


from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import numpy
 import tensorflow as tf

 from transformers.models.esm.modeling_tf_esm import (
     TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFEsmForMaskedLM,
     TFEsmForSequenceClassification,
     TFEsmForTokenClassification,
     TFEsmModel,
 )






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						parent
           _lowercase			:				Union[str, Any] 						=						13
           _lowercase			:				Optional[Any] 						=						7
           _lowercase			:				Optional[Any] 						=						True
           _lowercase			:				int 						=						True
           _lowercase			:				List[Any] 						=						True
           _lowercase			:				int 						=						99
           _lowercase			:				List[Any] 						=						32
           _lowercase			:				Optional[int] 						=						2
           _lowercase			:				Tuple 						=						4
           _lowercase			:				Any 						=						37
           _lowercase			:				List[str] 						=						'gelu'
           _lowercase			:				List[str] 						=						0.1
           _lowercase			:				int 						=						0.1
           _lowercase			:				Union[str, Any] 						=						5_12
           _lowercase			:				Any 						=						16
           _lowercase			:				Any 						=						2
           _lowercase			:				List[Any] 						=						0.0_2
           _lowercase			:				List[Any] 						=						3
           _lowercase			:				Optional[int] 						=						4
           _lowercase			:				Union[str, Any] 						=						None
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

           _lowercase			:				List[Any] 						=						None
           if self.use_input_mask:
               _lowercase			:				Dict 						=						random_attention_mask([self.batch_size, self.seq_length])

           _lowercase			:				str 						=						None
           _lowercase			:				Dict 						=						None
           _lowercase			:				List[str] 						=						None
           if self.use_labels:
               _lowercase			:				List[str] 						=						ids_tensor([self.batch_size], self.type_sequence_label_size)
               _lowercase			:				Dict 						=						ids_tensor([self.batch_size, self.seq_length], self.num_labels)
               _lowercase			:				Tuple 						=						ids_tensor([self.batch_size], self.num_choices)

           _lowercase			:				Tuple 						=						EsmConfig(
               vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, pad_token_id=1, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, )

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           (
               (
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							
           )			:				Union[str, Any] 						=						self.prepare_config_and_inputs()

           _lowercase			:				Optional[Any] 						=						True
           _lowercase			:				Optional[Any] 						=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _lowercase			:				str 						=						ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

           return (
               config,
               input_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Dict 						=						TFEsmModel(config=lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						{'input_ids': input_ids, 'attention_mask': input_mask}
           _lowercase			:				Tuple 						=						model(lowerCamelCase)

           _lowercase			:				List[Any] 						=						[input_ids, input_mask]
           _lowercase			:				Any 						=						model(lowerCamelCase)

           _lowercase			:				Optional[int] 						=						model(lowerCamelCase)

           self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, )			->  str:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						True

           _lowercase			:				int 						=						TFEsmModel(config=lowerCamelCase)
           _lowercase			:				Any 						=						{
               'input_ids': input_ids,
               'attention_mask': input_mask,
               'encoder_hidden_states': encoder_hidden_states,
               'encoder_attention_mask': encoder_attention_mask,
           }
           _lowercase			:				Optional[int] 						=						model(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						[input_ids, input_mask]
           _lowercase			:				Any 						=						model(lowerCamelCase, encoder_hidden_states=lowerCamelCase)

           # Also check the case where encoder outputs are not passed
           _lowercase			:				Optional[int] 						=						model(lowerCamelCase, attention_mask=lowerCamelCase)

           self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           _lowercase			:				Tuple 						=						TFEsmForMaskedLM(config=lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						model([input_ids, input_mask])
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						self.num_labels
           _lowercase			:				Tuple 						=						TFEsmForTokenClassification(config=lowerCamelCase)
           _lowercase			:				str 						=						{'input_ids': input_ids, 'attention_mask': input_mask}
           _lowercase			:				Union[str, Any] 						=						model(lowerCamelCase)
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))




       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				int 						=						self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							
           )			:				Optional[int] 						=						config_and_inputs
           _lowercase			:				Optional[int] 						=						{'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_tf
class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      List[Any]       					=     (
           (
               TFEsmModel,
               TFEsmForMaskedLM,
               TFEsmForSequenceClassification,
               TFEsmForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       lowercase_					:      Any       					=     (
           {
               """feature-extraction""": TFEsmModel,
               """fill-mask""": TFEsmForMaskedLM,
               """text-classification""": TFEsmForSequenceClassification,
               """token-classification""": TFEsmForTokenClassification,
               """zero-shot""": TFEsmForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowercase_					:      Any       					=     False
       lowercase_					:      Optional[int]       					=     False
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Tuple 						=						TFEsmModelTester(self)
           _lowercase			:				List[Any] 						=						ConfigTester(self, config_class=lowerCamelCase, hidden_size=37)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				str 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase)
       @slow
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase			:				Any 						=						TFEsmModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)
       @unittest.skip('Protein models do not support embedding resizing.')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('Protein models do not support embedding resizing.')
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           pass




       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Dict 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				List[str] 						=						model_class(lowerCamelCase)
               assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
               if model_class is TFEsmForMaskedLM:
                   # Output embedding test differs from the main test because they're a matrix, not a layer
                   _lowercase			:				str 						=						model.get_bias()
                   assert isinstance(lowerCamelCase, lowerCamelCase)
                   for k, v in name.items():
                       assert isinstance(lowerCamelCase, tf.Variable)
               else:
                   _lowercase			:				List[str] 						=						model.get_output_embeddings()
                   assert x is None
                   _lowercase			:				List[str] 						=						model.get_bias()
                   assert name is None






@require_tf
class _lowerCamelCase(		unittest.TestCase  ):
       @slow
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				str 						=						TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D')

           _lowercase			:				List[Any] 						=						tf.constant([[0, 1, 2, 3, 4, 5]])
           _lowercase			:				List[Any] 						=						model(lowerCamelCase)[0]
           _lowercase			:				Dict 						=						[1, 6, 33]
           self.assertEqual(list(output.numpy().shape), lowerCamelCase)
           # compare the actual values for a slice.
           _lowercase			:				Any 						=						tf.constant(
               [
                   [
                       [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
                       [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
                       [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
                   ]
               ])
           self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-2))




       @slow
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Dict 						=						TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D')

           _lowercase			:				int 						=						tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
           _lowercase			:				Optional[int] 						=						model(lowerCamelCase)[0]
           # compare the actual values for a slice.
           _lowercase			:				int 						=						tf.constant(
               [
                   [
                       [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
                       [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
                       [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
                   ]
               ])
           self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-4))






import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    # initialize config
    if "resnet-50" in model_name:
        _lowercase			:				Union[str, Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-50'					)
    elif "resnet-101" in model_name:
        _lowercase			:				Optional[Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-101'					)
    else:
        raise ValueError('Model name should include either resnet50 or resnet101'					)

    _lowercase			:				Tuple 						=						DetrConfig(use_timm_backbone=lowerCamelCase_				,					backbone_config=lowerCamelCase_					)

    # set label attributes
    _lowercase			:				Any 						=						'panoptic' in model_name
    if is_panoptic:
        _lowercase			:				List[Any] 						=						250
    else:
        _lowercase			:				str 						=						91
        _lowercase			:				List[Any] 						=						'huggingface/label-files'
        _lowercase			:				Any 						=						'coco-detection-id2label.json'
        _lowercase			:				Tuple 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
        _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
        _lowercase			:				int 						=						idalabel
        _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    return config, is_panoptic



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    _lowercase			:				List[str] 						=						[]

    # stem
    # fmt: off
    rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')					)
    rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')					)
    rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')					)
    rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')					)
    rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')					)
    # stages
    for stage_idx in range(len(config.backbone_config.depths					)					):
        for layer_idx in range(config.backbone_config.depths[stage_idx]					):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                    )					)
            # 3 convs
            for i in range(3					):
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                    )					)
    # fmt: on

    for i in range(config.encoder_layers					):
        # 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'),
        ]					)

    return rename_keys



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				str 						=						state_dict.pop(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						val



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



def 							UpperCamelCase_( )       ->		List[Any]:
    _lowercase			:				Dict 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				str 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)

    return im



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=False					)       ->		List[Any]:
    _lowercase						,							_lowercase			:				int 						=						get_detr_config(lowerCamelCase_					)

    # load original model from torch hub
    _lowercase			:				int 						=						{
        'detr-resnet-50': 'detr_resnet50',
        'detr-resnet-101': 'detr_resnet101',
    }
    logger.info(F'''Converting model {model_name}...'''					)
    _lowercase			:				Optional[Any] 						=						torch.hub.load('facebookresearch/detr'				,					model_name_to_original_name[model_name]				,					pretrained=lowerCamelCase_					).eval()
    _lowercase			:				str 						=						detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(lowerCamelCase_					):
        if is_panoptic:
            _lowercase			:				str 						=						'detr.' + src
        rename_key(lowerCamelCase_				,					lowerCamelCase_				,					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
    _lowercase			:				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'					)
            ):
                _lowercase			:				Tuple 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				int 						=						val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				Optional[Any] 						=						val
            elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
                continue
            else:
                _lowercase			:				Optional[Any] 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				Union[str, Any] 						=						val
        else:
            if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
                _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				List[str] 						=						val

    # finally, create HuggingFace model and load state dict
    _lowercase			:				Optional[Any] 						=						DetrForSegmentation(lowerCamelCase_					) if is_panoptic else DetrForObjectDetection(lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    # verify our conversion on an image
    _lowercase			:				str 						=						'coco_panoptic' if is_panoptic else 'coco_detection'
    _lowercase			:				Optional[int] 						=						DetrImageProcessor(format=lowerCamelCase_					)

    _lowercase			:				str 						=						processor(images=prepare_img()				,					return_tensors='pt'					)
    _lowercase			:				Tuple 						=						encoding['pixel_values']

    _lowercase			:				int 						=						detr(lowerCamelCase_					)
    _lowercase			:				Tuple 						=						model(lowerCamelCase_					)

    assert torch.allclose(outputs.logits				,					original_outputs['pred_logits']				,					atol=1e-3					)
    assert torch.allclose(outputs.pred_boxes				,					original_outputs['pred_boxes']				,					atol=1e-3					)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks				,					original_outputs['pred_masks']				,					atol=1e-4					)
    print('Looks ok!'					)

    if pytorch_dump_folder_path is not None:
        # 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_					)
        processor.save_pretrained(lowerCamelCase_					)

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info('Uploading PyTorch model and image processor to the hub...'					)
        model.push_to_hub(F'''nielsr/{model_name}'''					)
        processor.push_to_hub(F'''nielsr/{model_name}'''					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: List[Any]				   =					argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     default="detr-resnet-50",
     type=str,
     choices=["detr-resnet-50", "detr-resnet-101"],
     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."
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
 SCREAMING_SNAKE_CASE			: Tuple				   =					parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


