


"""simple docstring"""


import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


A  :     List[str]    					=    logging.get_logger(__name__)

A  :     str    					=    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

A  :     Tuple    					=    {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
A  :     int    					=    {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
A  :     List[Any]    					=    {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

A  :     List[str]    					=    {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
A  :     Any    					=    {
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
A  :     Dict    					=    {
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


A  :     Tuple    					=    {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
A  :     str    					=    {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
A  :     int    					=    {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[Any]          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					Dict          =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					str          =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					Optional[Any]          =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[str]          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					Tuple          =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					int          =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					Optional[int]          =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
A  :     Tuple    					=    collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

A  :     Union[str, Any]    					=    collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


A  :     List[Any]    					=    R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "
@add_start_docstrings(lowerCAmelCase__	)
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __call__(  self					,     __a					,     __a = None					,     __a = None					,     __a = False					,     __a = False					,     __a = None					,     __a = None					,     __a = None					,     **__a					,     ):
										if titles is None and texts is None:
													return super().__call__(
													    __a					,     padding=__a					,     truncation=__a					,     max_length=__a					,     return_tensors=__a					,     return_attention_mask=__a					,     **__a					,     )
										elif titles is None or texts is None:
													__lowerCAmelCase       					=    titles if texts is None else texts
													return super().__call__(
													    __a					,     __a					,     padding=__a					,     truncation=__a					,     max_length=__a					,     return_tensors=__a					,     return_attention_mask=__a					,     **__a					,     )
										__lowerCAmelCase       					=    titles if not isinstance(__a					,     __a			) else [titles]
										__lowerCAmelCase       					=    texts if not isinstance(__a					,     __a			) else [texts]
										__lowerCAmelCase       					=    len(__a			)
										__lowerCAmelCase       					=    questions if not isinstance(__a					,     __a			) else [questions] * n_passages
										if len(__a			) != len(__a			):
													raise ValueError(
													    f"There should be as many titles than texts but got {len(__a			)} titles and {len(__a			)} texts."			)
										__lowerCAmelCase       					=    super().__call__(__a					,     __a					,     padding=__a					,     truncation=__a			)["input_ids"]
										__lowerCAmelCase       					=    super().__call__(__a					,     add_special_tokens=__a					,     padding=__a					,     truncation=__a			)["input_ids"]
										__lowerCAmelCase       					=    {
										    "input_ids": [
										        (encoded_question_and_title + encoded_text)[:max_length]
										        if max_length is not None and truncation
										        else encoded_question_and_title + encoded_text
										        for encoded_question_and_title, encoded_text in zip(__a					,     __a			)
										    ]
										}
										if return_attention_mask is not False:
													__lowerCAmelCase       					=    []
													for input_ids in encoded_inputs["input_ids"]:
																attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
													__lowerCAmelCase       					=    attention_mask
										return self.pad(__a					,     padding=__a					,     max_length=__a					,     return_tensors=__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a = 16					,     __a = 64					,     __a = 4					,     ):
										__lowerCAmelCase       					=    reader_input["input_ids"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    reader_output[:3]
										__lowerCAmelCase       					=    len(__a			)
										__lowerCAmelCase       					=    sorted(range(__a			)					,     reverse=__a					,     key=relevance_logits.__getitem__			)
										__lowerCAmelCase       					=    []
										for doc_id in sorted_docs:
													__lowerCAmelCase       					=    list(input_ids[doc_id]			)
													# assuming question & title information is at the beginning of the sequence
													__lowerCAmelCase       					=    sequence_ids.index(self.sep_token_id					,     2			) + 1  # second sep id
													if sequence_ids[-1] == self.pad_token_id:
																__lowerCAmelCase       					=    sequence_ids.index(self.pad_token_id			)
													else:
																__lowerCAmelCase       					=    len(__a			)

													__lowerCAmelCase       					=    self._get_best_spans(
													    start_logits=start_logits[doc_id][passage_offset:sequence_len]					,     end_logits=end_logits[doc_id][passage_offset:sequence_len]					,     max_answer_length=__a					,     top_spans=__a					,     )
													for start_index, end_index in best_spans:
																start_index += passage_offset
																end_index += passage_offset
																nbest_spans_predictions.append(
																    DPRSpanPrediction(
																        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]					,     relevance_score=relevance_logits[doc_id]					,     doc_id=__a					,     start_index=__a					,     end_index=__a					,     text=self.decode(sequence_ids[start_index : end_index + 1]			)					,     )			)
													if len(__a			) >= num_spans:
																break
										return nbest_spans_predictions[:num_spans]



							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     ):
										__lowerCAmelCase       					=    []
										for start_index, start_score in enumerate(__a			):
													for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]			):
																scores.append(((start_index, start_index + answer_length), start_score + end_score)			)
										__lowerCAmelCase       					=    sorted(__a					,     key=lambda __a			: x[1]					,     reverse=__a			)
										__lowerCAmelCase       					=    []
										for (start_index, end_index), score in scores:
													if start_index > end_index:
																raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"			)
													__lowerCAmelCase       					=    end_index - start_index + 1
													if length > max_answer_length:
																raise ValueError(f"Span is too long: {length} > {max_answer_length}"			)
													if any(
													    start_index <= prev_start_index <= prev_end_index <= end_index
													    or prev_start_index <= start_index <= end_index <= prev_end_index
													    for (prev_start_index, prev_end_index) in chosen_span_intervals			):
																continue
													chosen_span_intervals.append((start_index, end_index)			)

													if len(__a			) == top_spans:
																break
										return chosen_span_intervals
@add_end_docstrings(lowerCAmelCase__	)
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Dict          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					List[str]          =READER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					int          =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					Any          =READER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase		:					Optional[int]          =["""input_ids""", """attention_mask"""]








"""simple docstring"""


import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    384
			__lowerCAmelCase       					=    7
			if "tiny" in model_name:
						__lowerCAmelCase       					=    96
						__lowerCAmelCase       					=    (2, 2, 6, 2)
						__lowerCAmelCase       					=    (3, 6, 12, 24)
			elif "small" in model_name:
						__lowerCAmelCase       					=    96
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (3, 6, 12, 24)
			elif "base" in model_name:
						__lowerCAmelCase       					=    128
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (4, 8, 16, 32)
						__lowerCAmelCase       					=    12
						__lowerCAmelCase       					=    512
			elif "large" in model_name:
						__lowerCAmelCase       					=    192
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (6, 12, 24, 48)
						__lowerCAmelCase       					=    12
						__lowerCAmelCase       					=    768

			# set label information
			__lowerCAmelCase       					=    150
			__lowerCAmelCase       					=    "huggingface/label-files"
			__lowerCAmelCase       					=    "ade20k-id2label.json"
			__lowerCAmelCase       					=    json.load(open(hf_hub_download(_UpperCamelCase					,				_UpperCamelCase					,				repo_type="dataset"   )					,				"r"   )   )
			__lowerCAmelCase       					=    {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
			__lowerCAmelCase       					=    {v: k for k, v in idalabel.items()}

			__lowerCAmelCase       					=    SwinConfig(
			    embed_dim=_UpperCamelCase					,				depths=_UpperCamelCase					,				num_heads=_UpperCamelCase					,				window_size=_UpperCamelCase					,				out_features=["stage1", "stage2", "stage3", "stage4"]					,				)
			__lowerCAmelCase       					=    UperNetConfig(
			    backbone_config=_UpperCamelCase					,				auxiliary_in_channels=_UpperCamelCase					,				num_labels=_UpperCamelCase					,				idalabel=_UpperCamelCase					,				labelaid=_UpperCamelCase					,				)

			return config


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []

			# fmt: off
			# stem
			rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")   )
			rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")   )
			rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")   )
			rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias")   )
			# stages
			for i in range(len(config.backbone_config.depths   )   ):
						for j in range(config.backbone_config.depths[i]   ):
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")   )

						if i < 3:
									rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")   )
									rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")   )
									rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")   )
						rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")   )
						rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")   )

			# decode head
			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"),
			    ]   )
			# fmt: on

			return rename_keys


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    dct.pop(_UpperCamelCase   )
			__lowerCAmelCase       					=    val


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
			for i in range(len(backbone_config.depths   )   ):
						__lowerCAmelCase       					=    num_features[i]
						for j in range(backbone_config.depths[i]   ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowerCAmelCase       					=    state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"   )
									__lowerCAmelCase       					=    state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"   )
									# next, add query, keys and values (in that order) to the state dict
									__lowerCAmelCase       					=    in_proj_weight[:dim, :]
									__lowerCAmelCase       					=    in_proj_bias[: dim]
									__lowerCAmelCase       					=    in_proj_weight[
									    dim : dim * 2, :
									]
									__lowerCAmelCase       					=    in_proj_bias[
									    dim : dim * 2
									]
									__lowerCAmelCase       					=    in_proj_weight[
									    -dim :, :
									]
									__lowerCAmelCase       					=    in_proj_bias[-dim :]
									# fmt: on


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    x.shape
			__lowerCAmelCase       					=    x.reshape(_UpperCamelCase					,				4					,				in_channel // 4   )
			__lowerCAmelCase       					=    x[:, [0, 2, 1, 3], :].transpose(1					,				2   ).reshape(_UpperCamelCase					,				_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    x.shape
			__lowerCAmelCase       					=    x.reshape(_UpperCamelCase					,				in_channel // 4					,				4   )
			__lowerCAmelCase       					=    x[:, :, [0, 2, 1, 3]].transpose(1					,				2   ).reshape(_UpperCamelCase					,				_UpperCamelCase   )

			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    x.shape[0]
			__lowerCAmelCase       					=    x.reshape(4					,				in_channel // 4   )
			__lowerCAmelCase       					=    x[[0, 2, 1, 3], :].transpose(0					,				1   ).reshape(_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    x.shape[0]
			__lowerCAmelCase       					=    x.reshape(in_channel // 4					,				4   )
			__lowerCAmelCase       					=    x[:, [0, 2, 1, 3]].transpose(0					,				1   ).reshape(_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {
			    "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
			    "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
			    "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
			    "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
			}
			__lowerCAmelCase       					=    model_name_to_url[model_name]
			__lowerCAmelCase       					=    torch.hub.load_state_dict_from_url(_UpperCamelCase					,				map_location="cpu"					,				file_name=_UpperCamelCase   )[
			    "state_dict"
			]

			for name, param in state_dict.items():
						print(_UpperCamelCase					,				param.shape   )

			__lowerCAmelCase       					=    get_upernet_config(_UpperCamelCase   )
			__lowerCAmelCase       					=    UperNetForSemanticSegmentation(_UpperCamelCase   )
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						__lowerCAmelCase       					=    state_dict.pop(_UpperCamelCase   )
						if "bn" in key:
									__lowerCAmelCase       					=    key.replace("bn"					,				"batch_norm"   )
						__lowerCAmelCase       					=    val

			# rename keys
			__lowerCAmelCase       					=    create_rename_keys(_UpperCamelCase   )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )
			read_in_q_k_v(_UpperCamelCase					,				config.backbone_config   )

			# fix downsample parameters
			for key, value in state_dict.items():
						if "downsample" in key:
									if "reduction" in key:
												__lowerCAmelCase       					=    reverse_correct_unfold_reduction_order(_UpperCamelCase   )
									if "norm" in key:
												__lowerCAmelCase       					=    reverse_correct_unfold_norm_order(_UpperCamelCase   )

			model.load_state_dict(_UpperCamelCase   )

			# verify on image
			__lowerCAmelCase       					=    "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
			__lowerCAmelCase       					=    Image.open(requests.get(_UpperCamelCase					,				stream=_UpperCamelCase   ).raw   ).convert("RGB"   )

			__lowerCAmelCase       					=    SegformerImageProcessor()
			__lowerCAmelCase       					=    processor(_UpperCamelCase					,				return_tensors="pt"   ).pixel_values

			with torch.no_grad():
						__lowerCAmelCase       					=    model(_UpperCamelCase   )
						__lowerCAmelCase       					=    outputs.logits

			print(logits.shape   )
			print("First values of logits:"					,				logits[0, 0, :3, :3]   )
			# assert values
			if model_name == "upernet-swin-tiny":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]   )
			elif model_name == "upernet-swin-small":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]   )
			elif model_name == "upernet-swin-base":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]   )
			elif model_name == "upernet-swin-large":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]   )
			print("Logits:"					,				outputs.logits[0, 0, :3, :3]   )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]					,				_UpperCamelCase					,				atol=1e-4   )
			print("Looks ok!"   )

			if pytorch_dump_folder_path is not None:
						print(f"Saving model {model_name} to {pytorch_dump_folder_path}"   )
						model.save_pretrained(_UpperCamelCase   )
						print(f"Saving processor to {pytorch_dump_folder_path}"   )
						processor.save_pretrained(_UpperCamelCase   )

			if push_to_hub:
						print(f"Pushing model and processor for {model_name} to hub"   )
						model.push_to_hub(f"openmmlab/{model_name}"   )
						processor.push_to_hub(f"openmmlab/{model_name}"   )


if __name__ == "__main__":
	A  :     int    					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="upernet-swin-tiny",
	    type=str,
	    choices=[f'''upernet-swin-{size}''' for size in ["tiny", "small", "base", "large"]],
	    help="Name of the Swin + UperNet 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 or not to push the converted model to the 🤗 hub."
	)

	A  :     List[Any]    					=    parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




