


'''simple docstring'''

import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

_UpperCAmelCase				: List[str]							    =				yaml.safe_load(
    """\
name: \"\"
allow_empty: false
allow_empty_text: true
subsections:
  - name: \"Dataset Card for X\" # First-level markdown heading
    allow_empty: false
    allow_empty_text: true
    subsections:
      - name: \"Table of Contents\"
        allow_empty: false
        allow_empty_text: false
        subsections: null
      - name: \"Dataset Description\"
        allow_empty: false
        allow_empty_text: false
        subsections:
          - name: \"Dataset Summary\"
            allow_empty: false
            allow_empty_text: false
            subsections: null
          - name: \"Supported Tasks and Leaderboards\"
            allow_empty: true
            allow_empty_text: true
            subsections: null
          - name: Languages
            allow_empty: false
            allow_empty_text: true
            subsections: null
"""
)


_UpperCAmelCase				: Tuple							    =				{
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}


_UpperCAmelCase				: Dict							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""


_UpperCAmelCase				: List[str]							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

_UpperCAmelCase				: str							    =				{
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [
                                {
                                    """name""": """Extra Ignored Subsection""",
                                    """text""": """""",
                                    """is_empty_text""": True,
                                    """subsections""": [],
                                }
                            ],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}

_UpperCAmelCase				: List[Any]							    =				"""\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

_UpperCAmelCase				: Any							    =				(
    """The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."""
)

_UpperCAmelCase				: Dict							    =				"""\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

_UpperCAmelCase				: int							    =				(
    """The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."""
)

_UpperCAmelCase				: List[str]							    =				"""\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

_UpperCAmelCase				: int							    =				"""The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."""

_UpperCAmelCase				: Union[str, Any]							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
_UpperCAmelCase				: Any							    =				"""The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."""


_UpperCAmelCase				: List[str]							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
"""
_UpperCAmelCase				: Dict							    =				"""The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."""

_UpperCAmelCase				: int							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
"""

_UpperCAmelCase				: Dict							    =				"""The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."""


_UpperCAmelCase				: Union[str, Any]							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
"""

_UpperCAmelCase				: List[Any]							    =				"""The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."""

_UpperCAmelCase				: Optional[Any]							    =				"""\
---
language:
- zh
- en
---

## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
_UpperCAmelCase				: List[Any]							    =				"""The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."""

_UpperCAmelCase				: str							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
"""

_UpperCAmelCase				: List[str]							    =				"""The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."""

_UpperCAmelCase				: str							    =				"""\
---
language:
- zh
- en
---

# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

_UpperCAmelCase				: int							    =				"""The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."""

_UpperCAmelCase				: List[str]							    =				""""""

_UpperCAmelCase				: Dict							    =				"""The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."""

_UpperCAmelCase				: Tuple							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

_UpperCAmelCase				: str							    =				"""The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."""

@pytest.mark.parametrize(
    '''readme_md, expected_dict''',				[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ],				)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase				):
						assert ReadMe.from_string(lowerCamelCase,				lowerCamelCase				).to_dict() == expected_dict

@pytest.mark.parametrize(
    '''readme_md, expected_error''',				[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ],				)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase				):
						with pytest.raises(lowerCamelCase,				match=re.escape(expected_error.format(path='''root'''				)				)				):
												__lowerCAmelCase							       =				ReadMe.from_string(lowerCamelCase,				lowerCamelCase				)
												readme.validate()

@pytest.mark.parametrize(
    '''readme_md, expected_error''',				[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ],				)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase				):
						with pytest.raises(lowerCamelCase,				match=re.escape(expected_error.format(path='''root'''				)				)				):
												ReadMe.from_string(lowerCamelCase,				lowerCamelCase				)

@pytest.mark.parametrize(
    '''readme_md,''',				[
        (README_MULTIPLE_SAME_HEADING_1),
    ],				)
def 							__magic_name__(						lowerCamelCase				):
						ReadMe.from_string(lowerCamelCase,				lowerCamelCase,				suppress_parsing_errors=lowerCamelCase				)

@pytest.mark.parametrize(
    '''readme_md, expected_dict''',				[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ],				)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase				):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase							       =				Path(lowerCamelCase				) / '''README.md'''
												with open(lowerCamelCase,				'''w+'''				) as readme_file:
																		readme_file.write(lowerCamelCase				)
												__lowerCAmelCase							       =				ReadMe.from_readme(lowerCamelCase,				lowerCamelCase				).to_dict()
												assert out["name"] == path
												assert out["text"] == ""
												assert out["is_empty_text"]
												assert out["subsections"] == expected_dict["subsections"]

@pytest.mark.parametrize(
    '''readme_md, expected_error''',				[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ],				)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase				):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase							       =				Path(lowerCamelCase				) / '''README.md'''
												with open(lowerCamelCase,				'''w+'''				) as readme_file:
																		readme_file.write(lowerCamelCase				)
												__lowerCAmelCase							       =				expected_error.format(path=lowerCamelCase				)
												with pytest.raises(lowerCamelCase,				match=re.escape(lowerCamelCase				)				):
																		__lowerCAmelCase							       =				ReadMe.from_readme(lowerCamelCase,				lowerCamelCase				)
																		readme.validate()

@pytest.mark.parametrize(
    '''readme_md, expected_error''',				[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ],				)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase				):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase							       =				Path(lowerCamelCase				) / '''README.md'''
												with open(lowerCamelCase,				'''w+'''				) as readme_file:
																		readme_file.write(lowerCamelCase				)
												__lowerCAmelCase							       =				expected_error.format(path=lowerCamelCase				)
												with pytest.raises(lowerCamelCase,				match=re.escape(lowerCamelCase				)				):
																		ReadMe.from_readme(lowerCamelCase,				lowerCamelCase				)




@pytest.mark.parametrize(
    '''readme_md,''',				[
        (README_MULTIPLE_SAME_HEADING_1),
    ],				)
def 							__magic_name__(						lowerCamelCase				):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase							       =				Path(lowerCamelCase				) / '''README.md'''
												with open(lowerCamelCase,				'''w+'''				) as readme_file:
																		readme_file.write(lowerCamelCase				)
												ReadMe.from_readme(lowerCamelCase,				lowerCamelCase,				suppress_parsing_errors=lowerCamelCase				)





'''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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase				: Optional[int]							    =				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase				: 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}.cross_attn.out_proj.weight""",
	        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
	    )
	)
	rename_keys.append(
	    (
	        f"""transformer.decoder.layers.{i}.cross_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"""))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
	)

	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
	rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
						__lowerCAmelCase							       =				val

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		__lowerCAmelCase							       =				key.replace('''backbone.0.body''',				'''backbone.conv_encoder.model''')
																		__lowerCAmelCase							       =				value
												else:
																		__lowerCAmelCase							       =				value

						return new_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				''''''
						if is_panoptic:
												__lowerCAmelCase							       =				'''conditional_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)
												__lowerCAmelCase							       =				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""")
												__lowerCAmelCase							       =				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
												__lowerCAmelCase							       =				in_proj_weight[:2_5_6, :]
												__lowerCAmelCase							       =				in_proj_bias[:2_5_6]
												__lowerCAmelCase							       =				in_proj_weight[2_5_6:5_1_2, :]
												__lowerCAmelCase							       =				in_proj_bias[2_5_6:5_1_2]
												__lowerCAmelCase							       =				in_proj_weight[-2_5_6:, :]
												__lowerCAmelCase							       =				in_proj_bias[-2_5_6:]

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__lowerCAmelCase							       =				Image.open(requests.get(lowerCamelCase,				stream=lowerCamelCase).raw)

						return im

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				ConditionalDetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												__lowerCAmelCase							       =				'''resnet101'''
						if "dc5" in model_name:
												__lowerCAmelCase							       =				True
						__lowerCAmelCase							       =				'''panoptic''' in model_name
						if is_panoptic:
												__lowerCAmelCase							       =				2_5_0
						else:
												__lowerCAmelCase							       =				9_1
												__lowerCAmelCase							       =				'''huggingface/label-files'''
												__lowerCAmelCase							       =				'''coco-detection-id2label.json'''
												__lowerCAmelCase							       =				json.load(open(hf_hub_download(lowerCamelCase,				lowerCamelCase,				repo_type='''dataset'''),				'''r'''))
												__lowerCAmelCase							       =				{int(lowerCamelCase): v for k, v in idalabel.items()}
												__lowerCAmelCase							       =				idalabel
												__lowerCAmelCase							       =				{v: k for k, v in idalabel.items()}

						# load image processor
						__lowerCAmelCase							       =				'''coco_panoptic''' if is_panoptic else '''coco_detection'''
						__lowerCAmelCase							       =				ConditionalDetrImageProcessor(format=lowerCamelCase)

						# prepare image
						__lowerCAmelCase							       =				prepare_img()
						__lowerCAmelCase							       =				image_processor(images=lowerCamelCase,				return_tensors='''pt''')
						__lowerCAmelCase							       =				encoding['''pixel_values''']

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

						# load original model from torch hub
						__lowerCAmelCase							       =				torch.hub.load('''DeppMeng/ConditionalDETR''',				lowerCamelCase,				pretrained=lowerCamelCase).eval()
						__lowerCAmelCase							       =				conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		__lowerCAmelCase							       =				'''conditional_detr.''' + src
												rename_key(lowerCamelCase,				lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				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
						__lowerCAmelCase							       =				'''conditional_detr.model.''' if is_panoptic else '''model.'''
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith('''conditional_detr''')
																		    and not key.startswith('''class_labels_classifier''')
																		    and not key.startswith('''bbox_predictor''')
																		):
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
																		elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
																								continue
																		else:
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
												else:
																		if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
    # finally, create HuggingFace model and load state dict
						__lowerCAmelCase							       =				ConditionalDetrForSegmentation(lowerCamelCase) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase)
						model.load_state_dict(lowerCamelCase)
						model.eval()
						model.push_to_hub(repo_id=lowerCamelCase,				organization='''DepuMeng''',				commit_message='''Add model''')
						# verify our conversion
						__lowerCAmelCase							       =				conditional_detr(lowerCamelCase)
						__lowerCAmelCase							       =				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__":
	_UpperCAmelCase				: str							    =				argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name""",
	    default="""conditional_detr_resnet50""",
	    type=str,
	    help="""Name of the CONDITIONAL_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."""
	)
	_UpperCAmelCase				: Any							    =				parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

