




from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel



@require_tf
class       a					:

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  BlenderbotSmallConfig
     lowerCAmelCase    :	List[str]       		=  {}
     lowerCAmelCase    :	str       		=  'gelu'
     def __init__(     self       :      int ,   __snake_case       :      Dict ,   __snake_case       :      Dict=13 ,   __snake_case       :      Optional[Any]=7 ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Dict=False ,   __snake_case       :      List[str]=99 ,   __snake_case       :      str=32 ,   __snake_case       :      Dict=2 ,   __snake_case       :      List[str]=4 ,   __snake_case       :      Dict=37 ,   __snake_case       :      Optional[Any]=0.1 ,   __snake_case       :      Optional[int]=0.1 ,   __snake_case       :      Optional[int]=20 ,   __snake_case       :      List[Any]=2 ,   __snake_case       :      Tuple=1 ,   __snake_case       :      List[Any]=0 ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					seq_length
         UpperCAmelCase_										=					is_training
         UpperCAmelCase_										=					use_labels
         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					intermediate_size

         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					eos_token_id
         UpperCAmelCase_										=					pad_token_id
         UpperCAmelCase_										=					bos_token_id
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length - 1] ,   self.vocab_size    )
         UpperCAmelCase_										=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    ) ,   1    )
         UpperCAmelCase_										=					tf.concat([input_ids, eos_tensor] ,   axis=1    )

         UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size    )

         UpperCAmelCase_										=					self.config_cls(
             vocab_size=self.vocab_size ,   d_model=self.hidden_size ,   encoder_layers=self.num_hidden_layers ,   decoder_layers=self.num_hidden_layers ,   encoder_attention_heads=self.num_attention_heads ,   decoder_attention_heads=self.num_attention_heads ,   encoder_ffn_dim=self.intermediate_size ,   decoder_ffn_dim=self.intermediate_size ,   dropout=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   eos_token_ids=[2] ,   bos_token_id=self.bos_token_id ,   pad_token_id=self.pad_token_id ,   decoder_start_token_id=self.pad_token_id ,   **self.config_updates ,   )
         UpperCAmelCase_										=					prepare_blenderbot_small_inputs_dict(__snake_case ,   __snake_case ,   __snake_case    )
         return config, inputs_dict




     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      List[str] ,   __snake_case       :      List[Any]    ):
         UpperCAmelCase_										=					TFBlenderbotSmallModel(config=__snake_case    ).get_decoder()
         UpperCAmelCase_										=					inputs_dict['''input_ids''']

         UpperCAmelCase_										=					input_ids[:1, :]
         UpperCAmelCase_										=					inputs_dict['''attention_mask'''][:1, :]
         UpperCAmelCase_										=					inputs_dict['''head_mask''']
         UpperCAmelCase_										=					1

         # first forward pass
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   head_mask=__snake_case ,   use_cache=__snake_case    )

         UpperCAmelCase_			,							UpperCAmelCase_										=					outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         UpperCAmelCase_										=					ids_tensor((self.batch_size, 3) ,   config.vocab_size    )
         UpperCAmelCase_										=					tf.cast(ids_tensor((self.batch_size, 3) ,   2    ) ,   tf.inta    )

         # append to next input_ids and
         UpperCAmelCase_										=					tf.concat([input_ids, next_tokens] ,   axis=-1    )
         UpperCAmelCase_										=					tf.concat([attention_mask, next_attn_mask] ,   axis=-1    )

         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case    )[0]
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   past_key_values=__snake_case    )[0]

         self.parent.assertEqual(next_tokens.shape[1] ,   output_from_past.shape[1]    )

         # select random slice
         UpperCAmelCase_										=					int(ids_tensor((1,) ,   output_from_past.shape[-1]    )    )
         UpperCAmelCase_										=					output_from_no_past[:, -3:, random_slice_idx]
         UpperCAmelCase_										=					output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(__snake_case ,   __snake_case ,   rtol=1E-3    )




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						List[str]=None	,					__UpperCamelCase      :						int=None	,					__UpperCamelCase      :						Optional[int]=None	,					__UpperCamelCase      :						Any=None	,					__UpperCamelCase      :						int=None	,					)  ->							Optional[Any]:
    if attention_mask is None:
        UpperCAmelCase_										=					tf.cast(tf.math.not_equal(__UpperCamelCase	,					config.pad_token_id				)	,					tf.inta				)
    if decoder_attention_mask is None:
        UpperCAmelCase_										=					tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape	,					dtype=tf.inta				),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,					config.pad_token_id				)	,					tf.inta				),
            ]	,					axis=-1	,					)
    if head_mask is None:
        UpperCAmelCase_										=					tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
    if decoder_head_mask is None:
        UpperCAmelCase_										=					tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
    if cross_attn_head_mask is None:
        UpperCAmelCase_										=					tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }



@require_tf
class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[str]       		=  (
         (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
     )
     lowerCAmelCase    :	Union[str, Any]       		=  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
     lowerCAmelCase    :	List[str]       		=  (
         {
             'conversational': TFBlenderbotSmallForConditionalGeneration,
             'feature-extraction': TFBlenderbotSmallModel,
             'summarization': TFBlenderbotSmallForConditionalGeneration,
             'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
             'translation': TFBlenderbotSmallForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase    :	Tuple       		=  True
     lowerCAmelCase    :	Dict       		=  False
     lowerCAmelCase    :	List[Any]       		=  False
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					TFBlenderbotSmallModelTester(self    )
         UpperCAmelCase_										=					ConfigTester(self ,   config_class=__snake_case    )
     def   lowerCamelCase_       (     self       :      Any    ):
         self.config_tester.run_common_tests()




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__snake_case    )



@require_tokenizers
@require_tf
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  [
         'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
         '  i\'m going to throw up.\nand why is that?'
     ]
     lowerCAmelCase    :	Union[str, Any]       		=  'facebook/blenderbot_small-90M'
     @cached_property
     def   lowerCamelCase_       (     self       :      Dict    ):
         # use "old" tokenizer here because of bug when downloading new tokenizer
         return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''    )
     @cached_property
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
         return model




     @slow
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.tokenizer(self.src_text ,   return_tensors='''tf'''    )
         UpperCAmelCase_										=					self.model.generate(
             model_inputs.input_ids ,   attention_mask=model_inputs.attention_mask ,   num_beams=2 ,   use_cache=__snake_case ,   )
         UpperCAmelCase_										=					self.tokenizer.batch_decode(generated_ids.numpy() ,   skip_special_tokens=__snake_case    )[0]
         assert generated_words in (
             "i don't know. i just feel like i'm going to throw up. it's not fun.",
             "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
             "i'm not sure. i just feel like i've been in a bad situation.",
         )











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()
_lowerCamelCase      			=						logging.get_logger(__name__)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							Dict:
    # initialize config
    if "resnet-50" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-50'''				)
    elif "resnet-101" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-101'''				)
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101'''				)

    UpperCAmelCase_										=					DetrConfig(use_timm_backbone=__UpperCamelCase	,					backbone_config=__UpperCamelCase				)

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

    return config, is_panoptic




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

    # 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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
    UpperCAmelCase_										=					val




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




def  SCREAMING_SNAKE_CASE     (	)  ->							int:
    UpperCAmelCase_										=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase_										=					Image.open(requests.get(__UpperCamelCase	,					stream=__UpperCamelCase				).raw				)

    return im




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						Any=None	,					__UpperCamelCase      :						Optional[Any]=False				)  ->							Optional[Any]:
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_detr_config(__UpperCamelCase				)

    # load original model from torch hub
    UpperCAmelCase_										=					{
        '''detr-resnet-50''': '''detr_resnet50''',
        '''detr-resnet-101''': '''detr_resnet101''',
    }
    logger.info(f'Converting model {model_name}...'				)
    UpperCAmelCase_										=					torch.hub.load('''facebookresearch/detr'''	,					model_name_to_original_name[model_name]	,					pretrained=__UpperCamelCase				).eval()
    UpperCAmelCase_										=					detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(__UpperCamelCase				):
        if is_panoptic:
            UpperCAmelCase_										=					'''detr.''' + src
        rename_key(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
    # query, key and value matrices need special treatment
    read_in_q_k_v(__UpperCamelCase	,					is_panoptic=__UpperCamelCase				)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    UpperCAmelCase_										=					'''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'''				)
            ):
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
            elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
                continue
            else:
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
        else:
            if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val

    # finally, create HuggingFace model and load state dict
    UpperCAmelCase_										=					DetrForSegmentation(__UpperCamelCase				) if is_panoptic else DetrForObjectDetection(__UpperCamelCase				)
    model.load_state_dict(__UpperCamelCase				)
    model.eval()

    # verify our conversion on an image
    UpperCAmelCase_										=					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCAmelCase_										=					DetrImageProcessor(format=__UpperCamelCase				)

    UpperCAmelCase_										=					processor(images=prepare_img()	,					return_tensors='''pt'''				)
    UpperCAmelCase_										=					encoding['''pixel_values''']

    UpperCAmelCase_										=					detr(__UpperCamelCase				)
    UpperCAmelCase_										=					model(__UpperCamelCase				)

    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(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
        model.save_pretrained(__UpperCamelCase				)
        processor.save_pretrained(__UpperCamelCase				)

    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__":
 _lowerCamelCase      			=						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.')
 _lowerCamelCase      			=						parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





