




'''simple docstring'''


import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_					=   logging.get_logger(__name__)


def     SCREAMING_SNAKE_CASE							(   a_						:				int      ):
  __a								=       SwinConfig.from_pretrained(
      'microsoft/swin-tiny-patch4-window7-224'					,       out_features=['stage1', 'stage2', 'stage3', 'stage4']      )
  __a								=       MaskFormerConfig(backbone_config=a_      )

  __a								=       'huggingface/label-files'
  if "ade20k-full" in model_name:
    # this should be ok
    __a								=       847
    __a								=       'maskformer-ade20k-full-id2label.json'
  elif "ade" in model_name:
    # this should be ok
    __a								=       150
    __a								=       'ade20k-id2label.json'
  elif "coco-stuff" in model_name:
    # this should be ok
    __a								=       171
    __a								=       'maskformer-coco-stuff-id2label.json'
  elif "coco" in model_name:
    # TODO
    __a								=       133
    __a								=       'coco-panoptic-id2label.json'
  elif "cityscapes" in model_name:
    # this should be ok
    __a								=       19
    __a								=       'cityscapes-id2label.json'
  elif "vistas" in model_name:
    # this should be ok
    __a								=       65
    __a								=       'mapillary-vistas-id2label.json'

  __a								=       json.load(open(hf_hub_download(a_					,       a_					,       repo_type='dataset'      )					,       'r'      )      )
  __a								=       {int(a_      ): v for k, v in idalabel.items()}

  return config


def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple      ):
  __a								=       []
  # stem
  # fmt: off
  rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')      )
  rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')      )
  rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')      )
  rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')      )
  # stages
  for i in range(len(config.backbone_config.depths      )      ):
    for j in range(config.backbone_config.depths[i]      ):
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")      )
      rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")      )

    if i < 3:
      rename_keys.append((f"backbone.layers.{i}.downsample.reduction.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")      )
      rename_keys.append((f"backbone.layers.{i}.downsample.norm.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")      )
      rename_keys.append((f"backbone.layers.{i}.downsample.norm.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")      )
    rename_keys.append((f"backbone.norm{i}.weight", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")      )
    rename_keys.append((f"backbone.norm{i}.bias", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")      )

  # FPN
  rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')      )
  rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')      )
  rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')      )
  for source_index, target_index in zip(range(3					,       0					,       -1      )					,       range(0					,       3      )      ):
    rename_keys.append((f"sem_seg_head.adapter_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")      )
    rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")      )
    rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")      )
    rename_keys.append((f"sem_seg_head.layer_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")      )
    rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")      )
    rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")      )
  rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')      )
  rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')      )

  # Transformer decoder
  for idx in range(config.decoder_config.decoder_layers      ):
    # self-attention out projection
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")      )
    # cross-attention out projection
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")      )
    # MLP 1
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", f"model.transformer_module.decoder.layers.{idx}.fc1.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", f"model.transformer_module.decoder.layers.{idx}.fc1.bias")      )
    # MLP 2
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", f"model.transformer_module.decoder.layers.{idx}.fc2.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", f"model.transformer_module.decoder.layers.{idx}.fc2.bias")      )
    # layernorm 1 (self-attention layernorm)
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")      )
    # layernorm 2 (cross-attention layernorm)
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")      )
    # layernorm 3 (final layernorm)
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")      )

  rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')      )
  rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')      )

  # heads on top
  rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')      )

  rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')      )
  rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')      )

  rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')      )
  rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')      )

  for i in range(3      ):
    rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.weight", f"mask_embedder.{i}.0.weight")      )
    rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.bias", f"mask_embedder.{i}.0.bias")      )
  # fmt: on

  return rename_keys


def     SCREAMING_SNAKE_CASE							(   a_						:				int					,       a_						:				Dict					,       a_						:				Union[str, Any]      ):
  __a								=       dct.pop(a_      )
  __a								=       val


def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Optional[int]      ):
  __a								=       [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
  for i in range(len(backbone_config.depths      )      ):
    __a								=       num_features[i]
    for j in range(backbone_config.depths[i]      ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      __a								=       state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"      )
      __a								=       state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"      )
      # next, add query, keys and values (in that order) to the state dict
      __a								=       in_proj_weight[:dim, :]
      __a								=       in_proj_bias[: dim]
      __a								=       in_proj_weight[
          dim : dim * 2, :
      ]
      __a								=       in_proj_bias[
          dim : dim * 2
      ]
      __a								=       in_proj_weight[
          -dim :, :
      ]
      __a								=       in_proj_bias[-dim :]
      # fmt: on


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				str      ):
  __a								=       config.decoder_config.hidden_size
  for idx in range(config.decoder_config.decoder_layers      ):
    # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
    __a								=       state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"      )
    __a								=       state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"      )
    # next, add query, keys and values (in that order) to the state dict
    __a								=       in_proj_weight[: hidden_size, :]
    __a								=       in_proj_bias[:config.hidden_size]
    __a								=       in_proj_weight[hidden_size : hidden_size * 2, :]
    __a								=       in_proj_bias[hidden_size : hidden_size * 2]
    __a								=       in_proj_weight[-hidden_size :, :]
    __a								=       in_proj_bias[-hidden_size :]
    # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
    __a								=       state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"      )
    __a								=       state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"      )
    # next, add query, keys and values (in that order) to the state dict
    __a								=       in_proj_weight[: hidden_size, :]
    __a								=       in_proj_bias[:config.hidden_size]
    __a								=       in_proj_weight[hidden_size : hidden_size * 2, :]
    __a								=       in_proj_bias[hidden_size : hidden_size * 2]
    __a								=       in_proj_weight[-hidden_size :, :]
    __a								=       in_proj_bias[-hidden_size :]
  # fmt: on


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       'http://images.cocodataset.org/val2017/000000039769.jpg'
  __a								=       Image.open(requests.get(a_					,       stream=a_      ).raw      )
  return im


@torch.no_grad()
def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				List[str]					,       a_						:				Tuple					,       a_						:				Optional[int] = False      ):
  __a								=       get_maskformer_config(a_      )

  # load original state_dict
  with open(a_					,       'rb'      ) as f:
    __a								=       pickle.load(a_      )
  __a								=       data['model']

  # for name, param in state_dict.items():
  #     print(name, param.shape)

  # rename keys
  __a								=       create_rename_keys(a_      )
  for src, dest in rename_keys:
    rename_key(a_					,       a_					,       a_      )
  read_in_swin_q_k_v(a_					,       config.backbone_config      )
  read_in_decoder_q_k_v(a_					,       a_      )

  # update to torch tensors
  for key, value in state_dict.items():
    __a								=       torch.from_numpy(a_      )

  # load 🤗 model
  __a								=       MaskFormerForInstanceSegmentation(a_      )
  model.eval()

  for name, param in model.named_parameters():
    print(a_					,       param.shape      )

  __a						,							__a								=       model.load_state_dict(a_					,       strict=a_      )
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(a_      ) == 0, f"Unexpected keys: {unexpected_keys}"

  # verify results
  __a								=       prepare_img()
  if "vistas" in model_name:
    __a								=       65
  elif "cityscapes" in model_name:
    __a								=       6_5535
  else:
    __a								=       255
  __a								=       True if 'ade' in model_name else False
  __a								=       MaskFormerImageProcessor(ignore_index=a_					,       reduce_labels=a_      )

  __a								=       image_processor(a_					,       return_tensors='pt'      )

  __a								=       model(**a_      )

  print('Logits:'					,       outputs.class_queries_logits[0, :3, :3]      )

  if model_name == "maskformer-swin-tiny-ade":
    __a								=       torch.tensor(
        [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]      )
  assert torch.allclose(outputs.class_queries_logits[0, :3, :3]					,       a_					,       atol=1e-4      )
  print('Looks ok!'      )

  if pytorch_dump_folder_path is not None:
    print(f"Saving model and image processor to {pytorch_dump_folder_path}"      )
    Path(a_      ).mkdir(exist_ok=a_      )
    model.save_pretrained(a_      )
    image_processor.save_pretrained(a_      )

  if push_to_hub:
    print('Pushing model and image processor to the hub...'      )
    model.push_to_hub(f"nielsr/{model_name}"      )
    image_processor.push_to_hub(f"nielsr/{model_name}"      )


if __name__ == "__main__":
  UpperCAmelCase_					=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="maskformer-swin-tiny-ade",
      type=str,
      help=("Name of the MaskFormer model you\'d like to convert",),
  )
  parser.add_argument(
      "--checkpoint_path",
      default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
      type=str,
      help="Path to the original state dict (.pth file).",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  UpperCAmelCase_					=   parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )










import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
    from transformers import (
        FlaxBertModel,
        FlaxCLIPVisionModel,
        FlaxVisionTextDualEncoderModel,
        FlaxViTModel,
        VisionTextDualEncoderConfig,
        VisionTextDualEncoderProcessor,
    )
    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )


if is_torch_available():
    import torch

    from transformers import VisionTextDualEncoderModel

if is_vision_available():
    from PIL import Image






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if isinstance(A				,							collections.abc.Iterable     ):
      return x
   return (x, x)

@require_flax
class     snake_case_		:






 '''simple docstring'''





 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Dict:
    pass

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    pass

 def  __UpperCAmelCase						(  self		)	->       str:
    pass

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=np.abs((a - b)		).max()
    self.assertLessEqual(A_,		A_,		f"""Difference between torch and flax is {diff} (>= {tol})."""		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[str]:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)

    self.assertEqual(output["text_embeds"].shape,		(input_ids.shape[0], config.projection_dim)		)
    self.assertEqual(output["image_embeds"].shape,		(pixel_values.shape[0], config.projection_dim)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)

    self.assertEqual(output["text_embeds"].shape,		(input_ids.shape[0], model.config.projection_dim)		)
    self.assertEqual(output["image_embeds"].shape,		(pixel_values.shape[0], model.config.projection_dim)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       Any:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)
    UpperCAmelCase__						=output[0]

    with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_		)

       UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)
       UpperCAmelCase__						=after_output[0]
       UpperCAmelCase__						=np.amax(np.abs(out_a - out_a		)		)
       self.assertLessEqual(A_,		1E-3		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(
        input_ids=A_,		pixel_values=A_,		attention_mask=A_,		output_attentions=A_		)

    UpperCAmelCase__						=output.vision_model_output.attentions
    self.assertEqual(len(A_		),		vision_config.num_hidden_layers		)

    # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
    UpperCAmelCase__						=to_atuple(vision_model.config.image_size		)
    UpperCAmelCase__						=to_atuple(vision_model.config.patch_size		)
    UpperCAmelCase__						=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    UpperCAmelCase__						=num_patches + 1
    self.assertEqual(vision_attentions[0].shape[-3:],		(vision_config.num_attention_heads, seq_len, seq_len)		)

    UpperCAmelCase__						=output.text_model_output.attentions
    self.assertEqual(len(A_		),		text_config.num_hidden_layers		)

    self.assertEqual(
        text_attentions[0].shape[-3:],		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Union[str, Any]:
    pt_model.to(A_		)
    pt_model.eval()

    # prepare inputs
    UpperCAmelCase__						=inputs_dict
    UpperCAmelCase__						={k: torch.tensor(v.tolist()		) for k, v in flax_inputs.items()}

    with torch.no_grad():
       UpperCAmelCase__						=pt_model(**A_		).to_tuple()

    UpperCAmelCase__						=fx_model(**A_		).to_tuple()
    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output, pt_output in zip(fx_outputs[:4],		pt_outputs[:4]		):
       self.assert_almost_equals(A_,		pt_output.numpy(),		4E-2		)

    # PT -> Flax
    with tempfile.TemporaryDirectory() as tmpdirname:
       pt_model.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_,		from_pt=A_		)

    UpperCAmelCase__						=fx_model_loaded(**A_		).to_tuple()
    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4],		pt_outputs[:4]		):
       self.assert_almost_equals(A_,		pt_output.numpy(),		4E-2		)

    # Flax -> PT
    with tempfile.TemporaryDirectory() as tmpdirname:
       fx_model.save_pretrained(A_		)
       UpperCAmelCase__						=VisionTextDualEncoderModel.from_pretrained(A_,		from_flax=A_		)

    pt_model_loaded.to(A_		)
    pt_model_loaded.eval()

    with torch.no_grad():
       UpperCAmelCase__						=pt_model_loaded(**A_		).to_tuple()

    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output, pt_output_loaded in zip(fx_outputs[:4],		pt_outputs_loaded[:4]		):
       self.assert_almost_equals(A_,		pt_output_loaded.numpy(),		4E-2		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Tuple:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=VisionTextDualEncoderModel(A_		)
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=convert_pytorch_state_dict_to_flax(pt_model.state_dict(),		A_		)
    UpperCAmelCase__						=fx_state

    self.check_pt_flax_equivalence(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Dict:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=VisionTextDualEncoderModel(A_		)
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=load_flax_weights_in_pytorch_model(A_,		fx_model.params		)

    self.check_pt_flax_equivalence(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_model_from_pretrained_configs(**A_		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_from_pretrained(**A_		)

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_save_load(**A_		)

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_vision_text_output_attention(**A_		)

 @is_pt_flax_cross_test
 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    UpperCAmelCase__						=config_inputs_dict.pop("vision_config"		)
    UpperCAmelCase__						=config_inputs_dict.pop("text_config"		)

    UpperCAmelCase__						=config_inputs_dict

    self.check_equivalence_pt_to_flax(A_,		A_,		A_		)
    self.check_equivalence_flax_to_pt(A_,		A_,		A_		)






 @slow
 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_pretrained_model_and_inputs()

    UpperCAmelCase__						=model_a(**A_		)
    UpperCAmelCase__						=outputs[0]

    with tempfile.TemporaryDirectory() as tmp_dirname:
       model_a.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_		)

       UpperCAmelCase__						=model_a(**A_		)
       UpperCAmelCase__						=after_outputs[0]
       UpperCAmelCase__						=np.amax(np.abs(out_a - out_a		)		)
       self.assertLessEqual(A_,		1E-5		)

@require_flax
class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
        "hf-internal-testing/tiny-random-vit",		"hf-internal-testing/tiny-bert",		vision_from_pt=A_,		text_from_pt=A_,		)
    UpperCAmelCase__						=13
    UpperCAmelCase__						=floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]		)
    UpperCAmelCase__						=ids_tensor([batch_size, 4],		model.config.text_config.vocab_size		)
    UpperCAmelCase__						=random_attention_mask([batch_size, 4]		)
    UpperCAmelCase__						={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

    return model, inputs

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=FlaxViTModel(A_		)
    UpperCAmelCase__						=FlaxBertModel(A_		)
    return vision_model, text_model






 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=FlaxViTModelTester(self		)
    UpperCAmelCase__						=FlaxBertModelTester(self		)
    UpperCAmelCase__						=vit_model_tester.prepare_config_and_inputs()
    UpperCAmelCase__						=bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase__			,							UpperCAmelCase__						=vision_config_and_inputs

    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=text_config_and_inputs

    # make sure that cross attention layers are added
    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": attention_mask,
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
    }

@require_torch
class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
        "hf-internal-testing/tiny-random-clip",		"hf-internal-testing/tiny-bert",		vision_from_pt=A_,		text_from_pt=A_,		)
    UpperCAmelCase__						=13
    UpperCAmelCase__						=floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]		)
    UpperCAmelCase__						=ids_tensor([batch_size, 4],		model.config.text_config.vocab_size		)
    UpperCAmelCase__						=random_attention_mask([batch_size, 4]		)
    UpperCAmelCase__						={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

    return model, inputs

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Tuple:
    UpperCAmelCase__						=FlaxCLIPVisionModel(A_		)
    UpperCAmelCase__						=FlaxBertModel(A_		)
    return vision_model, text_model






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=FlaxCLIPVisionModelTester(self		)
    UpperCAmelCase__						=FlaxBertModelTester(self		)
    UpperCAmelCase__						=clip_model_tester.prepare_config_and_inputs()
    UpperCAmelCase__						=bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase__			,							UpperCAmelCase__						=vision_config_and_inputs

    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=text_config_and_inputs

    # make sure that cross attention layers are added
    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": attention_mask,
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
    }

@require_flax
@require_vision
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 @slow
 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian",		logit_scale_init_value=1.0		)
    UpperCAmelCase__						=VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"		)

    UpperCAmelCase__						=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
    UpperCAmelCase__						=processor(
        text=["una foto di un gatto", "una foto di un cane"],		images=A_,		padding=A_,		return_tensors="np"		)

    UpperCAmelCase__						=model(**A_		)

    # verify the logits
    self.assertEqual(outputs.logits_per_image.shape,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
    self.assertEqual(
        outputs.logits_per_text.shape,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),		)

    UpperCAmelCase__						=np.array([[1.2_28_47_27, 0.3_10_41_22]]		)

    self.assertTrue(np.allclose(outputs.logits_per_image,		A_,		atol=1E-3		)		)



