





'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_         =			[]
for i in range(6):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
      )
      rename_keys.append(
          (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
      )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
      rename_keys.append(
          (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
      )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
      )
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
      )
      rename_keys.append(
          (
              f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
              f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
          )
      )
      rename_keys.append(
          (
              f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
              f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
          )
      )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
      )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
      )
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
      )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   Optional[int]   )						->Optional[Any]:
    _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      val





def lowerCamelCase						(					__lowerCamelCase			:   Any   )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE     =      OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _SCREAMING_SNAKE_CASE     =      key.replace("""backbone.0.body""" ,	"""backbone.conv_encoder.model"""   )
            _SCREAMING_SNAKE_CASE     =      value
        else:
            _SCREAMING_SNAKE_CASE     =      value

    return new_state_dict





def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   str=False   )						->int:
    _SCREAMING_SNAKE_CASE     =      """"""
    if is_panoptic:
        _SCREAMING_SNAKE_CASE     =      """detr."""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'   )
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'   )
        # next, add query, keys and values (in that order) to the state dict
        _SCREAMING_SNAKE_CASE     =      in_proj_weight[:256, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias[:256]
        _SCREAMING_SNAKE_CASE     =      in_proj_weight[256:512, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias[256:512]
        _SCREAMING_SNAKE_CASE     =      in_proj_weight[-256:, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'   )
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'   )
        # next, add query, keys and values (in that order) to the state dict
        _SCREAMING_SNAKE_CASE     =      in_proj_weight[:256, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias[:256]
        _SCREAMING_SNAKE_CASE     =      in_proj_weight[256:512, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias[256:512]
        _SCREAMING_SNAKE_CASE     =      in_proj_weight[-256:, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(
            F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'   )
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _SCREAMING_SNAKE_CASE     =      in_proj_weight_cross_attn[:256, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias_cross_attn[:256]
        _SCREAMING_SNAKE_CASE     =      in_proj_weight_cross_attn[256:512, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias_cross_attn[256:512]
        _SCREAMING_SNAKE_CASE     =      in_proj_weight_cross_attn[-256:, :]
        _SCREAMING_SNAKE_CASE     =      in_proj_bias_cross_attn[-256:]





def lowerCamelCase						(					)						->Optional[Any]:
    _SCREAMING_SNAKE_CASE     =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE     =      Image.open(requests.get(__lowerCamelCase ,	stream=__lowerCamelCase   ).raw   )

    return im





@torch.no_grad()
def lowerCamelCase						(					__lowerCamelCase			:   Dict ,	__lowerCamelCase			:   str   )						->Optional[int]:
    _SCREAMING_SNAKE_CASE     =      DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        _SCREAMING_SNAKE_CASE     =      """resnet101"""
    if "dc5" in model_name:
        _SCREAMING_SNAKE_CASE     =      True
    _SCREAMING_SNAKE_CASE     =      """panoptic""" in model_name
    if is_panoptic:
        _SCREAMING_SNAKE_CASE     =      250
    else:
        _SCREAMING_SNAKE_CASE     =      91
        _SCREAMING_SNAKE_CASE     =      """huggingface/label-files"""
        _SCREAMING_SNAKE_CASE     =      """coco-detection-id2label.json"""
        _SCREAMING_SNAKE_CASE     =      json.load(open(hf_hub_download(__lowerCamelCase ,	__lowerCamelCase ,	repo_type="""dataset"""   ) ,	"""r"""   )   )
        _SCREAMING_SNAKE_CASE     =      {int(__lowerCamelCase   ): v for k, v in idalabel.items()}
        _SCREAMING_SNAKE_CASE     =      idalabel
        _SCREAMING_SNAKE_CASE     =      {v: k for k, v in idalabel.items()}

    # load image processor
    _SCREAMING_SNAKE_CASE     =      """coco_panoptic""" if is_panoptic else """coco_detection"""
    _SCREAMING_SNAKE_CASE     =      DetrImageProcessor(format=__lowerCamelCase   )

    # prepare image
    _SCREAMING_SNAKE_CASE     =      prepare_img()
    _SCREAMING_SNAKE_CASE     =      image_processor(images=__lowerCamelCase ,	return_tensors="""pt"""   )
    _SCREAMING_SNAKE_CASE     =      encoding["""pixel_values"""]

    logger.info(F'Converting model {model_name}...'   )

    # load original model from torch hub
    _SCREAMING_SNAKE_CASE     =      torch.hub.load("""facebookresearch/detr""" ,	__lowerCamelCase ,	pretrained=__lowerCamelCase   ).eval()
    _SCREAMING_SNAKE_CASE     =      detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            _SCREAMING_SNAKE_CASE     =      """detr.""" + src
        rename_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      rename_backbone_keys(__lowerCamelCase   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(__lowerCamelCase ,	is_panoptic=__lowerCamelCase   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _SCREAMING_SNAKE_CASE     =      """detr.model.""" if is_panoptic else """model."""
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith("""detr"""   )
                and not key.startswith("""class_labels_classifier"""   )
                and not key.startswith("""bbox_predictor"""   )
            ):
                _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
            elif key.startswith("""bbox_attention"""   ) or key.startswith("""mask_head"""   ):
                continue
            else:
                _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
        else:
            if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
                _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
    # finally, create HuggingFace model and load state dict
    _SCREAMING_SNAKE_CASE     =      DetrForSegmentation(__lowerCamelCase   ) if is_panoptic else DetrForObjectDetection(__lowerCamelCase   )
    model.load_state_dict(__lowerCamelCase   )
    model.eval()
    # verify our conversion
    _SCREAMING_SNAKE_CASE     =      detr(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      model(__lowerCamelCase   )
    assert torch.allclose(outputs.logits ,	original_outputs["""pred_logits"""] ,	atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes ,	original_outputs["""pred_boxes"""] ,	atol=1e-4   )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks ,	original_outputs["""pred_masks"""] ,	atol=1e-4   )

    # Save model and image processor
    logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'   )
    Path(__lowerCamelCase   ).mkdir(exist_ok=__lowerCamelCase   )
    model.save_pretrained(__lowerCamelCase   )
    image_processor.save_pretrained(__lowerCamelCase   )


if __name__ == "__main__":
      lowercase_         =			argparse.ArgumentParser()

      parser.add_argument(
          """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR model you'd like to convert."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
      )
      lowercase_         =			parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase_         =			"""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowercase_         =			"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

lowercase_         =			"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION	)
class  a_					(    datasets.Metric	):

    '''simple docstring'''


    def       snake_case_(		self	)		->       Union[str, Any]:
        if version.parse(scb.__version__	) < version.parse("""1.4.12"""	):
            raise ImportWarning(
                """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
                """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""	)
        return datasets.MetricInfo(
            description=_DESCRIPTION			,		citation=_CITATION			,		homepage="""https://github.com/mjpost/sacreBLEU"""			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""			,		id="""sequence"""	),
                    """references""": datasets.Sequence(datasets.Value("""string"""			,		id="""sequence"""	)			,		id="""references"""	),
                }	)			,		codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]			,		reference_urls=[
                """https://github.com/mjpost/sacreBLEU""",
                """https://en.wikipedia.org/wiki/BLEU""",
                """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
            ]			,		)





    def       snake_case_(		self			,		A			,		A			,		A="exp"			,		A=None			,		A=False			,		A=False			,		A=None			,		A=False			,		)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      len(references[0]	)
        if any(len(A	) != references_per_prediction for refs in references	):
            raise ValueError("""Sacrebleu requires the same number of references for each prediction"""	)
        _SCREAMING_SNAKE_CASE     =      [[refs[i] for refs in references] for i in range(A	)]
        _SCREAMING_SNAKE_CASE     =      scb.corpus_bleu(
            A			,		A			,		smooth_method=A			,		smooth_value=A			,		force=A			,		lowercase=A			,		use_effective_order=A			,		**({"""tokenize""": tokenize} if tokenize else {})			,		)
        _SCREAMING_SNAKE_CASE     =      {
            """score""": output.score,
            """counts""": output.counts,
            """totals""": output.totals,
            """precisions""": output.precisions,
            """bp""": output.bp,
            """sys_len""": output.sys_len,
            """ref_len""": output.ref_len,
        }
        return output_dict



