


'''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()
A   :  Tuple       		=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A   :  List[Any]       		=				[]
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  :    Dict						,lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    str     ):
       _A  :			str											=      state_dict.pop(lowerCamelCase     )
       _A  :			Optional[Any]											=      val






def    lowerCAmelCase__ (       lowerCamelCase  :    Any     ):
       _A  :			Optional[Any]											=      OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     _A  :			str											=      key.replace('backbone.0.body'						,'backbone.conv_encoder.model'     )
                     _A  :			List[Any]											=      value
              else:
                     _A  :			str											=      value

       return new_state_dict






def    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    Tuple=False     ):
       _A  :			Dict											=      ''
       if is_panoptic:
              _A  :			Tuple											=      '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)
              _A  :			Optional[Any]											=      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'     )
              _A  :			Optional[int]											=      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
              _A  :			Any											=      in_proj_weight[:256, :]
              _A  :			List[str]											=      in_proj_bias[:256]
              _A  :			Union[str, Any]											=      in_proj_weight[256:512, :]
              _A  :			int											=      in_proj_bias[256:512]
              _A  :			Any											=      in_proj_weight[-256:, :]
              _A  :			str											=      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
              _A  :			Tuple											=      state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
              _A  :			Dict											=      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
              _A  :			Any											=      in_proj_weight[:256, :]
              _A  :			Union[str, Any]											=      in_proj_bias[:256]
              _A  :			List[Any]											=      in_proj_weight[256:512, :]
              _A  :			Dict											=      in_proj_bias[256:512]
              _A  :			Union[str, Any]											=      in_proj_weight[-256:, :]
              _A  :			Optional[int]											=      in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              _A  :			Dict											=      state_dict.pop(
                  F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'     )
              _A  :			Union[str, Any]											=      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
              _A  :			Optional[Any]											=      in_proj_weight_cross_attn[:256, :]
              _A  :			Optional[Any]											=      in_proj_bias_cross_attn[:256]
              _A  :			Union[str, Any]											=      in_proj_weight_cross_attn[256:512, :]
              _A  :			Any											=      in_proj_bias_cross_attn[256:512]
              _A  :			Optional[Any]											=      in_proj_weight_cross_attn[-256:, :]
              _A  :			Dict											=      in_proj_bias_cross_attn[-256:]






def    lowerCAmelCase__ (       ):
       _A  :			Dict											=      'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A  :			Any											=      Image.open(requests.get(lowerCamelCase						,stream=lowerCamelCase     ).raw     )

       return im






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

       # load image processor
       _A  :			int											=      'coco_panoptic' if is_panoptic else 'coco_detection'
       _A  :			Tuple											=      DetrImageProcessor(format=lowerCamelCase     )

       # prepare image
       _A  :			Tuple											=      prepare_img()
       _A  :			List[Any]											=      image_processor(images=lowerCamelCase						,return_tensors='pt'     )
       _A  :			Any											=      encoding['pixel_values']

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

       # load original model from torch hub
       _A  :			Union[str, Any]											=      torch.hub.load('facebookresearch/detr'						,lowerCamelCase						,pretrained=lowerCamelCase     ).eval()
       _A  :			Optional[Any]											=      detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     _A  :			Tuple											=      'detr.' + src
              rename_key(lowerCamelCase						,lowerCamelCase						,lowerCamelCase     )
       _A  :			Union[str, Any]											=      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
       _A  :			List[Any]											=      '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'     )
                     ):
                            _A  :			List[str]											=      state_dict.pop(lowerCamelCase     )
                            _A  :			List[str]											=      val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            _A  :			Tuple											=      state_dict.pop(lowerCamelCase     )
                            _A  :			List[str]											=      val
                     elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
                            continue
                     else:
                            _A  :			List[Any]											=      state_dict.pop(lowerCamelCase     )
                            _A  :			Optional[Any]											=      val
              else:
                     if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
                            _A  :			Optional[Any]											=      state_dict.pop(lowerCamelCase     )
                            _A  :			Dict											=      val
    # finally, create HuggingFace model and load state dict
       _A  :			List[str]											=      DetrForSegmentation(lowerCamelCase     ) if is_panoptic else DetrForObjectDetection(lowerCamelCase     )
       model.load_state_dict(lowerCamelCase     )
       model.eval()
       # verify our conversion
       _A  :			List[str]											=      detr(lowerCamelCase     )
       _A  :			Optional[int]											=      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__":
       A   :  Dict       		=				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.'''
       )
       A   :  Dict       		=				parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


A   :  Optional[Any]       		=				-1 if torch_device == '''cpu''' else 0


@is_pipeline_test
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""
    a									=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    a									=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			str											=      SummarizationPipeline(model=SCREAMING_SNAKE_CASE   ,							tokenizer=SCREAMING_SNAKE_CASE)
           return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           _A  :			str											=      summarizer.model

           _A  :			Tuple											=      summarizer('(CNN)The Palestinian Authority officially became')
           self.assertEqual(SCREAMING_SNAKE_CASE   ,							[{'summary_text': ANY(SCREAMING_SNAKE_CASE)}])

           _A  :			Optional[Any]											=      summarizer(
               '(CNN)The Palestinian Authority officially became '   ,							num_beams=2   ,							min_length=2   ,							max_length=5   ,							)
           self.assertEqual(SCREAMING_SNAKE_CASE   ,							[{'summary_text': ANY(SCREAMING_SNAKE_CASE)}])

           # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
           _A  :			Union[str, Any]											=      [
               'SwitchTransformersConfig',
               'T5Config',
               'LongT5Config',
               'LEDConfig',
               'PegasusXConfig',
               'FSMTConfig',
               'M2M100Config',
               'ProphetNetConfig',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
           ]
           if model.config.__class__.__name__ not in model_can_handle_longer_seq:
                  # Too long and exception is expected.
                  # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
                  # the embedding layer.
                  if not (
                      isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                      and get_gpu_count() > 0
                      and len(summarizer.model.trainable_weights) > 0
                  ):
                         with self.assertRaises(SCREAMING_SNAKE_CASE):
                                _A  :			Any											=      summarizer('This ' * 1000)
           _A  :			int											=      summarizer('This ' * 1000   ,							truncation=TruncationStrategy.ONLY_FIRST)


    @require_torch
    def      A					(						self    :							int):
           _A  :			Tuple											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='pt')
           _A  :			List[str]											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_tf
    def      A					(						self    :							int):
           _A  :			Optional[Any]											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='tf')
           _A  :			Any											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_torch
    @slow
    def      A					(						self    :							List[Any]):
           _A  :			int											=      pipeline(task='summarization'   ,							device=SCREAMING_SNAKE_CASE)
           _A  :			int											=      (
               ' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'
               ' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'
               ' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'
               ' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'
               ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
               ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
               ' situation in Palestinian territories, paving the way for possible war crimes investigations against'
               ' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'
               ' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'
               ' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'
               ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
               ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
               ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
               ' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'
               ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
               ' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'
               ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
               ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
               ' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'
               ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
               ' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'
               ' decision to join a treaty to which over 100 countries around the world are members." In January, when'
               ' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'
               ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
               ' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'
               ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
               ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
               ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
               ' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'
               ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
               ' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'
               ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
               ' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'
               ' will include alleged war crimes committed since June. The International Criminal Court was set up in'
               ' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'
               ' and Faith Karimi contributed to this report.'
           )
           _A  :			Optional[Any]											=      (
               ' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'
               ' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'
               ' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'
               ' says governments seeking to penalize Palestine should end pressure .'
           )
           _A  :			Union[str, Any]											=      summarizer(SCREAMING_SNAKE_CASE)
           self.assertEqual(result[0]['summary_text']   ,							SCREAMING_SNAKE_CASE)






