

"""simple docstring"""


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase=1		)      ->					Tuple:

 '''simple docstring'''






 if n_shave_prefix_segments >= 0:
  return ".".join(path.split("."		)[n_shave_prefix_segments:]		)
 else:
  return ".".join(path.split("."		)[:n_shave_prefix_segments]		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase=0		)      ->					Optional[int]:

 '''simple docstring'''






 snake_case__      							=			[]
 for old_item in old_list:
  snake_case__      							=			old_item
  snake_case__      							=			new_item.replace("block."		,       "resnets."		)
  snake_case__      							=			new_item.replace("conv_shorcut"		,       "conv1"		)
  snake_case__      							=			new_item.replace("in_shortcut"		,       "conv_shortcut"		)
  snake_case__      							=			new_item.replace("temb_proj"		,       "time_emb_proj"		)

  snake_case__      							=			shave_segments(_UpperCAmelCase		,       n_shave_prefix_segments=_UpperCAmelCase		)

  mapping.append({"old": old_item, "new": new_item}		)

 return mapping





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase=0		,       _UpperCAmelCase=False		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			[]
 for old_item in old_list:
  snake_case__      							=			old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   snake_case__      							=			new_item.replace("attn"		,       "attentions"		)
  snake_case__      							=			new_item.replace(".k."		,       ".key."		)
  snake_case__      							=			new_item.replace(".v."		,       ".value."		)
  snake_case__      							=			new_item.replace(".q."		,       ".query."		)

  snake_case__      							=			new_item.replace("proj_out"		,       "proj_attn"		)
  snake_case__      							=			new_item.replace("norm"		,       "group_norm"		)

  snake_case__      							=			shave_segments(_UpperCAmelCase		,       n_shave_prefix_segments=_UpperCAmelCase		)
  mapping.append({"old": old_item, "new": new_item}		)

 return mapping





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase=None		,       _UpperCAmelCase=None		,       _UpperCAmelCase=None		)      ->					Union[str, Any]:

 '''simple docstring'''






 assert isinstance(_UpperCAmelCase		,       _UpperCAmelCase		), "Paths should be a list of dicts containing 'old' and 'new' keys."

 if attention_paths_to_split is not None:
  if config is None:
   raise ValueError("Please specify the config if setting \'attention_paths_to_split\' to \'True\'."		)

  for path, path_map in attention_paths_to_split.items():
   snake_case__      							=			old_checkpoint[path]
   snake_case__      							=			old_tensor.shape[0] // 3

   snake_case__      							=			(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

   snake_case__      							=			old_tensor.shape[0] // config.get("num_head_channels"		,       1		) // 3

   snake_case__      							=			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
   snake_case__      							=			old_tensor.split(channels // num_heads		,       dim=1		)

   snake_case__      							=			query.reshape(_UpperCAmelCase		).squeeze()
   snake_case__      							=			key.reshape(_UpperCAmelCase		).squeeze()
   snake_case__      							=			value.reshape(_UpperCAmelCase		).squeeze()

 for path in paths:
  snake_case__      							=			path['new']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  snake_case__      							=			new_path.replace("down."		,       "down_blocks."		)
  snake_case__      							=			new_path.replace("up."		,       "up_blocks."		)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    snake_case__      							=			new_path.replace(replacement["old"]		,       replacement["new"]		)

  if "attentions" in new_path:
   snake_case__      							=			old_checkpoint[path['old']].squeeze()
  else:
   snake_case__      							=			old_checkpoint[path['old']]





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			{}

 snake_case__      							=			checkpoint['temb.dense.0.weight']
 snake_case__      							=			checkpoint['temb.dense.0.bias']
 snake_case__      							=			checkpoint['temb.dense.1.weight']
 snake_case__      							=			checkpoint['temb.dense.1.bias']

 snake_case__      							=			checkpoint['norm_out.weight']
 snake_case__      							=			checkpoint['norm_out.bias']

 snake_case__      							=			checkpoint['conv_in.weight']
 snake_case__      							=			checkpoint['conv_in.bias']
 snake_case__      							=			checkpoint['conv_out.weight']
 snake_case__      							=			checkpoint['conv_out.bias']

 snake_case__      							=			len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "down" in layer}		)
 snake_case__      							=			{
     layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)
 }

 snake_case__      							=			len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "up" in layer}		)
 snake_case__      							=			{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)}

 for i in range(_UpperCAmelCase		):
  snake_case__      							=			(i - 1) // (config['layers_per_block'] + 1)

  if any("downsample" in layer for layer in down_blocks[i]		):
   snake_case__      							=			checkpoint[
       f"down.{i}.downsample.op.weight"
   ]
   snake_case__      							=			checkpoint[f"down.{i}.downsample.op.bias"]
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

  if any("block" in layer for layer in down_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       2		).split("."		)[:2]		) for layer in down_blocks[i] if "block" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
       for layer_id in range(_UpperCAmelCase		)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"]		):
     snake_case__      							=			renew_resnet_paths(blocks[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)

  if any("attn" in layer for layer in down_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       2		).split("."		)[:2]		) for layer in down_blocks[i] if "attn" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
       for layer_id in range(_UpperCAmelCase		)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"]		):
     snake_case__      							=			renew_attention_paths(attns[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       config=_UpperCAmelCase		)

 snake_case__      							=			[key for key in checkpoint if 'mid.block_1' in key]
 snake_case__      							=			[key for key in checkpoint if 'mid.block_2' in key]
 snake_case__      							=			[key for key in checkpoint if 'mid.attn_1' in key]

 # Mid new 2
 snake_case__      							=			renew_resnet_paths(_UpperCAmelCase		)
 assign_to_checkpoint(
     _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]		,       )

 snake_case__      							=			renew_resnet_paths(_UpperCAmelCase		)
 assign_to_checkpoint(
     _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]		,       )

 snake_case__      							=			renew_attention_paths(_UpperCAmelCase		,       in_mid=_UpperCAmelCase		)
 assign_to_checkpoint(
     _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]		,       )

 for i in range(_UpperCAmelCase		):
  snake_case__      							=			num_up_blocks - 1 - i

  if any("upsample" in layer for layer in up_blocks[i]		):
   snake_case__      							=			checkpoint[
       f"up.{i}.upsample.conv.weight"
   ]
   snake_case__      							=			checkpoint[f"up.{i}.upsample.conv.bias"]

  if any("block" in layer for layer in up_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       2		).split("."		)[:2]		) for layer in up_blocks[i] if "block" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"] + 1		):
     snake_case__      							=			{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
     snake_case__      							=			renew_resnet_paths(blocks[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[replace_indices]		)

  if any("attn" in layer for layer in up_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       2		).split("."		)[:2]		) for layer in up_blocks[i] if "attn" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"] + 1		):
     snake_case__      							=			{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
     snake_case__      							=			renew_attention_paths(attns[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[replace_indices]		)

 snake_case__      							=			{k.replace("mid_new_2"		,       "mid_block"		): v for k, v in new_checkpoint.items()}
 return new_checkpoint





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					Dict:

 '''simple docstring'''






 snake_case__      							=			{}

 snake_case__      							=			checkpoint['encoder.norm_out.weight']
 snake_case__      							=			checkpoint['encoder.norm_out.bias']

 snake_case__      							=			checkpoint['encoder.conv_in.weight']
 snake_case__      							=			checkpoint['encoder.conv_in.bias']
 snake_case__      							=			checkpoint['encoder.conv_out.weight']
 snake_case__      							=			checkpoint['encoder.conv_out.bias']

 snake_case__      							=			checkpoint['decoder.norm_out.weight']
 snake_case__      							=			checkpoint['decoder.norm_out.bias']

 snake_case__      							=			checkpoint['decoder.conv_in.weight']
 snake_case__      							=			checkpoint['decoder.conv_in.bias']
 snake_case__      							=			checkpoint['decoder.conv_out.weight']
 snake_case__      							=			checkpoint['decoder.conv_out.bias']

 snake_case__      							=			len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "down" in layer}		)
 snake_case__      							=			{
     layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)
 }

 snake_case__      							=			len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "up" in layer}		)
 snake_case__      							=			{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)}

 for i in range(_UpperCAmelCase		):
  snake_case__      							=			(i - 1) // (config['layers_per_block'] + 1)

  if any("downsample" in layer for layer in down_blocks[i]		):
   snake_case__      							=			checkpoint[
       f"encoder.down.{i}.downsample.conv.weight"
   ]
   snake_case__      							=			checkpoint[
       f"encoder.down.{i}.downsample.conv.bias"
   ]

  if any("block" in layer for layer in down_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       3		).split("."		)[:3]		) for layer in down_blocks[i] if "block" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
       for layer_id in range(_UpperCAmelCase		)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"]		):
     snake_case__      							=			renew_resnet_paths(blocks[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)

  if any("attn" in layer for layer in down_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       3		).split("."		)[:3]		) for layer in down_blocks[i] if "attn" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
       for layer_id in range(_UpperCAmelCase		)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"]		):
     snake_case__      							=			renew_attention_paths(attns[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       config=_UpperCAmelCase		)

 snake_case__      							=			[key for key in checkpoint if 'mid.block_1' in key]
 snake_case__      							=			[key for key in checkpoint if 'mid.block_2' in key]
 snake_case__      							=			[key for key in checkpoint if 'mid.attn_1' in key]

 # Mid new 2
 snake_case__      							=			renew_resnet_paths(_UpperCAmelCase		)
 assign_to_checkpoint(
     _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]		,       )

 snake_case__      							=			renew_resnet_paths(_UpperCAmelCase		)
 assign_to_checkpoint(
     _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]		,       )

 snake_case__      							=			renew_attention_paths(_UpperCAmelCase		,       in_mid=_UpperCAmelCase		)
 assign_to_checkpoint(
     _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]		,       )

 for i in range(_UpperCAmelCase		):
  snake_case__      							=			num_up_blocks - 1 - i

  if any("upsample" in layer for layer in up_blocks[i]		):
   snake_case__      							=			checkpoint[
       f"decoder.up.{i}.upsample.conv.weight"
   ]
   snake_case__      							=			checkpoint[
       f"decoder.up.{i}.upsample.conv.bias"
   ]

  if any("block" in layer for layer in up_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       3		).split("."		)[:3]		) for layer in up_blocks[i] if "block" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"] + 1		):
     snake_case__      							=			{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
     snake_case__      							=			renew_resnet_paths(blocks[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[replace_indices]		)

  if any("attn" in layer for layer in up_blocks[i]		):
   snake_case__      							=			len(
       {".".join(shave_segments(_UpperCAmelCase		,       3		).split("."		)[:3]		) for layer in up_blocks[i] if "attn" in layer}		)
   snake_case__      							=			{
       layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(_UpperCAmelCase		)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"] + 1		):
     snake_case__      							=			{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
     snake_case__      							=			renew_attention_paths(attns[j]		)
     assign_to_checkpoint(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       additional_replacements=[replace_indices]		)

 snake_case__      							=			{k.replace("mid_new_2"		,       "mid_block"		): v for k, v in new_checkpoint.items()}
 snake_case__      							=			checkpoint['quant_conv.weight']
 snake_case__      							=			checkpoint['quant_conv.bias']
 if "quantize.embedding.weight" in checkpoint:
  snake_case__      							=			checkpoint['quantize.embedding.weight']
 snake_case__      							=			checkpoint['post_quant_conv.weight']
 snake_case__      							=			checkpoint['post_quant_conv.bias']

 return new_checkpoint


if __name__ == "__main__":
   a__   :       List[str]   						=			argparse.ArgumentParser()

   parser.add_argument(
       """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
   )

   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help="""The config json file corresponding to the architecture.""",
   )

   parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

   a__   :       int   						=			parser.parse_args()
   a__   :       Tuple   						=			torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      a__   :       Optional[int]   						=			json.loads(f.read())

   # unet case
   a__   :       int   						=			{key.split(""".""")[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
      a__   :       Dict   						=			convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
      a__   :       int   						=			convert_ddpm_checkpoint(checkpoint, config)

   if "ddpm" in config:
      del config["ddpm"]

   if config["_class_name"] == "VQModel":
      a__   :       List[str]   						=			VQModel(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
      a__   :       Optional[int]   						=			AutoencoderKL(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   else:
      a__   :       int   						=			UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      a__   :       str   						=			DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

      a__   :       List[Any]   						=			DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)








"""simple docstring"""


import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       EfficientFormerForImageClassification,
       EfficientFormerForImageClassificationWithTeacher,
       EfficientFormerModel,
   )
   from transformers.models.efficientformer.modeling_efficientformer import (
       EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )


if is_vision_available():
   from PIL import Image

   from transformers import EfficientFormerImageProcessor



class        lowercase_			:

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = 13				,   __SCREAMING_SNAKE_CASE = 64				,   __SCREAMING_SNAKE_CASE = 2				,   __SCREAMING_SNAKE_CASE = 3				,   __SCREAMING_SNAKE_CASE = 3				,   __SCREAMING_SNAKE_CASE = True				,   __SCREAMING_SNAKE_CASE = True				,   __SCREAMING_SNAKE_CASE = 128				,   __SCREAMING_SNAKE_CASE=[16, 32, 64, 128]				,   __SCREAMING_SNAKE_CASE = 7				,   __SCREAMING_SNAKE_CASE = 4				,   __SCREAMING_SNAKE_CASE = 37				,   __SCREAMING_SNAKE_CASE = "gelu"				,   __SCREAMING_SNAKE_CASE = 0.1				,   __SCREAMING_SNAKE_CASE = 0.1				,   __SCREAMING_SNAKE_CASE = 10				,   __SCREAMING_SNAKE_CASE = 0.02				,   __SCREAMING_SNAKE_CASE = 2				,   __SCREAMING_SNAKE_CASE = 1				,   __SCREAMING_SNAKE_CASE = 128				,   __SCREAMING_SNAKE_CASE = [2, 2, 2, 2]				,   __SCREAMING_SNAKE_CASE = 2				,   __SCREAMING_SNAKE_CASE = 2				,   ):






        '''simple docstring'''

        snake_case__      							=			parent
        snake_case__      							=			batch_size
        snake_case__      							=			image_size
        snake_case__      							=			patch_size
        snake_case__      							=			num_channels
        snake_case__      							=			is_training
        snake_case__      							=			use_labels
        snake_case__      							=			hidden_size
        snake_case__      							=			num_hidden_layers
        snake_case__      							=			num_attention_heads
        snake_case__      							=			intermediate_size
        snake_case__      							=			hidden_act
        snake_case__      							=			hidden_dropout_prob
        snake_case__      							=			attention_probs_dropout_prob
        snake_case__      							=			type_sequence_label_size
        snake_case__      							=			initializer_range
        snake_case__      							=			encoder_stride
        snake_case__      							=			num_attention_outputs
        snake_case__      							=			embed_dim
        snake_case__      							=			embed_dim + 1
        snake_case__      							=			resolution
        snake_case__      							=			depths
        snake_case__      							=			hidden_sizes
        snake_case__      							=			dim
        snake_case__      							=			mlp_expansion_ratio

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

        snake_case__      							=			None
        if self.use_labels:
         snake_case__      							=			ids_tensor([self.batch_size]				,   self.type_sequence_label_size       )

        snake_case__      							=			self.get_config()
        return config, pixel_values, labels

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return EfficientFormerConfig(
            image_size=self.image_size				,   patch_size=self.patch_size				,   num_channels=self.num_channels				,   hidden_size=self.hidden_size				,   num_hidden_layers=self.num_hidden_layers				,   num_attention_heads=self.num_attention_heads				,   intermediate_size=self.intermediate_size				,   hidden_act=self.hidden_act				,   hidden_dropout_prob=self.hidden_dropout_prob				,   attention_probs_dropout_prob=self.attention_probs_dropout_prob				,   is_decoder=__SCREAMING_SNAKE_CASE				,   initializer_range=self.initializer_range				,   encoder_stride=self.encoder_stride				,   resolution=self.resolution				,   depths=self.depths				,   hidden_sizes=self.hidden_sizes				,   dim=self.dim				,   mlp_expansion_ratio=self.mlp_expansion_ratio				,   )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			EfficientFormerModel(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			model(__SCREAMING_SNAKE_CASE       )
        self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.type_sequence_label_size
        snake_case__      							=			EfficientFormerForImageClassification(__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   labels=__SCREAMING_SNAKE_CASE       )
        self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)       )

        # test greyscale images
        snake_case__      							=			1
        snake_case__      							=			EfficientFormerForImageClassification(__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()

        snake_case__      							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        snake_case__      							=			model(__SCREAMING_SNAKE_CASE       )
        self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.prepare_config_and_inputs()
        (
            (
            snake_case__
        )							,				(
            snake_case__
        )							,				(
            snake_case__
        )							,				
        )      							=			config_and_inputs
        snake_case__      							=			{"pixel_values": pixel_values}
        return config, inputs_dict



@require_torch
class        lowercase_			(       __lowerCAmelCase			,__lowerCAmelCase			,unittest.TestCase							):
       snake_case__			:							str										=       (
           (
               EfficientFormerModel,
               EfficientFormerForImageClassificationWithTeacher,
               EfficientFormerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       snake_case__			:							Optional[Any]										=       (
           {
               """feature-extraction""": EfficientFormerModel,
               """image-classification""": (
                   EfficientFormerForImageClassification,
                   EfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_torch_available()
           else {}
       )
       snake_case__			:							Tuple										=       False

       snake_case__			:							List[Any]										=       False
       snake_case__			:							Union[str, Any]										=       False
       snake_case__			:							Optional[Any]										=       False

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			EfficientFormerModelTester(self       )
        snake_case__      							=			ConfigTester(
            self				,   config_class=__SCREAMING_SNAKE_CASE				,   has_text_modality=__SCREAMING_SNAKE_CASE				,   hidden_size=37       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

       @unittest.skip(reason="EfficientFormer does not use inputs_embeds"       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        pass

       @unittest.skip(reason="EfficientFormer does not support input and output embeddings"       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        pass

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         snake_case__      							=			inspect.signature(model.forward       )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case__      							=			[*signature.parameters.keys()]

         snake_case__      							=			["pixel_values"]
         self.assertListEqual(arg_names[:1]				,   __SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        def check_hidden_states_output(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()

         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )

         snake_case__      							=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         snake_case__      							=			getattr(
             self.model_tester				,   "expected_num_hidden_layers"				,   self.model_tester.num_hidden_layers + 1       )
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   __SCREAMING_SNAKE_CASE       )

         if hasattr(self.model_tester				,   "encoder_seq_length"       ):
          snake_case__      							=			self.model_tester.encoder_seq_length
          if hasattr(self.model_tester				,   "chunk_length"       ) and self.model_tester.chunk_length > 1:
           snake_case__      							=			seq_length * self.model_tester.chunk_length
         else:
          snake_case__      							=			self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[-1].shape[-2:]       )				,   [seq_length, self.model_tester.hidden_size]				,   )

         if config.is_encoder_decoder:
          snake_case__      							=			outputs.decoder_hidden_states

          self.assertIsInstance(__SCREAMING_SNAKE_CASE				,   (list, tuple)       )
          self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   __SCREAMING_SNAKE_CASE       )
          snake_case__      							=			getattr(self.model_tester				,   "seq_length"				,   __SCREAMING_SNAKE_CASE       )
          snake_case__      							=			getattr(self.model_tester				,   "decoder_seq_length"				,   __SCREAMING_SNAKE_CASE       )

          self.assertListEqual(
              list(hidden_states[-1].shape[-2:]       )				,   [decoder_seq_length, self.model_tester.hidden_size]				,   )

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         snake_case__      							=			True
         check_hidden_states_output(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case__      							=			True

         check_hidden_states_output(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=False       ):






        '''simple docstring'''

        snake_case__      							=			super()._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   return_labels=__SCREAMING_SNAKE_CASE       )

        if return_labels:
         if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
          del inputs_dict["labels"]

        return inputs_dict

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

       @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        if not self.model_tester.is_training:
         return

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()
        snake_case__      							=			True

        for model_class in self.all_model_classes:
         # EfficientFormerForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(__SCREAMING_SNAKE_CASE       )
             or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
         ):
          continue
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.train()
         snake_case__      							=			self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   return_labels=__SCREAMING_SNAKE_CASE       )
         snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       ).loss
         loss.backward()

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        snake_case__      							=			[
            {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
            {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
            {"title": "regression", "num_labels": 1, "dtype": torch.float},
        ]

        for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(__SCREAMING_SNAKE_CASE       ),
             ]
             or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
         ):
          continue

         for problem_type in problem_types:
          with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"       ):
           snake_case__      							=			problem_type["title"]
           snake_case__      							=			problem_type["num_labels"]

           snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
           model.to(__SCREAMING_SNAKE_CASE       )
           model.train()

           snake_case__      							=			self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   return_labels=__SCREAMING_SNAKE_CASE       )

           if problem_type["num_labels"] > 1:
            snake_case__      							=			inputs["labels"].unsqueeze(1       ).repeat(1				,   problem_type["num_labels"]       )

           snake_case__      							=			inputs["labels"].to(problem_type["dtype"]       )

           # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
           # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
           # they have the same size." which is a symptom something in wrong for the regression problem.
           # See https://github.com/huggingface/transformers/issues/11780
           with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE       ) as warning_list:
            snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       ).loss
           for w in warning_list:
            if "Using a target size that is different to the input size" in str(w.message       ):
             raise ValueError(
                 F"Something is going wrong in the regression problem: intercepted {w.message}"       )

           loss.backward()

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case__      							=			EfficientFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
         self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()
        snake_case__      							=			True

        snake_case__      							=			getattr(self.model_tester				,   "seq_length"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			getattr(self.model_tester				,   "encoder_seq_length"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			getattr(self.model_tester				,   "key_length"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			getattr(self.model_tester				,   "chunk_length"				,   __SCREAMING_SNAKE_CASE       )

        if chunk_length is not None and hasattr(self.model_tester				,   "num_hashes"       ):
         snake_case__      							=			encoder_seq_length * self.model_tester.num_hashes

        for model_class in self.all_model_classes:
         snake_case__      							=			True
         snake_case__      							=			False
         snake_case__      							=			True
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()
         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )
         snake_case__      							=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   self.model_tester.num_attention_outputs       )

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         snake_case__      							=			True
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()
         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )
         snake_case__      							=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   self.model_tester.num_attention_outputs       )

         if chunk_length is not None:
          self.assertListEqual(
              list(attentions[0].shape[-4:]       )				,   [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]				,   )
         else:
          self.assertListEqual(
              list(attentions[0].shape[-3:]       )				,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]				,   )





def 							a__     (					)      ->					Dict:

 '''simple docstring'''






 snake_case__      							=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
 return image



@require_torch
@require_vision
class        lowercase_			(       unittest.TestCase							):

       @cached_property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return (
            EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"       )
            if is_vision_available()
            else None
        )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"       ).to(
            __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.default_image_processor
        snake_case__      							=			prepare_img()
        snake_case__      							=			image_processor(images=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

        # forward pass
        with torch.no_grad():
         snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       )

        # verify the logits
        snake_case__      							=			(1, 1000)
        self.assertEqual(outputs.logits.shape				,   __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			torch.tensor([-0.0555, 0.4825, -0.0852]       ).to(__SCREAMING_SNAKE_CASE       )
        self.assertTrue(torch.allclose(outputs.logits[0][:3]				,   __SCREAMING_SNAKE_CASE				,   atol=1e-4       )       )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			EfficientFormerForImageClassificationWithTeacher.from_pretrained(
            "snap-research/efficientformer-l1-300"       ).to(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.default_image_processor
        snake_case__      							=			prepare_img()
        snake_case__      							=			image_processor(images=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

        # forward pass
        with torch.no_grad():
         snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       )

        # verify the logits
        snake_case__      							=			(1, 1000)
        self.assertEqual(outputs.logits.shape				,   __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			torch.tensor([-0.1312, 0.4353, -1.0499]       ).to(__SCREAMING_SNAKE_CASE       )
        self.assertTrue(torch.allclose(outputs.logits[0][:3]				,   __SCREAMING_SNAKE_CASE				,   atol=1e-4       )       )

