



'''simple docstring'''



import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							Tuple             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					Union[str, Any]=False       ,		A	:					Any=False					):
							SCREAMING_SNAKE_CASE       :   str         =      'backbone.' if is_semantic else ''

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      []
							for i in range(config.num_hidden_layers					):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")					)
														rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")					)
														rename_keys.append(
														    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")					)
														rename_keys.append(
														    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")					)
														rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")					)
														rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")					)
														rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")					)
														rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")					)
														rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")					)
														rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")					)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (F"""{prefix}cls_token""", '''beit.embeddings.cls_token'''),
							        (F"""{prefix}patch_embed.proj.weight""", '''beit.embeddings.patch_embeddings.projection.weight'''),
							        (F"""{prefix}patch_embed.proj.bias""", '''beit.embeddings.patch_embeddings.projection.bias'''),
							        (F"""{prefix}pos_embed""", '''beit.embeddings.position_embeddings'''),
							    ]					)

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ('''mask_token''', '''beit.embeddings.mask_token'''),
														        ('''norm.weight''', '''layernorm.weight'''),
														        ('''norm.bias''', '''layernorm.bias'''),
														    ]					)
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
														        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
														        ('''head.weight''', '''classifier.weight'''),
														        ('''head.bias''', '''classifier.bias'''),
														    ]					)

							return rename_keys
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Any       ,		A	:					Dict=False       ,		A	:					int=False					):
							for i in range(config.num_hidden_layers					):
														SCREAMING_SNAKE_CASE       :   Any         =      'backbone.' if is_semantic else ''
														# queries, keys and values
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""					)
														SCREAMING_SNAKE_CASE       :   Tuple         =      state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""					)

														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[
														    : config.hidden_size, :
														]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      q_bias
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE       :   List[str]         =      v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														SCREAMING_SNAKE_CASE       :   Dict         =      state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""					)

														SCREAMING_SNAKE_CASE       :   Dict         =      gamma_a
														SCREAMING_SNAKE_CASE       :   List[str]         =      gamma_a
def        UpperCAmelCase (    A	:					int       ,		A	:					Optional[Any]       ,		A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   Any         =      dct.pop(A					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      val
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      'http://images.cocodataset.org/val2017/000000039769.jpg'
							SCREAMING_SNAKE_CASE       :   int         =      Image.open(requests.get(A       ,		stream=A					).raw					)
							return im
@torch.no_grad()
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					Any       ,		A	:					List[Any]=False					):
							SCREAMING_SNAKE_CASE       :   int         =      False if 'rvlcdip' in checkpoint_url else True
							SCREAMING_SNAKE_CASE       :   Tuple         =      BeitConfig(use_absolute_position_embeddings=A       ,		use_mask_token=A					)

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      1024
														SCREAMING_SNAKE_CASE       :   List[str]         =      4096
														SCREAMING_SNAKE_CASE       :   List[Any]         =      24
														SCREAMING_SNAKE_CASE       :   List[str]         =      16

							# labels
							if "rvlcdip" in checkpoint_url:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      16
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      'huggingface/label-files'
														SCREAMING_SNAKE_CASE       :   Tuple         =      'rvlcdip-id2label.json'
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
														SCREAMING_SNAKE_CASE       :   str         =      {int(A					): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Dict         =      {v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.hub.load_state_dict_from_url(A       ,		map_location='''cpu'''					)['model']

							SCREAMING_SNAKE_CASE       :   Tuple         =      create_rename_keys(A       ,		has_lm_head=A					)
							for src, dest in rename_keys:
														rename_key(A       ,		A       ,		A					)
							read_in_q_k_v(A       ,		A       ,		has_lm_head=A					)

							# load HuggingFace model
							SCREAMING_SNAKE_CASE       :   List[Any]         =      BeitForMaskedImageModeling(A					) if has_lm_head else BeitForImageClassification(A					)
							model.eval()
							model.load_state_dict(A					)

							# Check outputs on an image
							SCREAMING_SNAKE_CASE       :   List[str]         =      BeitImageProcessor(
							    size=config.image_size       ,		resample=PILImageResampling.BILINEAR       ,		do_center_crop=A					)
							SCREAMING_SNAKE_CASE       :   Dict         =      prepare_img()

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      image_processor(images=A       ,		return_tensors='''pt'''					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      encoding['pixel_values']

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(A					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      outputs.logits

							# verify logits
							SCREAMING_SNAKE_CASE       :   Dict         =      [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8192]
							assert logits.shape == torch.Size(A					), "Shape of logits not as expected"

							Path(A					).mkdir(exist_ok=A					)
							print(F"""Saving model to {pytorch_dump_folder_path}"""					)
							model.save_pretrained(A					)
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
							image_processor.save_pretrained(A					)

							if push_to_hub:
														if has_lm_head:
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      'dit-base' if 'base' in checkpoint_url else 'dit-large'
														else:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
														image_processor.push_to_hub(
														    repo_path_or_name=Path(A       ,		A					)       ,		organization='''nielsr'''       ,		commit_message='''Add image processor'''       ,		use_temp_dir=A       ,		)
														model.push_to_hub(
														    repo_path_or_name=Path(A       ,		A					)       ,		organization='''nielsr'''       ,		commit_message='''Add model'''       ,		use_temp_dir=A       ,		)


if __name__ == "__main__":
							lowerCAmelCase_  :							str             =    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_url',
							    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
							    type=str,
							    help='URL to the original PyTorch checkpoint (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							)
							lowerCAmelCase_  :							List[str]             =    parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""
import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFEfficientFormerForImageClassification,
			    TFEfficientFormerForImageClassificationWithTeacher,
			    TFEfficientFormerModel,
			)
			from transformers.models.efficientformer.modeling_tf_efficientformer import (
			    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


if is_vision_available():
			from PIL import Image

			from transformers import EfficientFormerImageProcessor


class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 1_3   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 3   ,				lowerCAmelCase_ = 3   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = 1_2_8   ,				lowerCAmelCase_=[1_6, 3_2, 6_4, 1_2_8]   ,				lowerCAmelCase_ = 7   ,				lowerCAmelCase_ = 4   ,				lowerCAmelCase_ = 3_7   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = 1_2_8   ,				lowerCAmelCase_ = [2, 2, 2, 2]   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    parent
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    image_size
										_SCREAMING_SNAKE_CASE   :		int       		=    patch_size
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    is_training
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		Dict       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Any       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		str       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    type_sequence_label_size
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		int       		=    encoder_stride
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_attention_outputs
										_SCREAMING_SNAKE_CASE   :		Any       		=    embed_dim
										_SCREAMING_SNAKE_CASE   :		str       		=    embed_dim + 1
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    resolution
										_SCREAMING_SNAKE_CASE   :		int       		=    depths
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_sizes
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    dim
										_SCREAMING_SNAKE_CASE   :		Any       		=    mlp_expansion_ratio







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ids_tensor([self.batch_size]   ,				self.type_sequence_label_size							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.get_config()

										return config, pixel_values, labels







						def 			A      (    self							)		->							List[str]:
										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=lowerCAmelCase_   ,				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 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    TFEfficientFormerModel(config=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.type_sequence_label_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    TFEfficientFormerForImageClassification(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(lowerCAmelCase_   ,				labels=lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)							)

										# test greyscale images
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    1
										_SCREAMING_SNAKE_CASE   :		Any       		=    TFEfficientFormerForImageClassification(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(lowerCAmelCase_   ,				labels=lowerCAmelCase_							)
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)							)







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    config_and_inputs
										_SCREAMING_SNAKE_CASE   :		Dict       		=    {'pixel_values': pixel_values}
										return config, inputs_dict







@require_tf
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(
						    {
						        'feature-extraction': TFEfficientFormerModel,
						        'image-classification': (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						SCREAMING_SNAKE_CASE_:					List[Any] 					=				False

						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				False
						SCREAMING_SNAKE_CASE_:					List[str] 					=				False
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				False
						SCREAMING_SNAKE_CASE_:					str 					=				False







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    TFEfficientFormerModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ConfigTester(
										    self   ,				config_class=lowerCAmelCase_   ,				has_text_modality=lowerCAmelCase_   ,				hidden_size=3_7							)







						def 			A      (    self							)		->							Tuple:
										self.config_tester.run_common_tests()







						@unittest.skip(reason='EfficientFormer does not use inputs_embeds'							)
						def 			A      (    self							)		->							Dict:
										pass







						@unittest.skip(reason='EfficientFormer does not support input and output embeddings'							)
						def 			A      (    self							)		->							Optional[Any]:
										pass







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model_class(lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    inspect.signature(model.call							)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [*signature.parameters.keys()]

														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ['pixel_values']
														self.assertListEqual(arg_names[:1]   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Union[str, Any]:
										def check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    model_class(lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(**self._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_							)   ,				training=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    getattr(
														    self.model_tester   ,				'expected_num_hidden_layers'   ,				self.model_tester.num_hidden_layers + 1							)
														self.assertEqual(len(lowerCAmelCase_							)   ,				lowerCAmelCase_							)

														if hasattr(self.model_tester   ,				'encoder_seq_length'							):
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.encoder_seq_length
																		if hasattr(self.model_tester   ,				'chunk_length'							) and self.model_tester.chunk_length > 1:
																						_SCREAMING_SNAKE_CASE   :		Tuple       		=    seq_length * self.model_tester.chunk_length
														else:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    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:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    outputs.decoder_hidden_states

																		self.asseretIsInstance(lowerCAmelCase_   ,				(list, tuple)							)
																		self.assertEqual(len(lowerCAmelCase_							)   ,				lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    getattr(self.model_tester   ,				'seq_length'   ,				lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Tuple       		=    getattr(self.model_tester   ,				'decoder_seq_length'   ,				lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		str       		=    True
														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_SCREAMING_SNAKE_CASE   :		Any       		=    True

														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=False							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    super()._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_   ,				return_labels=lowerCAmelCase_							)

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

										return inputs_dict







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_							)







						@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'							)
						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_							)







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)







						@slow
						def 			A      (    self							)		->							List[str]:
										for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE   :		str       		=    TFEfficientFormerModel.from_pretrained(lowerCAmelCase_							)
														self.assertIsNotNone(lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.model_tester.prepare_config_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    True

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    getattr(self.model_tester   ,				'seq_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    getattr(self.model_tester   ,				'encoder_seq_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    getattr(self.model_tester   ,				'key_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    getattr(self.model_tester   ,				'chunk_length'   ,				lowerCAmelCase_							)

										if chunk_length is not None and hasattr(self.model_tester   ,				'num_hashes'							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    encoder_seq_length * self.model_tester.num_hashes

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		str       		=    True
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    False
														_SCREAMING_SNAKE_CASE   :		Dict       		=    True
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model_class(lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    model(**self._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_							)   ,				training=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Any       		=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
														self.assertEqual(len(lowerCAmelCase_							)   ,				self.model_tester.num_attention_outputs							)

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    True
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    model_class(lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    model(**self._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_							)   ,				training=lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
														self.assertEqual(len(lowerCAmelCase_							)   ,				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      (    self							)		->							Any:
										# We use a simplified version of this test for EfficientFormer because it requires training=False
										# and Keras refuses to let us force that during functional construction
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														# Prepare our model
														_SCREAMING_SNAKE_CASE   :		int       		=    model_class(lowerCAmelCase_							)
														# These are maximally general inputs for the model, with multiple None dimensions
														# Hopefully this will catch any conditionals that fail for flexible shapes
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
														    key: tf.keras.Input(shape=val.shape[1:]   ,				dtype=val.dtype   ,				name=lowerCAmelCase_							)
														    for key, val in model.input_signature.items()
														    if key in model.dummy_inputs
														}
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(lowerCAmelCase_							)
														self.assertTrue(outputs_dict is not None							)







def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				return image






@require_tf
@require_vision
class _lowerCAmelCase						(    unittest.TestCase       ):







						@cached_property
						def 			A      (    self							)		->							str:
										return (
										    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'							)
										    if is_vision_available()
										    else None
										)







						@slow
						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.default_image_processor
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		int       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='tf'							)
										# forward pass
										_SCREAMING_SNAKE_CASE   :		Dict       		=    model(**lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										# verify the logits
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.TensorShape((1, 1_0_0_0)							)
										self.assertEqual(outputs.logits.shape   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.constant([-0.0_555, 0.4_825, -0.0_852]							)
										self.assertTrue(np.allclose(outputs.logits[0, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)







						@slow
						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
										    'snap-research/efficientformer-l1-300'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.default_image_processor
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='tf'							)
										# forward pass
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(**lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										# verify the logits
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.TensorShape((1, 1_0_0_0)							)
										self.assertEqual(outputs.logits.shape   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.constant([-0.1_312, 0.4_353, -1.0_499]							)
										self.assertTrue(np.allclose(outputs.logits[0, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)

