



import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
							    FlaxRobertaPreLayerNormForCausalLM,
							    FlaxRobertaPreLayerNormForMaskedLM,
							    FlaxRobertaPreLayerNormForMultipleChoice,
							    FlaxRobertaPreLayerNormForQuestionAnswering,
							    FlaxRobertaPreLayerNormForSequenceClassification,
							    FlaxRobertaPreLayerNormForTokenClassification,
							    FlaxRobertaPreLayerNormModel,
							)
class 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						Tuple,					__lowercase					:						Any,					__lowercase					:						List[Any]=13,					__lowercase					:						Union[str, Any]=7,					__lowercase					:						Tuple=True,					__lowercase					:						int=True,					__lowercase					:						List[str]=True,					__lowercase					:						Any=True,					__lowercase					:						Optional[Any]=99,					__lowercase					:						List[Any]=32,					__lowercase					:						Tuple=5,					__lowercase					:						Optional[int]=4,					__lowercase					:						Dict=37,					__lowercase					:						str="gelu",					__lowercase					:						List[str]=0.1,					__lowercase					:						List[str]=0.1,					__lowercase					:						Tuple=512,					__lowercase					:						Tuple=16,					__lowercase					:						List[str]=2,					__lowercase					:						int=0.02,					__lowercase					:						List[Any]=4,					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				seq_length
							lowercase__					  =				is_training
							lowercase__					  =				use_attention_mask
							lowercase__					  =				use_token_type_ids
							lowercase__					  =				use_labels
							lowercase__					  =				vocab_size
							lowercase__					  =				hidden_size
							lowercase__					  =				num_hidden_layers
							lowercase__					  =				num_attention_heads
							lowercase__					  =				intermediate_size
							lowercase__					  =				hidden_act
							lowercase__					  =				hidden_dropout_prob
							lowercase__					  =				attention_probs_dropout_prob
							lowercase__					  =				max_position_embeddings
							lowercase__					  =				type_vocab_size
							lowercase__					  =				type_sequence_label_size
							lowercase__					  =				initializer_range
							lowercase__					  =				num_choices




				def 							A__      (						self					:						int						):
							lowercase__					  =				ids_tensor([self.batch_size, self.seq_length],					self.vocab_size						)

							lowercase__					  =				None
							if self.use_attention_mask:
										lowercase__					  =				random_attention_mask([self.batch_size, self.seq_length]						)

							lowercase__					  =				None
							if self.use_token_type_ids:
										lowercase__					  =				ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size						)

							lowercase__					  =				RobertaPreLayerNormConfig(
							    vocab_size=self.vocab_size,					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,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					is_decoder=__lowercase,					initializer_range=self.initializer_range,					)

							return config, input_ids, token_type_ids, attention_mask




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs
							lowercase__					  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
							return config, inputs_dict




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs

							lowercase__					  =				True
							lowercase__					  =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
							lowercase__					  =				ids_tensor([self.batch_size, self.seq_length],					vocab_size=2						)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					int     		=True

				UpperCamelCase__				:					Union[str, Any]     		=(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				FlaxRobertaPreLayerNormModelTester(self						)




				@slow
				def 							A__      (						self					:						Tuple						):
							for model_class_name in self.all_model_classes:
										lowercase__					  =				model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40",					from_pt=__lowercase						)
										lowercase__					  =				model(np.ones((1, 1)						)						)
										self.assertIsNotNone(__lowercase						)

@require_flax
class 				_snake_case			(					unittest.TestCase):




				@slow
				def 							A__      (						self					:						str						):
							lowercase__					  =				FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40",					from_pt=__lowercase						)

							lowercase__					  =				np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],					dtype=jnp.intaa						)
							lowercase__					  =				model(__lowercase						)[0]
							lowercase__					  =				[1, 11, 5_0265]
							self.assertEqual(list(output.shape						),					__lowercase						)
							# compare the actual values for a slice.
							lowercase__					  =				np.array(
							    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]],					dtype=np.floataa						)
							self.assertTrue(np.allclose(output[:, :3, :3],					__lowercase,					atol=1e-4						)						)




				@slow
				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40",					from_pt=__lowercase						)

							lowercase__					  =				np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],					dtype=jnp.intaa						)
							lowercase__					  =				model(__lowercase						)[0]
							# compare the actual values for a slice.
							lowercase__					  =				np.array(
							    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]],					dtype=np.floataa						)
							self.assertTrue(np.allclose(output[:, :3, :3],					__lowercase,					atol=1e-4						)						)









import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				"huggingface/label-files"
			lowercase__					  =				"imagenet-1k-id2label.json"
			lowercase__					  =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  repo_type="dataset"  )						,  "r"  )  )
			lowercase__					  =				{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
			lowercase__					  =				{v: k for k, v in idalabel.items()}

			lowercase__					  =				"std_conv" if "bit" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			lowercase__					  =				BitConfig(
			    conv_layer=SCREAMING_SNAKE_CASE_						,  num_labels=1000						,  idalabel=SCREAMING_SNAKE_CASE_						,  labelaid=SCREAMING_SNAKE_CASE_						,  )

			return config



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if "stem.conv" in name:
						lowercase__					  =				name.replace("stem.conv"						,  "bit.embedder.convolution"  )
			if "blocks" in name:
						lowercase__					  =				name.replace("blocks"						,  "layers"  )
			if "head.fc" in name:
						lowercase__					  =				name.replace("head.fc"						,  "classifier.1"  )
			if name.startswith("norm"  ):
						lowercase__					  =				"bit." + name
			if "bit" not in name and "classifier" not in name:
						lowercase__					  =				"bit.encoder." + name

			return name



def __lowerCAmelCase (     ):
			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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__					  =				get_config(SCREAMING_SNAKE_CASE_  )

			# load original model from timm
			lowercase__					  =				create_model(SCREAMING_SNAKE_CASE_						,  pretrained=SCREAMING_SNAKE_CASE_  )
			timm_model.eval()

			# load state_dict of original model
			lowercase__					  =				timm_model.state_dict()
			for key in state_dict.copy().keys():
						lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				val.squeeze() if "head" in key else val

			# load HuggingFace model
			lowercase__					  =				BitForImageClassification(SCREAMING_SNAKE_CASE_  )
			model.eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE_  )

			# create image processor
			lowercase__					  =				create_transform(**resolve_data_config({}						,  model=SCREAMING_SNAKE_CASE_  )  )
			lowercase__					  =				transform.transforms

			lowercase__					  =				{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			lowercase__					  =				BitImageProcessor(
			    do_resize=SCREAMING_SNAKE_CASE_						,  size={"shortest_edge": timm_transforms[0].size}						,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,  do_center_crop=SCREAMING_SNAKE_CASE_						,  crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}						,  do_normalize=SCREAMING_SNAKE_CASE_						,  image_mean=timm_transforms[-1].mean.tolist()						,  image_std=timm_transforms[-1].std.tolist()						,  )

			lowercase__					  =				prepare_img()
			lowercase__					  =				transform(SCREAMING_SNAKE_CASE_  ).unsqueeze(0  )
			lowercase__					  =				processor(SCREAMING_SNAKE_CASE_						,  return_tensors="pt"  ).pixel_values

			# verify pixel values
			assert torch.allclose(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

			# verify logits
			with torch.no_grad():
						lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				outputs.logits

			print("Logits:"						,  logits[0, :3]  )
			print("Predicted class:"						,  model.config.idalabel[logits.argmax(-1  ).item()]  )
			lowercase__					  =				timm_model(SCREAMING_SNAKE_CASE_  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(SCREAMING_SNAKE_CASE_						,  outputs.logits						,  atol=1e-3  )
			print("Looks ok!"  )

			if pytorch_dump_folder_path is not None:
						Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
						print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''  )
						model.save_pretrained(SCREAMING_SNAKE_CASE_  )
						processor.save_pretrained(SCREAMING_SNAKE_CASE_  )

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


if __name__ == "__main__":
							lowercase_								=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""resnetv2_50x1_bitm""",
							    type=str,
							    help="""Name of the BiT timm model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)
							parser.add_argument(
							    """--push_to_hub""",
							    action="""store_true""",
							    help="""Whether to push the model to the hub.""",
							)

							lowercase_								=     parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




