



import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import BridgeTowerImageProcessor
class 					_snake_case      (       unittest.TestCase):




				def __init__(    self       :    Dict,       __lowercase       :    str,       __lowercase       :    Union[str, Any] = True,       __lowercase       :    int = None,       __lowercase       :    Union[str, Any] = 32,       __lowercase       :    Dict = True,       __lowercase       :    str = 1 / 255,       __lowercase       :    int = True,       __lowercase       :    List[Any] = True,       __lowercase       :    Union[str, Any] = [0.48145466, 0.4578275, 0.40821073],       __lowercase       :    int = [0.26862954, 0.26130258, 0.27577711],       __lowercase       :    Union[str, Any] = True,       __lowercase       :    str=7,       __lowercase       :    Tuple=30,       __lowercase       :    Tuple=400,       __lowercase       :    Optional[Any]=3,       ):
							lowercase__       	=					parent
							lowercase__       	=					do_resize
							lowercase__       	=					size if size is not None else {"shortest_edge": 288}
							lowercase__       	=					size_divisor
							lowercase__       	=					do_rescale
							lowercase__       	=					rescale_factor
							lowercase__       	=					do_normalize
							lowercase__       	=					do_center_crop
							lowercase__       	=					image_mean
							lowercase__       	=					image_std
							lowercase__       	=					do_pad
							lowercase__       	=					batch_size
							lowercase__       	=					num_channels
							lowercase__       	=					min_resolution
							lowercase__       	=					max_resolution




				def 				A__   (    self       :    Tuple    ):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}




				def 				A__   (    self       :    str,       __lowercase       :    List[Any],       __lowercase       :    int=False    ):
							if not batched:
										lowercase__       	=					self.size["shortest_edge"]
										lowercase__       	=					image_inputs[0]
										if isinstance(a_,       Image.Image    ):
													lowercase__	,							lowercase__       	=					image.size
										else:
													lowercase__	,							lowercase__       	=					image.shape[1], image.shape[2]
										lowercase__       	=					size / min(a_,       a_    )
										if h < w:
													lowercase__	,							lowercase__       	=					size, scale * w
										else:
													lowercase__	,							lowercase__       	=					scale * h, size

										lowercase__       	=					int((1333 / 800) * size    )
										if max(a_,       a_    ) > max_size:
													lowercase__       	=					max_size / max(a_,       a_    )
													lowercase__       	=					newh * scale
													lowercase__       	=					neww * scale

										lowercase__	,							lowercase__       	=					int(newh + 0.5    ), int(neww + 0.5    )
										lowercase__	,							lowercase__       	=					(
										    newh // self.size_divisor * self.size_divisor,
										    neww // self.size_divisor * self.size_divisor,
										)

							else:
										lowercase__       	=					[]
										for image in image_inputs:
													lowercase__	,							lowercase__       	=					self.get_expected_values([image]    )
													expected_values.append((expected_height, expected_width)    )
										lowercase__       	=					max(a_,       key=lambda __lowercase    : item[0]    )[0]
										lowercase__       	=					max(a_,       key=lambda __lowercase    : item[1]    )[1]

							return expected_height, expected_width
@require_torch
@require_vision
class 					_snake_case      (       lowercase__     ,						unittest.TestCase):
				UpperCamelCase__					:       List[str]  		=BridgeTowerImageProcessor if is_vision_available() else None




				def 				A__   (    self       :    Any    ):
							lowercase__       	=					BridgeTowerImageProcessingTester(self    )




				@property
				def 				A__   (    self       :    List[str]    ):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 				A__   (    self       :    List[Any]    ):
							lowercase__       	=					self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(a_,       "image_mean"    )    )
							self.assertTrue(hasattr(a_,       "image_std"    )    )
							self.assertTrue(hasattr(a_,       "do_normalize"    )    )
							self.assertTrue(hasattr(a_,       "do_resize"    )    )
							self.assertTrue(hasattr(a_,       "size"    )    )
							self.assertTrue(hasattr(a_,       "size_divisor"    )    )




				def 				A__   (    self       :    Any    ):
							pass




				def 				A__   (    self       :    Dict    ):
							lowercase__       	=					self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							lowercase__       	=					prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_    )
							for image in image_inputs:
										self.assertIsInstance(a_,       Image.Image    )

							# Test not batched input
							lowercase__       	=					image_processing(image_inputs[0],       return_tensors="pt"    ).pixel_values

							lowercase__	,							lowercase__       	=					self.image_processor_tester.get_expected_values(a_    )
							self.assertEqual(
							    encoded_images.shape,       (1, self.image_processor_tester.num_channels, expected_height, expected_width),       )

							# Test batched
							lowercase__       	=					image_processing(a_,       return_tensors="pt"    ).pixel_values

							lowercase__	,							lowercase__       	=					self.image_processor_tester.get_expected_values(a_,       batched=a_    )
							self.assertEqual(
							    encoded_images.shape,       (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),       )




				def 				A__   (    self       :    Any    ):
							lowercase__       	=					self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							lowercase__       	=					prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       numpify=a_    )
							for image in image_inputs:
										self.assertIsInstance(a_,       np.ndarray    )

							# Test not batched input
							lowercase__       	=					image_processing(image_inputs[0],       return_tensors="pt"    ).pixel_values

							lowercase__	,							lowercase__       	=					self.image_processor_tester.get_expected_values(a_    )
							self.assertEqual(
							    encoded_images.shape,       (1, self.image_processor_tester.num_channels, expected_height, expected_width),       )

							# Test batched
							lowercase__       	=					image_processing(a_,       return_tensors="pt"    ).pixel_values

							lowercase__	,							lowercase__       	=					self.image_processor_tester.get_expected_values(a_,       batched=a_    )
							self.assertEqual(
							    encoded_images.shape,       (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),       )




				def 				A__   (    self       :    Optional[int]    ):
							lowercase__       	=					self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							lowercase__       	=					prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       torchify=a_    )
							for image in image_inputs:
										self.assertIsInstance(a_,       torch.Tensor    )

							# Test not batched input
							lowercase__       	=					image_processing(image_inputs[0],       return_tensors="pt"    ).pixel_values

							lowercase__	,							lowercase__       	=					self.image_processor_tester.get_expected_values(a_    )
							self.assertEqual(
							    encoded_images.shape,       (1, self.image_processor_tester.num_channels, expected_height, expected_width),       )

							# Test batched
							lowercase__       	=					image_processing(a_,       return_tensors="pt"    ).pixel_values

							lowercase__	,							lowercase__       	=					self.image_processor_tester.get_expected_values(a_,       batched=a_    )
							self.assertEqual(
							    encoded_images.shape,       (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),       )







'''simple docstring'''




import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
  # encoder.embeddings are double copied in original FLAVA
  return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : Optional[int]    ):
  UpperCAmelCase    				=						{}

  for key, value in state_dict.items():
    if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
      continue

    UpperCAmelCase    				=						key.replace('heads.cmd.mim_head.cls.predictions'							, 'mmm_image_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.mlm_head.cls.predictions'							, 'mmm_text_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.itm_head.cls'							, 'itm_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.itm_head.pooler'							, 'itm_head.pooler'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.clip_head.logit_scale'							, 'flava.logit_scale'    )
    UpperCAmelCase    				=						key.replace('heads.fairseq_mlm.cls.predictions'							, 'mlm_head'    )
    UpperCAmelCase    				=						key.replace('heads.imagenet.mim_head.cls.predictions'							, 'mim_head'    )
    UpperCAmelCase    				=						key.replace('mm_text_projection'							, 'flava.text_to_mm_projection'    )
    UpperCAmelCase    				=						key.replace('mm_image_projection'							, 'flava.image_to_mm_projection'    )
    UpperCAmelCase    				=						key.replace('image_encoder.module'							, 'flava.image_model'    )
    UpperCAmelCase    				=						key.replace('text_encoder.module'							, 'flava.text_model'    )
    UpperCAmelCase    				=						key.replace('mm_encoder.module.encoder.cls_token'							, 'flava.multimodal_model.cls_token'    )
    UpperCAmelCase    				=						key.replace('mm_encoder.module'							, 'flava.multimodal_model'    )
    UpperCAmelCase    				=						key.replace('text_projection'							, 'flava.text_projection'    )
    UpperCAmelCase    				=						key.replace('image_projection'							, 'flava.image_projection'    )

    UpperCAmelCase    				=						value.float()

  for key, value in codebook_state_dict.items():
    UpperCAmelCase    				=						value

  return upgrade


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Any=None    ):
  if config_path is not None:
    UpperCAmelCase    				=						FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE    )
  else:
    UpperCAmelCase    				=						FlavaConfig()

  UpperCAmelCase    				=						FlavaForPreTraining(SCREAMING_SNAKE_CASE    ).eval()

  UpperCAmelCase    				=						convert_dalle_checkpoint(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, save_checkpoint=SCREAMING_SNAKE_CASE    )

  if os.path.exists(SCREAMING_SNAKE_CASE    ):
    UpperCAmelCase    				=						torch.load(SCREAMING_SNAKE_CASE							, map_location='cpu'    )
  else:
    UpperCAmelCase    				=						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE							, map_location='cpu'    )

  UpperCAmelCase    				=						upgrade_state_dict(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						hf_model.state_dict()
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    ) + count_parameters(SCREAMING_SNAKE_CASE    )

  assert torch.allclose(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, atol=1E-3    )

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  _a					:			str							=    argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
  parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  _a					:			str							=    parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)


