase__ ):
							raise ValueError("Invalid use_ema_warmup" )

					_UpperCAmelCase		:       int								=      state_dict.get("inv_gamma"	,  self.inv_gamma )
					if not isinstance(self.inv_gamma	,  (float, int) ):
							raise ValueError("Invalid inv_gamma" )

					_UpperCAmelCase		:       Any								=      state_dict.get("power"	,  self.power )
					if not isinstance(self.power	,  (float, int) ):
							raise ValueError("Invalid power" )

					_UpperCAmelCase		:       List[str]								=      state_dict.get("shadow_params"	,  lowerCamelCase__ )
					if shadow_params is not None:
							_UpperCAmelCase		:       Optional[Any]								=      shadow_params
							if not isinstance(self.shadow_params	,  lowerCamelCase__ ):
									raise ValueError("shadow_params must be a list" )
							if not all(isinstance(lowerCamelCase__	,  torch.Tensor ) for p in self.shadow_params ):
									raise ValueError("shadow_params must all be Tensors" )



