
from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
			import tensorflow as tf

			from ..tf_utils import stable_softmax


if is_torch_available():
			import torch


_A   =     logging.get_logger(__name__)





@add_end_docstrings(
    __a					,      R'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''					,      )
class 	_lowerCAmelCase					(	__a       ):




				def      __a						(					self				,				_UpperCamelCase     )				->      np.ndarray:
							if self.framework == "tf":
										lowerCAmelCase_						=	tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()
							elif self.framework == "pt":
										lowerCAmelCase_						=	torch.nonzero(input_ids == self.tokenizer.mask_token_id				,				as_tuple=_UpperCamelCase     )
							else:
										raise ValueError("Unsupported framework"     )
							return masked_index




				def      __a						(					self				,				_UpperCamelCase     )				->      np.ndarray:
							lowerCAmelCase_						=	self.get_masked_index(_UpperCamelCase     )
							lowerCAmelCase_						=	np.prod(masked_index.shape     )
							if numel < 1:
										raise PipelineException(
										    "fill-mask"				,				self.model.base_model_prefix				,				f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""				,				)




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[Any]:
							if isinstance(_UpperCamelCase				,				_UpperCamelCase     ):
										for model_input in model_inputs:
													self._ensure_exactly_one_mask_token(model_input["input_ids"][0]     )
							else:
										for input_ids in model_inputs["input_ids"]:
													self._ensure_exactly_one_mask_token(_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=None				,				**_UpperCamelCase     )				->      Dict[str, GenericTensor]:
							if return_tensors is None:
										lowerCAmelCase_						=	self.framework
							lowerCAmelCase_						=	self.tokenizer(_UpperCamelCase				,				return_tensors=_UpperCamelCase     )
							self.ensure_exactly_one_mask_token(_UpperCamelCase     )
							return model_inputs




				def      __a						(					self				,				_UpperCamelCase     )				->      int:
							lowerCAmelCase_						=	self.model(**_UpperCamelCase     )
							lowerCAmelCase_						=	model_inputs["input_ids"]
							return model_outputs




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=5				,				_UpperCamelCase=None     )				->      Union[str, Any]:
							# Cap top_k if there are targets
							if target_ids is not None and target_ids.shape[0] < top_k:
										lowerCAmelCase_						=	target_ids.shape[0]
							lowerCAmelCase_						=	model_outputs["input_ids"][0]
							lowerCAmelCase_						=	model_outputs["logits"]

							if self.framework == "tf":
										lowerCAmelCase_						=	tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()[:, 0]

										lowerCAmelCase_						=	outputs.numpy()

										lowerCAmelCase_						=	outputs[0, masked_index, :]
										lowerCAmelCase_						=	stable_softmax(_UpperCamelCase				,				axis=-1     )
										if target_ids is not None:
													lowerCAmelCase_						=	tf.gather_nd(tf.squeeze(_UpperCamelCase				,				0     )				,				target_ids.reshape(-1				,				1     )     )
													lowerCAmelCase_						=	tf.expand_dims(_UpperCamelCase				,				0     )

										lowerCAmelCase_						=	tf.math.top_k(_UpperCamelCase				,				k=_UpperCamelCase     )
										lowerCAmelCase_	,  lowerCAmelCase_						=	topk.values.numpy(), topk.indices.numpy()
							else:
										lowerCAmelCase_						=	torch.nonzero(input_ids == self.tokenizer.mask_token_id				,				as_tuple=_UpperCamelCase     ).squeeze(-1     )
										# Fill mask pipeline supports only one ${mask_token} per sample

										lowerCAmelCase_						=	outputs[0, masked_index, :]
										lowerCAmelCase_						=	logits.softmax(dim=-1     )
										if target_ids is not None:
													lowerCAmelCase_						=	probs[..., target_ids]

										lowerCAmelCase_	,  lowerCAmelCase_						=	probs.topk(_UpperCamelCase     )

							lowerCAmelCase_						=	[]
							lowerCAmelCase_						=	values.shape[0] == 1
							for i, (_values, _predictions) in enumerate(zip(values.tolist()				,				predictions.tolist()     )     ):
										lowerCAmelCase_						=	[]
										for v, p in zip(_values				,				_predictions     ):
													# Copy is important since we're going to modify this array in place
													lowerCAmelCase_						=	input_ids.numpy().copy()
													if target_ids is not None:
																lowerCAmelCase_						=	target_ids[p].tolist()

													lowerCAmelCase_						=	p
													# Filter padding out:
													lowerCAmelCase_						=	tokens[np.where(tokens != self.tokenizer.pad_token_id     )]
													# Originally we skip special tokens to give readable output.
													# For multi masks though, the other [MASK] would be removed otherwise
													# making the output look odd, so we add them back
													lowerCAmelCase_						=	self.tokenizer.decode(_UpperCamelCase				,				skip_special_tokens=_UpperCamelCase     )
													lowerCAmelCase_						=	{"score": v, "token": p, "token_str": self.tokenizer.decode([p]     ), "sequence": sequence}
													row.append(_UpperCamelCase     )
										result.append(_UpperCamelCase     )
							if single_mask:
										return result[0]
							return result




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      int:
							if isinstance(_UpperCamelCase				,				_UpperCamelCase     ):
										lowerCAmelCase_						=	[targets]
							try:
										lowerCAmelCase_						=	self.tokenizer.get_vocab()
							except Exception:
										lowerCAmelCase_						=	{}
							lowerCAmelCase_						=	[]
							for target in targets:
										lowerCAmelCase_						=	vocab.get(_UpperCamelCase				,				_UpperCamelCase     )
										if id_ is None:
													lowerCAmelCase_						=	self.tokenizer(
													    _UpperCamelCase				,				add_special_tokens=_UpperCamelCase				,				return_attention_mask=_UpperCamelCase				,				return_token_type_ids=_UpperCamelCase				,				max_length=1				,				truncation=_UpperCamelCase				,				)["input_ids"]
													if len(_UpperCamelCase     ) == 0:
																logger.warning(
																    f"""The specified target token `{target}` does not exist in the model vocabulary. """
																    "We cannot replace it with anything meaningful, ignoring it"     )
																continue
													lowerCAmelCase_						=	input_ids[0]
													# XXX: If users encounter this pass
													# it becomes pretty slow, so let's make sure
													# The warning enables them to fix the input to
													# get faster performance.
													logger.warning(
													    f"""The specified target token `{target}` does not exist in the model vocabulary. """
													    f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_     )}`."""     )
										target_ids.append(id_     )
							lowerCAmelCase_						=	list(set(_UpperCamelCase     )     )
							if len(_UpperCamelCase     ) == 0:
										raise ValueError("At least one target must be provided when passed."     )
							lowerCAmelCase_						=	np.array(_UpperCamelCase     )
							return target_ids




				def      __a						(					self				,				_UpperCamelCase=None				,				_UpperCamelCase=None     )				->      Dict:
							lowerCAmelCase_						=	{}

							if targets is not None:
										lowerCAmelCase_						=	self.get_target_ids(_UpperCamelCase				,				_UpperCamelCase     )
										lowerCAmelCase_						=	target_ids

							if top_k is not None:
										lowerCAmelCase_						=	top_k

							if self.tokenizer.mask_token_id is None:
										raise PipelineException(
										    "fill-mask"				,				self.model.base_model_prefix				,				"The tokenizer does not define a `mask_token`."     )
							return {}, {}, postprocess_params




				def __call__(					self				,				_UpperCamelCase				,				*_UpperCamelCase				,				**_UpperCamelCase     )				->      int:
							lowerCAmelCase_						=	super().__call__(_UpperCamelCase				,				**_UpperCamelCase     )
							if isinstance(_UpperCamelCase				,				_UpperCamelCase     ) and len(_UpperCamelCase     ) == 1:
										return outputs[0]
							return outputs





import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  str=False				):




			"""simple docstring"""


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

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("cls_token", "deit.embeddings.cls_token"),
			        ("dist_token", "deit.embeddings.distillation_token"),
			        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
			        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
			        ("pos_embed", "deit.embeddings.position_embeddings"),
			    ]				)

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("norm.weight", "layernorm.weight"),
						        ("norm.bias", "layernorm.bias"),
						        ("pre_logits.fc.weight", "pooler.dense.weight"),
						        ("pre_logits.fc.bias", "pooler.dense.bias"),
						    ]				)

						# if just the base model, we should remove "deit" from all keys that start with "deit"
						lowerCAmelCase_						=	[(pair[0], pair[1][4:]) if pair[1].startswith("deit"				) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ("norm.weight", "deit.layernorm.weight"),
						        ("norm.bias", "deit.layernorm.bias"),
						        ("head.weight", "cls_classifier.weight"),
						        ("head.bias", "cls_classifier.bias"),
						        ("head_dist.weight", "distillation_classifier.weight"),
						        ("head_dist.bias", "distillation_classifier.bias"),
						    ]				)

			return rename_keys






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  str=False				):




			"""simple docstring"""


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Dict				):




			"""simple docstring"""


			lowerCAmelCase_						=	dct.pop(__lowerCAmelCase				)
			lowerCAmelCase_						=	val






def    lowerCamelCase__				(				):




			"""simple docstring"""


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






@torch.no_grad()
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	DeiTConfig()
			# all deit models have fine-tuned heads
			lowerCAmelCase_						=	False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCAmelCase_						=	1000
			lowerCAmelCase_						=	"huggingface/label-files"
			lowerCAmelCase_						=	"imagenet-1k-id2label.json"
			lowerCAmelCase_						=	json.load(open(hf_hub_download(__lowerCAmelCase    ,							__lowerCAmelCase    ,							repo_type="dataset"				)    ,							"r"				)				)
			lowerCAmelCase_						=	{int(__lowerCAmelCase				): v for k, v in idalabel.items()}
			lowerCAmelCase_						=	idalabel
			lowerCAmelCase_						=	{v: k for k, v in idalabel.items()}
			lowerCAmelCase_						=	int(deit_name[-6:-4]				)
			lowerCAmelCase_						=	int(deit_name[-3:]				)
			# size of the architecture
			if deit_name[9:].startswith("tiny"				):
						lowerCAmelCase_						=	192
						lowerCAmelCase_						=	768
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	3
			elif deit_name[9:].startswith("small"				):
						lowerCAmelCase_						=	384
						lowerCAmelCase_						=	1536
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	6
			if deit_name[9:].startswith("base"				):
						pass
			elif deit_name[4:].startswith("large"				):
						lowerCAmelCase_						=	1024
						lowerCAmelCase_						=	4096
						lowerCAmelCase_						=	24
						lowerCAmelCase_						=	16

			# load original model from timm
			lowerCAmelCase_						=	timm.create_model(__lowerCAmelCase    ,							pretrained=__lowerCAmelCase				)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCAmelCase_						=	timm_model.state_dict()
			lowerCAmelCase_						=	create_rename_keys(__lowerCAmelCase    ,							__lowerCAmelCase				)
			for src, dest in rename_keys:
						rename_key(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)
			read_in_q_k_v(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)

			# load HuggingFace model
			lowerCAmelCase_						=	DeiTForImageClassificationWithTeacher(__lowerCAmelCase				).eval()
			model.load_state_dict(__lowerCAmelCase				)

			# Check outputs on an image, prepared by DeiTImageProcessor
			lowerCAmelCase_						=	int(
			    (256 / 224) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			lowerCAmelCase_						=	DeiTImageProcessor(size=__lowerCAmelCase    ,							crop_size=config.image_size				)
			lowerCAmelCase_						=	image_processor(images=prepare_img()    ,							return_tensors="pt"				)
			lowerCAmelCase_						=	encoding["pixel_values"]
			lowerCAmelCase_						=	model(__lowerCAmelCase				)

			lowerCAmelCase_						=	timm_model(__lowerCAmelCase				)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(__lowerCAmelCase    ,							outputs.logits    ,							atol=1e-3				)

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


if __name__ == "__main__":
			_A   =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--deit_name",
			    default="vit_deit_base_distilled_patch16_224",
			    type=str,
			    help="Name of the DeiT 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."
			)

			_A   =     parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



