
import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	List[str]    =BarthezTokenizer
							__lowerCamelCase	:	Optional[int]    =BarthezTokenizerFast
							__lowerCamelCase	:	List[Any]    =True
							__lowerCamelCase	:	str    =True





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											super().setUp()

											__a       =		BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""	)
											tokenizer.save_pretrained(self.tmpdirname	)
											tokenizer.save_pretrained(self.tmpdirname      ,				legacy_format=__lowercase	)
											__a       =		tokenizer





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

											'''simple docstring'''
											__a       =		"""<pad>"""
											__a       =		1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase	)      ,				__lowercase	)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase	)      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]      ,				"""<s>"""	)
											self.assertEqual(vocab_keys[1]      ,				"""<pad>"""	)
											self.assertEqual(vocab_keys[-1]      ,				"""<mask>"""	)
											self.assertEqual(len(__lowercase	)      ,				101122	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											self.assertEqual(self.get_tokenizer().vocab_size      ,				101122	)





							@require_torch
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											__a       =		[0, 57, 3018, 70307, 91, 2]

											__a       =		self.tokenizer(
											    __lowercase      ,				max_length=len(__lowercase	)      ,				padding=__lowercase      ,				truncation=__lowercase      ,				return_tensors="""pt"""	)
											self.assertIsInstance(__lowercase      ,				__lowercase	)

											self.assertEqual((2, 6)      ,				batch.input_ids.shape	)
											self.assertEqual((2, 6)      ,				batch.attention_mask.shape	)
											__a       =		batch.input_ids.tolist()[0]
											self.assertListEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											if not self.test_rust_tokenizer:
															return

											__a       =		self.get_tokenizer()
											__a       =		self.get_rust_tokenizer()

											__a       =		"""I was born in 92000, and this is falsé."""

											__a       =		tokenizer.tokenize(__lowercase	)
											__a       =		rust_tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		self.get_rust_tokenizer()
											__a       =		tokenizer.encode(__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)







							@slow
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											# fmt: off
											__a       =		{"""input_ids""": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											__a       =		[
											    """Le transformeur est un modèle d\'apprentissage profond introduit en 2017, """
											    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
											    """À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
											    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
											    """telles que la traduction et la synthèse de texte.""",
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=__lowercase      ,				model_name="""moussaKam/mbarthez"""      ,				revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""      ,				sequences=__lowercase      ,				)





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				__a       =		DPTConfig(embedding_type="""hybrid"""    )

				if "large" in checkpoint_url:
								__a       =		1024
								__a       =		4096
								__a       =		24
								__a       =		16
								__a       =		[5, 11, 17, 23]
								__a       =		[256, 512, 1024, 1024]
								__a       =		(1, 384, 384)

				if "nyu" or "midas" in checkpoint_url:
								__a       =		768
								__a       =		[1, 1, 1, 0.5]
								__a       =		[256, 512, 768, 768]
								__a       =		150
								__a       =		16
								__a       =		(1, 384, 384)
								__a       =		False
								__a       =		"""project"""

				if "ade" in checkpoint_url:
								__a       =		True
								__a       =		768
								__a       =		[1, 1, 1, 0.5]
								__a       =		150
								__a       =		16
								__a       =		"""huggingface/label-files"""
								__a       =		"""ade20k-id2label.json"""
								__a       =		json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     repo_type="""dataset"""    )    )				,     """r"""    )    )
								__a       =		{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
								__a       =		idalabel
								__a       =		{v: k for k, v in idalabel.items()}
								__a       =		[1, 150, 480, 480]

				return config, expected_shape

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				__a       =		["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
				for k in ignore_keys:
								state_dict.pop(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple    ):
				"""simple docstring"""







				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								__a       =		name.replace("""pretrained.model"""				,     """dpt.encoder"""    )
				if "pretrained.model" in name:
								__a       =		name.replace("""pretrained.model"""				,     """dpt.embeddings"""    )
				if "patch_embed" in name:
								__a       =		name.replace("""patch_embed"""				,     """"""    )
				if "pos_embed" in name:
								__a       =		name.replace("""pos_embed"""				,     """position_embeddings"""    )
				if "attn.proj" in name:
								__a       =		name.replace("""attn.proj"""				,     """attention.output.dense"""    )
				if "proj" in name and "project" not in name:
								__a       =		name.replace("""proj"""				,     """projection"""    )
				if "blocks" in name:
								__a       =		name.replace("""blocks"""				,     """layer"""    )
				if "mlp.fc1" in name:
								__a       =		name.replace("""mlp.fc1"""				,     """intermediate.dense"""    )
				if "mlp.fc2" in name:
								__a       =		name.replace("""mlp.fc2"""				,     """output.dense"""    )
				if "norm1" in name and "backbone" not in name:
								__a       =		name.replace("""norm1"""				,     """layernorm_before"""    )
				if "norm2" in name and "backbone" not in name:
								__a       =		name.replace("""norm2"""				,     """layernorm_after"""    )
				if "scratch.output_conv" in name:
								__a       =		name.replace("""scratch.output_conv"""				,     """head"""    )
				if "scratch" in name:
								__a       =		name.replace("""scratch"""				,     """neck"""    )
				if "layer1_rn" in name:
								__a       =		name.replace("""layer1_rn"""				,     """convs.0"""    )
				if "layer2_rn" in name:
								__a       =		name.replace("""layer2_rn"""				,     """convs.1"""    )
				if "layer3_rn" in name:
								__a       =		name.replace("""layer3_rn"""				,     """convs.2"""    )
				if "layer4_rn" in name:
								__a       =		name.replace("""layer4_rn"""				,     """convs.3"""    )
				if "refinenet" in name:
								__a       =		int(name[len("""neck.refinenet"""    ) : len("""neck.refinenet"""    ) + 1]    )
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								__a       =		name.replace(f"refinenet{layer_idx}"				,     f"fusion_stage.layers.{abs(layer_idx-4    )}"    )
				if "out_conv" in name:
								__a       =		name.replace("""out_conv"""				,     """projection"""    )
				if "resConfUnit1" in name:
								__a       =		name.replace("""resConfUnit1"""				,     """residual_layer1"""    )
				if "resConfUnit2" in name:
								__a       =		name.replace("""resConfUnit2"""				,     """residual_layer2"""    )
				if "conv1" in name:
								__a       =		name.replace("""conv1"""				,     """convolution1"""    )
				if "conv2" in name:
								__a       =		name.replace("""conv2"""				,     """convolution2"""    )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess1.0.project.0"""				,     """neck.reassemble_stage.readout_projects.0.0"""    )
				if "pretrained.act_postprocess2.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess2.0.project.0"""				,     """neck.reassemble_stage.readout_projects.1.0"""    )
				if "pretrained.act_postprocess3.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess3.0.project.0"""				,     """neck.reassemble_stage.readout_projects.2.0"""    )
				if "pretrained.act_postprocess4.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess4.0.project.0"""				,     """neck.reassemble_stage.readout_projects.3.0"""    )

				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess1.3"""				,     """neck.reassemble_stage.layers.0.projection"""    )
				if "pretrained.act_postprocess1.4" in name:
								__a       =		name.replace("""pretrained.act_postprocess1.4"""				,     """neck.reassemble_stage.layers.0.resize"""    )
				if "pretrained.act_postprocess2.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess2.3"""				,     """neck.reassemble_stage.layers.1.projection"""    )
				if "pretrained.act_postprocess2.4" in name:
								__a       =		name.replace("""pretrained.act_postprocess2.4"""				,     """neck.reassemble_stage.layers.1.resize"""    )
				if "pretrained.act_postprocess3.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess3.3"""				,     """neck.reassemble_stage.layers.2.projection"""    )
				if "pretrained.act_postprocess4.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess4.3"""				,     """neck.reassemble_stage.layers.3.projection"""    )
				if "pretrained.act_postprocess4.4" in name:
								__a       =		name.replace("""pretrained.act_postprocess4.4"""				,     """neck.reassemble_stage.layers.3.resize"""    )
				if "pretrained" in name:
								__a       =		name.replace("""pretrained"""				,     """dpt"""    )
				if "bn" in name:
								__a       =		name.replace("""bn"""				,     """batch_norm"""    )
				if "head" in name:
								__a       =		name.replace("""head"""				,     """head.head"""    )
				if "encoder.norm" in name:
								__a       =		name.replace("""encoder.norm"""				,     """layernorm"""    )
				if "auxlayer" in name:
								__a       =		name.replace("""auxlayer"""				,     """auxiliary_head.head"""    )
				if "backbone" in name:
								__a       =		name.replace("""backbone"""				,     """backbone.bit.encoder"""    )

				if ".." in name:
								__a       =		name.replace(""".."""				,     """."""    )

				if "stem.conv" in name:
								__a       =		name.replace("""stem.conv"""				,     """bit.embedder.convolution"""    )
				if "blocks" in name:
								__a       =		name.replace("""blocks"""				,     """layers"""    )
				if "convolution" in name and "backbone" in name:
								__a       =		name.replace("""convolution"""				,     """conv"""    )
				if "layer" in name and "backbone" in name:
								__a       =		name.replace("""layer"""				,     """layers"""    )
				if "backbone.bit.encoder.bit" in name:
								__a       =		name.replace("""backbone.bit.encoder.bit"""				,     """backbone.bit"""    )
				if "embedder.conv" in name:
								__a       =		name.replace("""embedder.conv"""				,     """embedder.convolution"""    )
				if "backbone.bit.encoder.stem.norm" in name:
								__a       =		name.replace("""backbone.bit.encoder.stem.norm"""				,     """backbone.bit.embedder.norm"""    )
				return name

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				for i in range(config.num_hidden_layers    ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								__a       =		state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"    )
								__a       =		state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"    )
								# next, add query, keys and values (in that order) to the state dict
								__a       =		in_proj_weight[: config.hidden_size, :]
								__a       =		in_proj_bias[: config.hidden_size]
								__a       =		in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__a       =		in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__a       =		in_proj_weight[
								    -config.hidden_size :, :
								]
								__a       =		in_proj_bias[-config.hidden_size :]

def   lowerCAmelCase__						(							):
				"""simple docstring"""







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

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a     ,					__a       =		get_dpt_config(_SCREAMING_SNAKE_CASE    )
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				__a       =		torch.load(_SCREAMING_SNAKE_CASE				,     map_location="""cpu"""    )
				# remove certain keys
				remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
				# rename keys
				for key in state_dict.copy().keys():
								__a       =		state_dict.pop(_SCREAMING_SNAKE_CASE    )
								__a       =		val
				# read in qkv matrices
				read_in_q_k_v(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# load HuggingFace model
				__a       =		DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(_SCREAMING_SNAKE_CASE    )
				model.load_state_dict(_SCREAMING_SNAKE_CASE    )
				model.eval()

				# Check outputs on an image
				__a       =		480 if """ade""" in checkpoint_url else 384
				__a       =		DPTImageProcessor(size=_SCREAMING_SNAKE_CASE    )

				__a       =		prepare_img()
				__a       =		image_processor(_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""    )

				# forward pass
				__a       =		model(**_SCREAMING_SNAKE_CASE    ).logits if """ade""" in checkpoint_url else model(**_SCREAMING_SNAKE_CASE    ).predicted_depth

				if show_prediction:
								__a       =		(
								    torch.nn.functional.interpolate(
								        outputs.unsqueeze(1    )				,     size=(image.size[1], image.size[0])				,     mode="""bicubic"""				,     align_corners=_SCREAMING_SNAKE_CASE				,     )
								    .squeeze()
								    .cpu()
								    .numpy()
								)

								Image.fromarray((prediction / prediction.max()) * 255    ).show()

				if pytorch_dump_folder_path is not None:
								Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
								print(f"Saving model 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:
								model.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )
								image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )


if __name__ == "__main__":
						lowerCamelCase__			= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
						    type=str,
						    help="""URL of the original DPT checkpoint you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    required=False,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument(
						    """--push_to_hub""",
						    action="""store_true""",
						)
						parser.add_argument(
						    """--model_name""",
						    default="""dpt-large""",
						    type=str,
						    help="""Name of the model, in case you're pushing to the hub.""",
						)
						parser.add_argument(
						    """--show_prediction""",
						    action="""store_true""",
						)

						lowerCamelCase__			= parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)
