


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__=False							,				SCREAMING_SNAKE_CASE__=False							,				SCREAMING_SNAKE_CASE__="data2vec."					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				"backbone." if is_semantic else ""

				_SCREAMING_SNAKE_CASE			:	List[str]							=				[]
				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"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")					)
								rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")					)
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")					)
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")					)
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")					)
								rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")					)
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")					)
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")					)
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")					)
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")					)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
				        (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
				        (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
				    ]					)

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
								        (
								            """rel_pos_bias.relative_position_bias_table""",
								            f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
								        ),
								        (
								            """rel_pos_bias.relative_position_index""",
								            f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
								        ),
								        ("""norm.weight""", """layernorm.weight"""),
								        ("""norm.bias""", """layernorm.bias"""),
								    ]					)
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
								        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
								        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
								        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
								    ]					)
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
								        ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
								        ("""head.weight""", """classifier.weight"""),
								        ("""head.bias""", """classifier.bias"""),
								    ]					)

				return rename_keys







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__=False							,				SCREAMING_SNAKE_CASE__=False							,				SCREAMING_SNAKE_CASE__="data2vec_vision."					):







				"""simple docstring"""





				for i in range(config.num_hidden_layers					):
								_SCREAMING_SNAKE_CASE			:	Tuple							=				"backbone." if is_semantic else ""
								# queries, keys and values
								_SCREAMING_SNAKE_CASE			:	int							=				state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""					)
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""					)
								_SCREAMING_SNAKE_CASE			:	int							=				state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""					)

								_SCREAMING_SNAKE_CASE			:	int							=				in_proj_weight[
								    : config.hidden_size, :
								]
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				q_bias
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE			:	Tuple							=				in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE			:	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			:	Union[str, Any]							=				gamma_a
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												_SCREAMING_SNAKE_CASE			:	Any							=				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""					)
												_SCREAMING_SNAKE_CASE			:	Dict							=				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""					)

												_SCREAMING_SNAKE_CASE			:	str							=				table
												_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				index







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				argparse.ArgumentParser(
				    """Convert Data2VecVision to HF for image classification and pretraining"""							,				add_help=lowercase__					)
				parser.add_argument("""--hf_checkpoint_name"""							,				type=lowercase__					)
				parser.add_argument("""--input_size"""							,				default=224							,				type=lowercase__							,				help="""images input size"""					)
				parser.add_argument("""--beit_checkpoint"""							,				default=""""""							,				help="""beit checkpoint"""					)

				return parser.parse_args()







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				def load_state_dict(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__=""							,				SCREAMING_SNAKE_CASE__="relative_position_index"					):
								_SCREAMING_SNAKE_CASE			:	List[str]							=				[]
								_SCREAMING_SNAKE_CASE			:	Any							=				[]
								_SCREAMING_SNAKE_CASE			:	Any							=				[]
								# copy state_dict so _load_from_state_dict can modify it
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				getattr(lowercase__							,				"""_metadata"""							,				lowercase__					)
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				state_dict.copy()
								if metadata is not None:
												_SCREAMING_SNAKE_CASE			:	str							=				metadata

								def load(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__=""					):
												_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				{} if metadata is None else metadata.get(prefix[:-1]							,				{}					)
												module._load_from_state_dict(
												    lowercase__							,				lowercase__							,				lowercase__							,				lowercase__							,				lowercase__							,				lowercase__							,				lowercase__					)
												for name, child in module._modules.items():
																if child is not None:
																				load(lowercase__							,				prefix + name + """."""					)

								load(lowercase__							,				prefix=lowercase__					)

								_SCREAMING_SNAKE_CASE			:	Dict							=				[]
								_SCREAMING_SNAKE_CASE			:	Dict							=				[]
								for key in missing_keys:
												_SCREAMING_SNAKE_CASE			:	int							=				True
												for ignore_key in ignore_missing.split("""|"""					):
																if ignore_key in key:
																				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				False
																				break
												if keep_flag:
																warn_missing_keys.append(lowercase__					)
												else:
																ignore_missing_keys.append(lowercase__					)

								_SCREAMING_SNAKE_CASE			:	int							=				warn_missing_keys

								if len(lowercase__					) > 0:
												print(
												    """Weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__							,				lowercase__					)					)
								if len(lowercase__					) > 0:
												print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__							,				lowercase__					)					)
								if len(lowercase__					) > 0:
												print(
												    """Ignored weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__							,				lowercase__					)					)
								if len(lowercase__					) > 0:
												print("""\n""".join(lowercase__					)					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				{
				    "pretrained": False,
				    "use_shared_rel_pos_bias": True,
				    "use_abs_pos_emb": False,
				    "init_values": 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        """num_classes""": 1000,
								        """use_mean_pooling""": True,
								        """init_scale""": 0.0_0_1,
								        """use_rel_pos_bias""": True,
								    }					)

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				create_model(
				    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""							,				**lowercase__							,				)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				model.patch_embed.patch_size
				_SCREAMING_SNAKE_CASE			:	str							=				(args.input_size // patch_size[0], args.input_size // patch_size[1])
				_SCREAMING_SNAKE_CASE			:	str							=				torch.load(args.beit_checkpoint							,				map_location="""cpu"""					)

				print(f"""Load ckpt from {args.beit_checkpoint}"""					)
				_SCREAMING_SNAKE_CASE			:	int							=				None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												_SCREAMING_SNAKE_CASE			:	Optional[int]							=				checkpoint[model_key]
												print(f"""Load state_dict by model_key = {model_key}"""					)
												break

				_SCREAMING_SNAKE_CASE			:	Tuple							=				list(checkpoint_model.keys()					)
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(lowercase__					)

								if "relative_position_bias_table" in key:
												_SCREAMING_SNAKE_CASE			:	List[Any]							=				checkpoint_model[key]
												_SCREAMING_SNAKE_CASE			:	int							=				rel_pos_bias.size()
												_SCREAMING_SNAKE_CASE			:	Optional[int]							=				model.state_dict()[key].size()
												_SCREAMING_SNAKE_CASE			:	List[str]							=				model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(lowercase__							,				lowercase__							,				prefix=""""""					)

				return model







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				get_args()

				_SCREAMING_SNAKE_CASE			:	Dict							=				"ft1k" in args.hf_checkpoint_name
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"large" in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				_SCREAMING_SNAKE_CASE			:	Any							=				DataaVecVisionConfig()
				if is_finetuned:
								_SCREAMING_SNAKE_CASE			:	Tuple							=				True
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				False
								_SCREAMING_SNAKE_CASE			:	Dict							=				True
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				1000

								_SCREAMING_SNAKE_CASE			:	List[str]							=				"huggingface/label-files"
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"imagenet-1k-id2label.json"
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				json.load(open(hf_hub_download(lowercase__							,				lowercase__							,				repo_type="""dataset"""					)							,				"""r"""					)					)
								_SCREAMING_SNAKE_CASE			:	int							=				{int(lowercase__					): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE			:	List[str]							=				idalabel
								_SCREAMING_SNAKE_CASE			:	Dict							=				{v: k for k, v in idalabel.items()}
				else:
								_SCREAMING_SNAKE_CASE			:	Dict							=				False
								_SCREAMING_SNAKE_CASE			:	Dict							=				True
								_SCREAMING_SNAKE_CASE			:	str							=				False

				if is_large:
								_SCREAMING_SNAKE_CASE			:	Tuple							=				1024
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				4096
								_SCREAMING_SNAKE_CASE			:	str							=				24
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				16

				# 2. Load Beit model
				_SCREAMING_SNAKE_CASE			:	List[str]							=				load_beit_model(lowercase__							,				lowercase__							,				lowercase__					)
				orig_model.eval()

				# 3. Forward Beit model
				_SCREAMING_SNAKE_CASE			:	List[str]							=				BeitImageProcessor(size=config.image_size							,				do_center_crop=lowercase__					)
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""					)
				_SCREAMING_SNAKE_CASE			:	str							=				image_processor(images=lowercase__							,				return_tensors="""pt"""					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				encoding["pixel_values"]

				_SCREAMING_SNAKE_CASE			:	int							=				(pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE			:	Any							=				orig_model(*lowercase__					)

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								_SCREAMING_SNAKE_CASE			:	Dict							=				DataaVecVisionForImageClassification(lowercase__					)
								hf_model.eval()
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				False
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				"data2vec_vision."
				else:
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				DataaVecVisionModel(lowercase__					)
								hf_model.eval()
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				True
								_SCREAMING_SNAKE_CASE			:	int							=				""

				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				create_rename_keys(lowercase__							,				hf_prefix=lowercase__							,				has_lm_head=lowercase__					)
				_SCREAMING_SNAKE_CASE			:	int							=				orig_model.state_dict()
				for src, dest in rename_keys:
								_SCREAMING_SNAKE_CASE			:	List[str]							=				state_dict.pop(lowercase__					)
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				val

				read_in_q_k_v(lowercase__							,				lowercase__							,				hf_prefix=lowercase__							,				has_lm_head=lowercase__					)
				_SCREAMING_SNAKE_CASE			:	Dict							=				hf_model.load_state_dict(lowercase__							,				strict=lowercase__					)
				print("""HF missing"""							,				lowercase__					)
				print("""HF unexpected_keys"""							,				lowercase__					)

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE			:	Dict							=				hf_model(lowercase__					)

				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				_SCREAMING_SNAKE_CASE			:	int							=				torch.max(torch.abs(hf_output - orig_model_output					)					).item()

				print(f"""max_absolute_diff = {max_absolute_diff}"""					)
				_SCREAMING_SNAKE_CASE			:	Any							=				torch.allclose(lowercase__							,				lowercase__							,				atol=1E-3					)
				print("""Do both models output the same tensors?"""							,				"""🔥""" if success else """💩"""					)
				if not success:
								raise Exception("""Something went wRoNg"""					)

				# 7. Save
				print(f"""Saving to {args.hf_checkpoint_name}"""					)
				hf_model.save_pretrained(args.hf_checkpoint_name					)
				image_processor.save_pretrained(args.hf_checkpoint_name					)


if __name__ == "__main__":
				main()
				# Run the following to convert checkpoints
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"








'''simple docstring'''


from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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 numpy as np
				import tensorflow as tf

				from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
				from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.config_class(**self.inputs_dict  )
											self.parent.assertTrue(hasattr(__snake_case   , """hidden_sizes"""  )  )
											self.parent.assertTrue(hasattr(__snake_case   , """neck_hidden_sizes"""  )  )
											self.parent.assertTrue(hasattr(__snake_case   , """num_attention_heads"""  )  )





class    lowercase__  :





							'''simple docstring'''


							def __init__(   self   , __snake_case   , __snake_case=13   , __snake_case=32   , __snake_case=2   , __snake_case=3   , __snake_case=640   , __snake_case=4   , __snake_case="silu"   , __snake_case=3   , __snake_case=32   , __snake_case=0.1   , __snake_case=0.1   , __snake_case=0.1   , __snake_case=0.02   , __snake_case=True   , __snake_case=True   , __snake_case=10   , __snake_case=None   , ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				parent
											_SCREAMING_SNAKE_CASE			:	Any							=				batch_size
											_SCREAMING_SNAKE_CASE			:	str							=				image_size
											_SCREAMING_SNAKE_CASE			:	str							=				patch_size
											_SCREAMING_SNAKE_CASE			:	Any							=				num_channels
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				last_hidden_size
											_SCREAMING_SNAKE_CASE			:	Any							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Any							=				hidden_act
											_SCREAMING_SNAKE_CASE			:	Any							=				conv_kernel_size
											_SCREAMING_SNAKE_CASE			:	int							=				output_stride
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				hidden_dropout_prob
											_SCREAMING_SNAKE_CASE			:	int							=				attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE			:	Any							=				classifier_dropout_prob
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				use_labels
											_SCREAMING_SNAKE_CASE			:	List[str]							=				is_training
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_labels
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				initializer_range
											_SCREAMING_SNAKE_CASE			:	List[str]							=				scope






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

											_SCREAMING_SNAKE_CASE			:	str							=				None
											_SCREAMING_SNAKE_CASE			:	int							=				None
											if self.use_labels:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				ids_tensor([self.batch_size]   , self.num_labels  )
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				ids_tensor([self.batch_size, self.image_size, self.image_size]   , self.num_labels  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				self.get_config()

											return config, pixel_values, labels, pixel_labels






							def UpperCAmelCase_     (   self  ):
											return MobileViTConfig(
											    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , num_attention_heads=self.num_attention_heads   , hidden_act=self.hidden_act   , conv_kernel_size=self.conv_kernel_size   , output_stride=self.output_stride   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , classifier_dropout_prob=self.classifier_dropout_prob   , initializer_range=self.initializer_range   , )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				TFMobileViTModel(config=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Any							=				model(__snake_case   , training=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				self.image_size // self.output_stride
											self.parent.assertEqual(
											    result.last_hidden_state.shape   , (self.batch_size, self.last_hidden_size, expected_height, expected_width)  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.num_labels
											_SCREAMING_SNAKE_CASE			:	Any							=				TFMobileViTForImageClassification(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				model(__snake_case   , labels=__snake_case   , training=__snake_case  )
											self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.num_labels
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				TFMobileViTForSemanticSegmentation(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.image_size // self.output_stride

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				model(__snake_case   , training=__snake_case  )
											self.parent.assertEqual(
											    result.logits.shape   , (self.batch_size, self.num_labels, expected_height, expected_width)  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				model(__snake_case   , labels=__snake_case   , training=__snake_case  )
											self.parent.assertEqual(
											    result.logits.shape   , (self.batch_size, self.num_labels, expected_height, expected_width)  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	int							=				config_and_inputs
											_SCREAMING_SNAKE_CASE			:	Tuple							=				{"""pixel_values""": pixel_values}
											return config, inputs_dict





@require_tf
class    lowercase__  (						_snake_case					,					_snake_case					,					unittest.TestCase ):





							'''simple docstring'''


							A_							:				List[Any]				     =  (
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							A_							:				List[str]				     =  (
							    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							A_							:				str				     =  False
							A_							:				int				     =  False
							A_							:				Union[str, Any]				     =  False
							A_							:				List[str]				     =  False
							A_							:				str				     =  False






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				TFMobileViTModelTester(self  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				TFMobileViTConfigTester(self   , config_class=__snake_case   , has_text_modality=__snake_case  )






							def UpperCAmelCase_     (   self  ):
											self.config_tester.run_common_tests()






							@unittest.skip(reason="""MobileViT does not use inputs_embeds"""  )
							def UpperCAmelCase_     (   self  ):
											pass






							@unittest.skip(reason="""MobileViT does not support input and output embeddings"""  )
							def UpperCAmelCase_     (   self  ):
											pass






							@unittest.skip(reason="""MobileViT does not output attentions"""  )
							def UpperCAmelCase_     (   self  ):
											pass






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	str							=				self.model_tester.prepare_config_and_inputs_for_common()

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

															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				["""pixel_values"""]
															self.assertListEqual(arg_names[:1]   , __snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case  )






							def UpperCAmelCase_     (   self  ):
											def check_hidden_states_output(__snake_case   , __snake_case   , __snake_case  ):
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				model_class(__snake_case  )

															_SCREAMING_SNAKE_CASE			:	List[str]							=				model(**self._prepare_for_class(__snake_case   , __snake_case  )  )

															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				outputs.hidden_states

															_SCREAMING_SNAKE_CASE			:	Dict							=				5
															self.assertEqual(len(__snake_case  )   , __snake_case  )

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															_SCREAMING_SNAKE_CASE			:	int							=				2
															for i in range(len(__snake_case  )  ):
																			self.assertListEqual(
																			    list(hidden_states[i].shape[-2:]  )   , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   , )
																			divisor *= 2

															self.assertEqual(self.model_tester.output_stride   , divisor // 2  )

											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				True
															check_hidden_states_output(__snake_case   , __snake_case   , __snake_case  )

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

															check_hidden_states_output(__snake_case   , __snake_case   , __snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case  )






							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0   , reason="""TF does not support backprop for grouped convolutions on CPU."""   , )
							def UpperCAmelCase_     (   self  ):
											super().test_dataset_conversion()






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case=2e-1   , __snake_case=2e-1  ):
											self.assertTrue(np.allclose(__snake_case   , __snake_case   , atol=__snake_case   , rtol=__snake_case  )  )






							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0   , reason="""TF does not support backprop for grouped convolutions on CPU."""   , )
							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
															if model_class.__name__ != "TFMobileViTModel":
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				model_class(__snake_case  )
																			if getattr(__snake_case   , """hf_compute_loss"""   , __snake_case  ):
																							super().test_keras_fit()






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Dict							=				self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															# set an ignore index to correctly test the masked loss used in
															# `TFMobileViTForSemanticSegmentation`.
															if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																			_SCREAMING_SNAKE_CASE			:	int							=				5

															_SCREAMING_SNAKE_CASE			:	Dict							=				model_class(__snake_case  )
															if getattr(__snake_case   , """hf_compute_loss"""   , __snake_case  ):
																			# The number of elements in the loss should be the same as the number of elements in the label
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				prepared_for_class[
																			    sorted(prepared_for_class.keys() - inputs_dict.keys()   , reverse=__snake_case  )[0]
																			]
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				added_label.shape.as_list()[:1]

																			# Test that model correctly compute the loss with kwargs
																			_SCREAMING_SNAKE_CASE			:	int							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				{"""input_ids""", """pixel_values""", """input_features"""}
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				possible_input_names.intersection(set(__snake_case  )  ).pop()
																			_SCREAMING_SNAKE_CASE			:	int							=				prepared_for_class.pop(__snake_case  )

																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				model(__snake_case   , **__snake_case  )[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

																			# Test that model correctly compute the loss when we mask some positions
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Dict							=				{"""input_ids""", """pixel_values""", """input_features"""}
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				possible_input_names.intersection(set(__snake_case  )  ).pop()
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				prepared_for_class.pop(__snake_case  )
																			if "labels" in prepared_for_class:
																							_SCREAMING_SNAKE_CASE			:	int							=				prepared_for_class["""labels"""].numpy()
																							if len(labels.shape  ) > 1 and labels.shape[1] != 1:
																											# labels[0] = -100
																											_SCREAMING_SNAKE_CASE			:	int							=				tf.convert_to_tensor(__snake_case  )
																											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				model(__snake_case   , **__snake_case  )[0]
																											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
																											self.assertTrue(not np.any(np.isnan(loss.numpy()  )  )  )

                # Test that model correctly compute the loss with a dict
																			_SCREAMING_SNAKE_CASE			:	int							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				model(__snake_case  )[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

																			# Test that model correctly compute the loss with a tuple
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )

																			# Get keys that were added with the _prepare_for_class function
																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				prepared_for_class.keys() - inputs_dict.keys()
																			_SCREAMING_SNAKE_CASE			:	Any							=				inspect.signature(model.call  ).parameters
																			_SCREAMING_SNAKE_CASE			:	Dict							=				list(signature.keys()  )

																			# Create a dictionary holding the location of the tensors in the tuple
																			_SCREAMING_SNAKE_CASE			:	str							=				{0: input_name}
																			for label_key in label_keys:
																							_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				signature_names.index(__snake_case  )
																							_SCREAMING_SNAKE_CASE			:	int							=				label_key
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				sorted(tuple_index_mapping.items()  )
																			# Initialize a list with their default values, update the values and convert to a tuple
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				[]

																			for name in signature_names:
																							if name != "kwargs":
																											list_input.append(signature[name].default  )

																			for index, value in sorted_tuple_index_mapping:
																							_SCREAMING_SNAKE_CASE			:	List[str]							=				prepared_for_class[value]

																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tuple(__snake_case  )

																			# Send to model
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				model(tuple_input[:-1]  )[0]

																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )






							@slow
							def UpperCAmelCase_     (   self  ):
											for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				TFMobileViTModel.from_pretrained(__snake_case  )
															self.assertIsNotNone(__snake_case  )







def       snake_case_       (  ):







				"""simple docstring"""





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







@require_tf
class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				prepare_img()
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				image_processor(images=__snake_case   , return_tensors="""tf"""  )

											# forward pass
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				model(**__snake_case   , training=__snake_case  )

											# verify the logits
											_SCREAMING_SNAKE_CASE			:	int							=				tf.TensorShape((1, 1000)  )
											self.assertEqual(outputs.logits.shape   , __snake_case  )

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tf.constant([-1.9364, -1.2327, -0.4653]  )

											tf.debugging.assert_near(outputs.logits[0, :3]   , __snake_case   , atol=1e-4   , rtol=1e-04  )






							@slow
							def UpperCAmelCase_     (   self  ):
											# `from_pt` will be removed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )

											_SCREAMING_SNAKE_CASE			:	int							=				MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				prepare_img()
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				image_processor(images=__snake_case   , return_tensors="""tf"""  )

											# forward pass
											_SCREAMING_SNAKE_CASE			:	Any							=				model(inputs.pixel_values   , training=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				outputs.logits

											# verify the logits
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tf.TensorShape((1, 21, 32, 32)  )
											self.assertEqual(logits.shape   , __snake_case  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tf.constant(
											    [
											        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
											        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
											        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
											    ]  )

											tf.debugging.assert_near(logits[0, :3, :3, :3]   , __snake_case   , rtol=1e-4   , atol=1e-4  )




