



import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

	from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Union[str, Any]=3							,      lowerCamelCase	:							Optional[int]=32							,      lowerCamelCase	:							Optional[int]=3							,      lowerCamelCase	:							Any=10							,      lowerCamelCase	:							Tuple=[8, 16, 32, 64]							,      lowerCamelCase	:							Optional[Any]=[1, 1, 2, 1]							,      lowerCamelCase	:							Optional[int]=True							,      lowerCamelCase	:							Optional[Any]=True							,      lowerCamelCase	:							Optional[int]="relu"							,      lowerCamelCase	:							Union[str, Any]=3							,      lowerCamelCase	:							List[str]=None							,      lowerCamelCase	:							Optional[Any]=["stage2", "stage3", "stage4"]							,      lowerCamelCase	:							Dict=[2, 3, 4]							,      lowerCamelCase	:							int=1							,      ):






							'''simple docstring'''



							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			image_size
							__lowercase		       =			num_channels
							__lowercase		       =			embeddings_size
							__lowercase		       =			hidden_sizes
							__lowercase		       =			depths
							__lowercase		       =			is_training
							__lowercase		       =			use_labels
							__lowercase		       =			hidden_act
							__lowercase		       =			num_labels
							__lowercase		       =			scope
							__lowercase		       =			len(lowerCamelCase	)
							__lowercase		       =			out_features
							__lowercase		       =			out_indices
							__lowercase		       =			num_groups



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



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

							__lowercase		       =			None
							if self.use_labels:
								__lowercase		       =			ids_tensor([self.batch_size]							,      self.num_labels	)

							__lowercase		       =			self.get_config()

							return config, pixel_values, labels



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							return BitConfig(
							    num_channels=self.num_channels							,      embeddings_size=self.embeddings_size							,      hidden_sizes=self.hidden_sizes							,      depths=self.depths							,      hidden_act=self.hidden_act							,      num_labels=self.num_labels							,      out_features=self.out_features							,      out_indices=self.out_indices							,      num_groups=self.num_groups							,      )



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			BitModel(config=lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase	)
							self.parent.assertEqual(
							    result.last_hidden_state.shape							,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,      )



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			self.num_labels
							__lowercase		       =			BitForImageClassification(lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase							,      labels=lowerCamelCase	)
							self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)	)



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			BitBackbone(config=lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase	)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps	)							,      len(config.out_features	)	)
							self.parent.assertListEqual(list(result.feature_maps[0].shape	)							,      [self.batch_size, self.hidden_sizes[1], 4, 4]	)

							# verify channels
							self.parent.assertEqual(len(model.channels	)							,      len(config.out_features	)	)
							self.parent.assertListEqual(model.channels							,      config.hidden_sizes[1:]	)

							# verify backbone works with out_features=None
							__lowercase		       =			None
							__lowercase		       =			BitBackbone(config=lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase	)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps	)							,      1	)
							self.parent.assertListEqual(list(result.feature_maps[0].shape	)							,      [self.batch_size, self.hidden_sizes[-1], 1, 1]	)

							# verify channels
							self.parent.assertEqual(len(model.channels	)							,      1	)
							self.parent.assertListEqual(model.channels							,      [config.hidden_sizes[-1]]	)



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



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

@require_torch
class   _A   ( UpperCamelCase__  ,						UpperCamelCase__  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						_snake_case			:     int      							=      (
						    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_snake_case			:     Dict      							=      False
						_snake_case			:     Optional[Any]      							=      False
						_snake_case			:     str      							=      False
						_snake_case			:     List[Any]      							=      False
						_snake_case			:     str      							=      False



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			BitModelTester(self	)
							__lowercase		       =			ConfigTester(self							,      config_class=lowerCamelCase							,      has_text_modality=lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							return



						@unittest.skip(reason="Bit does not output attentions"	)
						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							pass



						@unittest.skip(reason="Bit does not use inputs_embeds"	)
						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							pass



						@unittest.skip(reason="Bit does not support input and output embeddings"	)
						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							pass



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase		       =			["pixel_values"]
								self.assertListEqual(arg_names[:1]							,      lowerCamelCase	)



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase	)



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowerCamelCase	)



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								__lowercase		       =			model_class(config=lowerCamelCase	)
								for name, module in model.named_modules():
									if isinstance(lowerCamelCase							,      (nn.BatchNormad, nn.GroupNorm)	):
										self.assertTrue(
										    torch.all(module.weight == 1	)							,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""							,      )
										self.assertTrue(
										    torch.all(module.bias == 0	)							,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""							,      )



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							def check_hidden_states_output(lowerCamelCase	:							str							,      lowerCamelCase	:							int							,      lowerCamelCase	:							List[str]	):
								__lowercase		       =			model_class(lowerCamelCase	)
								model.to(lowerCamelCase	)
								model.eval()

								with torch.no_grad():
									__lowercase		       =			model(**self._prepare_for_class(lowerCamelCase							,      lowerCamelCase	)	)

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

								__lowercase		       =			self.model_tester.num_stages
								self.assertEqual(len(lowerCamelCase	)							,      expected_num_stages + 1	)

								# Bit's feature maps are of shape (batch_size, num_channels, height, width)
								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]	)							,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,      )

							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()
							__lowercase		       =			["preactivation", "bottleneck"]
							for model_class in self.all_model_classes:
								for layer_type in layers_type:
									__lowercase		       =			layer_type
									__lowercase		       =			True
									check_hidden_states_output(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

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

									check_hidden_states_output(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)



						@unittest.skip(reason="Bit does not use feedforward chunking"	)
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							pass



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase	)



						@slow
						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase		       =			BitModel.from_pretrained(lowerCamelCase	)
								self.assertIsNotNone(lowerCamelCase	)



def    snake_case_				(    ):
	__lowercase		       =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
	return image

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


						'''simple docstring'''





						@cached_property
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							return (
							    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	) if is_vision_available() else None
							)



						@slow
						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(lowerCamelCase	)

							__lowercase		       =			self.default_image_processor
							__lowercase		       =			prepare_img()
							__lowercase		       =			image_processor(images=lowerCamelCase							,      return_tensors="pt"	).to(lowerCamelCase	)

							# forward pass
							with torch.no_grad():
								__lowercase		       =			model(**lowerCamelCase	)

							# verify the logits
							__lowercase		       =			torch.Size((1, 1_000)	)
							self.assertEqual(outputs.logits.shape							,      lowerCamelCase	)

							__lowercase		       =			torch.tensor([[-0.6526, -0.5263, -1.4398]]	).to(lowerCamelCase	)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      lowerCamelCase							,      atol=1e-4	)	)

@require_torch
class   _A   ( UpperCamelCase__  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      (BitBackbone,) if is_torch_available() else ()
						_snake_case			:     str      							=      BitConfig

						_snake_case			:     Optional[Any]      							=      False



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			BitModelTester(self	)








import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__					:     Optional[int]    		=   logging.get_logger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

	if "_quant" in model_name:
		raise ValueError("Quantized models are not supported."   )

	__lowercase		       =			re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"  ,	_SCREAMING_SNAKE_CASE   )
	if matches:
		__lowercase		       =			float(matches[1]   )
		__lowercase		       =			int(matches[2]   )

	# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
	# the usual 1000. The first class (index 0) is "background".
	__lowercase		       =			1_0_0_1
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ) + 1: v for k, v in idalabel.items()}
	__lowercase		       =			"background"
	__lowercase		       =			idalabel
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	return config



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_mobilenet_va_config(_SCREAMING_SNAKE_CASE   )

	# Load 🤗 model
	__lowercase		       =			MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()

	# Load weights from TensorFlow checkpoint
	load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	# Check outputs on an image, prepared by MobileNetV1ImageProcessor
	__lowercase		       =			MobileNetVaImageProcessor(
	    crop_size={"width": config.image_size, "height": config.image_size}  ,	size={"shortest_edge": config.image_size + 3_2}  ,	)
	__lowercase		       =			image_processor(images=prepare_img()  ,	return_tensors="pt"   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			outputs.logits

	assert logits.shape == (1, 1_0_0_1)

	if model_name == "mobilenet_v1_1.0_224":
		__lowercase		       =			torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   )
	elif model_name == "mobilenet_v1_0.75_192":
		__lowercase		       =			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
	else:
		__lowercase		       =			None

	if expected_logits is not None:
		assert torch.allclose(logits[0, :3]  ,	_SCREAMING_SNAKE_CASE  ,	atol=1E-4   )

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

	if push_to_hub:
		print("Pushing to the hub..."   )
		__lowercase		       =			"google/" + model_name
		image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   )
		model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""mobilenet_v1_1.0_224""",
	    type=str,
	    help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
	)
	parser.add_argument(
	    """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	snake_case__					:     Dict    		=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)



