





'''simple docstring'''






import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image

      from transformers.image_utils import load_image
else:







      class 		UpperCAmelCase   :


        """simple docstring"""







        @staticmethod
        def   UpperCamelCase__      (				*UpperCamelCase__    :					Any							,	**UpperCamelCase__    :					List[str]  )     ->						Union[str, Any]:
          pass




      def 					_a   (__SCREAMING_SNAKE_CASE   ):


        """simple docstring"""

        return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__lowerCamelCase						: Dict		=   (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)







@is_pipeline_test
@require_torch
@require_vision
class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


  @require_pytesseract
  @require_vision
  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					Optional[Any]  )     ->						List[str]:
    _UpperCamelCase       			=pipeline(
        '''document-question-answering'''							,	model=UpperCamelCase__							,	tokenizer=UpperCamelCase__							,	image_processor=UpperCamelCase__  )

    _UpperCamelCase       			=INVOICE_URL
    _UpperCamelCase       			=list(zip(*apply_tesseract(load_image(UpperCamelCase__  )							,	UpperCamelCase__							,	''''''  )  )  )
    _UpperCamelCase       			='''What is the placebo?'''
    _UpperCamelCase       			=[
        {
            '''image''': load_image(UpperCamelCase__  ),
            '''question''': question,
        },
        {
            '''image''': image,
            '''question''': question,
        },
        {
            '''image''': image,
            '''question''': question,
            '''word_boxes''': word_boxes,
        },
    ]
    return dqa_pipeline, examples


  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					Tuple  )     ->						Union[str, Any]:
    _UpperCamelCase       			=dqa_pipeline(UpperCamelCase__							,	top_k=2  )
    self.assertEqual(
        UpperCamelCase__							,	[
            [
                {'''score''': ANY(UpperCamelCase__  ), '''answer''': ANY(UpperCamelCase__  ), '''start''': ANY(UpperCamelCase__  ), '''end''': ANY(UpperCamelCase__  )},
                {'''score''': ANY(UpperCamelCase__  ), '''answer''': ANY(UpperCamelCase__  ), '''start''': ANY(UpperCamelCase__  ), '''end''': ANY(UpperCamelCase__  )},
            ]
        ]
        * 3							,	)


  @require_torch
  @require_detectrona
  @require_pytesseract
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=pipeline('''document-question-answering'''							,	model='''hf-internal-testing/tiny-random-layoutlmv2'''  )
    _UpperCamelCase       			=INVOICE_URL
    _UpperCamelCase       			='''How many cats are there?'''

    _UpperCamelCase       			=[
        {'''score''': 0.0001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
        {'''score''': 0.0001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
    ]
    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(nested_simplify(UpperCamelCase__							,	decimals=4  )							,	UpperCamelCase__  )

    _UpperCamelCase       			=dqa_pipeline({'''image''': image, '''question''': question}							,	top_k=2  )
    self.assertEqual(nested_simplify(UpperCamelCase__							,	decimals=4  )							,	UpperCamelCase__  )

    # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
    # Empty answer probably
    _UpperCamelCase       			='''./tests/fixtures/tests_samples/COCO/000000039769.png'''
    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(UpperCamelCase__							,	[]  )

    # We can optionnally pass directly the words and bounding boxes
    _UpperCamelCase       			='''./tests/fixtures/tests_samples/COCO/000000039769.png'''
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	words=UpperCamelCase__							,	boxes=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(UpperCamelCase__							,	[]  )


  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def   UpperCamelCase__      (				self    :					str  )     ->						List[str]:
    _UpperCamelCase       			=pipeline(
        '''document-question-answering'''							,	model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''							,	revision='''9977165'''							,	)
    _UpperCamelCase       			=INVOICE_URL
    _UpperCamelCase       			='''What is the invoice number?'''

    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.9944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            {'''score''': 0.0009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
        ]							,	)

    _UpperCamelCase       			=dqa_pipeline({'''image''': image, '''question''': question}							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.9944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            {'''score''': 0.0009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
        ]							,	)

    _UpperCamelCase       			=dqa_pipeline(
        [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            [
                {'''score''': 0.9944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                {'''score''': 0.0009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            ],
        ]
        * 2							,	)


  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=pipeline(
        '''document-question-answering'''							,	model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''							,	revision='''9977165'''							,	max_seq_len=50							,	)
    _UpperCamelCase       			=INVOICE_URL
    _UpperCamelCase       			='''What is the invoice number?'''

    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
            {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
        ]							,	)

    _UpperCamelCase       			=dqa_pipeline({'''image''': image, '''question''': question}							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
            {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
        ]							,	)

    _UpperCamelCase       			=dqa_pipeline(
        [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            [
                {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            ]
        ]
        * 2							,	)


  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def   UpperCamelCase__      (				self    :					int  )     ->						Optional[int]:
    _UpperCamelCase       			=AutoTokenizer.from_pretrained(
        '''impira/layoutlm-document-qa'''							,	revision='''3dc6de3'''							,	add_prefix_space=UpperCamelCase__  )
    _UpperCamelCase       			=pipeline(
        '''document-question-answering'''							,	model='''impira/layoutlm-document-qa'''							,	tokenizer=UpperCamelCase__							,	revision='''3dc6de3'''							,	)
    _UpperCamelCase       			=INVOICE_URL
    _UpperCamelCase       			='''What is the invoice number?'''

    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
        ]							,	)

    _UpperCamelCase       			=dqa_pipeline({'''image''': image, '''question''': question}							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
        ]							,	)

    _UpperCamelCase       			=dqa_pipeline(
        [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            [
                {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
            ]
        ]
        * 2							,	)

    _UpperCamelCase       			=list(zip(*apply_tesseract(load_image(UpperCamelCase__  )							,	UpperCamelCase__							,	''''''  )  )  )

    # This model should also work if `image` is set to None
    _UpperCamelCase       			=dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
        ]							,	)


  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def   UpperCamelCase__      (				self    :					Any  )     ->						str:
    _UpperCamelCase       			=AutoTokenizer.from_pretrained(
        '''impira/layoutlm-document-qa'''							,	revision='''3dc6de3'''							,	add_prefix_space=UpperCamelCase__  )
    _UpperCamelCase       			=pipeline(
        '''document-question-answering'''							,	model='''impira/layoutlm-document-qa'''							,	tokenizer=UpperCamelCase__							,	revision='''3dc6de3'''							,	max_seq_len=50							,	)
    _UpperCamelCase       			=INVOICE_URL
    _UpperCamelCase       			='''What is the invoice number?'''

    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
        ]							,	)

    _UpperCamelCase       			=dqa_pipeline(
        [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            [
                {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            ]
        ]
        * 2							,	)

    _UpperCamelCase       			=list(zip(*apply_tesseract(load_image(UpperCamelCase__  )							,	UpperCamelCase__							,	''''''  )  )  )

    # This model should also work if `image` is set to None
    _UpperCamelCase       			=dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}							,	top_k=2  )
    self.assertEqual(
        nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[
            {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
            {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
        ]							,	)


  @slow
  @require_torch
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						List[Any]:
    _UpperCamelCase       			=pipeline(
        '''document-question-answering'''							,	model='''naver-clova-ix/donut-base-finetuned-docvqa'''							,	tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa'''  )							,	feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa'''							,	)

    _UpperCamelCase       			=INVOICE_URL
    _UpperCamelCase       			='''What is the invoice number?'''
    _UpperCamelCase       			=dqa_pipeline(image=UpperCamelCase__							,	question=UpperCamelCase__							,	top_k=2  )
    self.assertEqual(nested_simplify(UpperCamelCase__							,	decimals=4  )							,	[{'''answer''': '''us-001'''}]  )




  @require_tf
  @unittest.skip('''Document question answering not implemented in TF'''  )
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    pass













'''simple docstring'''






import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase						: Any		=   logging.get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=SwinConfig(
      embed_dim=192  ,      depths=(2, 2, 18, 2)  ,      num_heads=(6, 12, 24, 48)  ,      window_size=12  ,      out_features=['''stage2''', '''stage3''', '''stage4''']  ,      )

  _UpperCamelCase       			=DetaConfig(
      backbone_config=__SCREAMING_SNAKE_CASE  ,      num_queries=900  ,      encoder_ffn_dim=2048  ,      decoder_ffn_dim=2048  ,      num_feature_levels=5  ,      assign_first_stage=__SCREAMING_SNAKE_CASE  ,      with_box_refine=__SCREAMING_SNAKE_CASE  ,      two_stage=__SCREAMING_SNAKE_CASE  ,      )

  # set labels
  _UpperCamelCase       			='''huggingface/label-files'''
  if "o365" in model_name:
    _UpperCamelCase       			=366
    _UpperCamelCase       			='''object365-id2label.json'''
  else:
    _UpperCamelCase       			=91
    _UpperCamelCase       			='''coco-detection-id2label.json'''

  _UpperCamelCase       			=num_labels
  _UpperCamelCase       			=json.load(open(cached_download(hf_hub_url(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      repo_type='''dataset'''   )   )  ,      '''r'''   )   )
  _UpperCamelCase       			={int(__SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
  _UpperCamelCase       			=idalabel
  _UpperCamelCase       			={v: k for k, v in idalabel.items()}

  return config


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[]

  # stem
  # fmt: off
  rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')   )
  rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')   )
  rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')   )
  rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.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.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')   )

    if i < 3:
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')   )
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')   )

  rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')   )
  rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')   )
  rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')   )
  rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')   )
  rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')   )
  rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')   )

  # transformer encoder
  for i in range(config.encoder_layers   ):
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')   )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')   )

  # transformer decoder
  for i in range(config.decoder_layers   ):
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')   )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')   )

  # fmt: on

  return rename_keys


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=dct.pop(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=val


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
  for i in range(len(backbone_config.depths   )   ):
    _UpperCamelCase       			=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)
      _UpperCamelCase       			=state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''   )
      _UpperCamelCase       			=state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''   )
      # next, add query, keys and values (in that order) to the state dict
      _UpperCamelCase       			=in_proj_weight[:dim, :]
      _UpperCamelCase       			=in_proj_bias[: dim]
      _UpperCamelCase       			=in_proj_weight[
          dim : dim * 2, :
      ]
      _UpperCamelCase       			=in_proj_bias[
          dim : dim * 2
      ]
      _UpperCamelCase       			=in_proj_weight[
          -dim :, :
      ]
      _UpperCamelCase       			=in_proj_bias[-dim :]
      # fmt: on


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=config.d_model
  for i in range(config.decoder_layers   ):
    # read in weights + bias of input projection layer of self-attention
    _UpperCamelCase       			=state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
    _UpperCamelCase       			=state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''   )
    # next, add query, keys and values (in that order) to the state dict
    _UpperCamelCase       			=in_proj_weight[:hidden_size, :]
    _UpperCamelCase       			=in_proj_bias[:hidden_size]
    _UpperCamelCase       			=in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    _UpperCamelCase       			=in_proj_bias[hidden_size : hidden_size * 2]
    _UpperCamelCase       			=in_proj_weight[-hidden_size:, :]
    _UpperCamelCase       			=in_proj_bias[-hidden_size:]


def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _UpperCamelCase       			=Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,      stream=__SCREAMING_SNAKE_CASE   ).raw   )

  return im


@torch.no_grad()
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=get_deta_config(__SCREAMING_SNAKE_CASE   )

  # load original state dict
  if model_name == "deta-swin-large":
    _UpperCamelCase       			=hf_hub_download(repo_id='''nielsr/deta-checkpoints'''  ,      filename='''adet_swin_ft.pth'''   )
  elif model_name == "deta-swin-large-o365":
    _UpperCamelCase       			=hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''  ,      filename='''deta_swin_pt_o365.pth'''   )
  else:
    raise ValueError(f'''Model name {model_name} not supported'''   )

  _UpperCamelCase       			=torch.load(__SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''   )['''model''']

  # original state dict
  for name, param in state_dict.items():
    print(__SCREAMING_SNAKE_CASE  ,      param.shape   )

  # rename keys
  _UpperCamelCase       			=create_rename_keys(__SCREAMING_SNAKE_CASE   )
  for src, dest in rename_keys:
    rename_key(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  read_in_swin_q_k_v(__SCREAMING_SNAKE_CASE  ,      config.backbone_config   )
  read_in_decoder_q_k_v(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      _UpperCamelCase       			=state_dict.pop(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=val
    if "input_proj" in key:
      _UpperCamelCase       			=state_dict.pop(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _UpperCamelCase       			=state_dict.pop(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=val

    # finally, create HuggingFace model and load state dict
  _UpperCamelCase       			=DetaForObjectDetection(__SCREAMING_SNAKE_CASE   )
  model.load_state_dict(__SCREAMING_SNAKE_CASE   )
  model.eval()

  _UpperCamelCase       			='''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(__SCREAMING_SNAKE_CASE   )

  # load image processor
  _UpperCamelCase       			=DetaImageProcessor(format='''coco_detection'''   )

  # verify our conversion on image
  _UpperCamelCase       			=prepare_img()
  _UpperCamelCase       			=processor(images=__SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''   )
  _UpperCamelCase       			=encoding['''pixel_values''']
  _UpperCamelCase       			=model(pixel_values.to(__SCREAMING_SNAKE_CASE   )   )

  # verify logits
  print('''Logits:'''  ,      outputs.logits[0, :3, :3]   )
  print('''Boxes:'''  ,      outputs.pred_boxes[0, :3, :3]   )
  if model_name == "deta-swin-large":
    _UpperCamelCase       			=torch.tensor(
        [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]   )
    _UpperCamelCase       			=torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]   )
  elif model_name == "deta-swin-large-o365":
    _UpperCamelCase       			=torch.tensor(
        [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]   )
    _UpperCamelCase       			=torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]   )
  assert torch.allclose(outputs.logits[0, :3, :3]  ,      expected_logits.to(__SCREAMING_SNAKE_CASE   )  ,      atol=1E-4   )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,      expected_boxes.to(__SCREAMING_SNAKE_CASE   )  ,      atol=1E-4   )
  print('''Everything ok!'''   )

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''   )
    Path(__SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE   )
    model.save_pretrained(__SCREAMING_SNAKE_CASE   )
    processor.save_pretrained(__SCREAMING_SNAKE_CASE   )

  # Push to hub
  if push_to_hub:
    print('''Pushing model and processor to hub...'''   )
    model.push_to_hub(f'''jozhang97/{model_name}'''   )
    processor.push_to_hub(f'''jozhang97/{model_name}'''   )


if __name__ == "__main__":
      __lowerCamelCase						: str		=   argparse.ArgumentParser()

      parser.add_argument(
          '--model_name',
          type=str,
          default='deta-swin-large',
          choices=['deta-swin-large', 'deta-swin-large-o365'],
          help='Name of the 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.',
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )
      __lowerCamelCase						: List[str]		=   parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






