



from __future__ import annotations

import unittest

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

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 TFLEDForConditionalGeneration, TFLEDModel







@require_tf
class 		snake_case						:



   '''simple docstring'''






   snake_case_					: Any          =		LEDConfig
   snake_case_					: int          =		{}
   snake_case_					: List[str]          =		"""gelu"""

   def __init__(					self							:			Tuple				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Optional[Any]=13				,		lowerCAmelCase							:			Any=7				,		lowerCAmelCase							:			List[Any]=True				,		lowerCAmelCase							:			Dict=False				,		lowerCAmelCase							:			Optional[int]=99				,		lowerCAmelCase							:			Any=32				,		lowerCAmelCase							:			List[str]=2				,		lowerCAmelCase							:			Any=4				,		lowerCAmelCase							:			Union[str, Any]=37				,		lowerCAmelCase							:			Tuple=0.1				,		lowerCAmelCase							:			Optional[Any]=0.1				,		lowerCAmelCase							:			Any=20				,		lowerCAmelCase							:			Tuple=2				,		lowerCAmelCase							:			Optional[int]=1				,		lowerCAmelCase							:			Union[str, Any]=0				,		lowerCAmelCase							:			int=4				,		)				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					parent
    _snake_case    :  List[Any]					=					batch_size
    _snake_case    :  List[str]					=					seq_length
    _snake_case    :  List[Any]					=					is_training
    _snake_case    :  Optional[int]					=					use_labels
    _snake_case    :  int					=					vocab_size
    _snake_case    :  Dict					=					hidden_size
    _snake_case    :  int					=					num_hidden_layers
    _snake_case    :  int					=					num_attention_heads
    _snake_case    :  Union[str, Any]					=					intermediate_size

    _snake_case    :  Any					=					hidden_dropout_prob
    _snake_case    :  Union[str, Any]					=					attention_probs_dropout_prob
    _snake_case    :  Optional[int]					=					max_position_embeddings
    _snake_case    :  Optional[int]					=					eos_token_id
    _snake_case    :  List[str]					=					pad_token_id
    _snake_case    :  List[Any]					=					bos_token_id
    _snake_case    :  int					=					attention_window

    # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
    # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
    # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
    # because its local attention only attends to `self.attention_window` and one before and one after
    _snake_case    :  List[Any]					=					self.attention_window + 2

    # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
    # the `test_attention_outputs` and `test_hidden_states_output` tests
    _snake_case    :  List[Any]					=					(
        self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
    )

   def      UpperCamelCase_  (					self							:			Any)				->				Any:

    """simple docstring"""







    _snake_case    :  Dict					=					ids_tensor([self.batch_size, self.seq_length - 1]				,		self.vocab_size)
    _snake_case    :  str					=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)				,		1)
    _snake_case    :  int					=					tf.concat([input_ids, eos_tensor]				,		axis=1)

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

    _snake_case    :  str					=					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				,		attention_window=self.attention_window				,		**self.config_updates				,		)
    _snake_case    :  Optional[Any]					=					prepare_led_inputs_dict(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					tf.concat(
        [tf.zeros_like(lowerCAmelCase)[:, :-1], tf.ones_like(lowerCAmelCase)[:, -1:]]				,		axis=-1				,		)
    _snake_case    :  List[Any]					=					global_attention_mask
    return config, inputs_dict


   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			Union[str, Any])				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					TFLEDModel(config=lowerCAmelCase).get_decoder()
    _snake_case    :  Union[str, Any]					=					inputs_dict["""input_ids"""]

    _snake_case    :  Dict					=					input_ids[:1, :]
    _snake_case    :  List[Any]					=					inputs_dict["""attention_mask"""][:1, :]
    _snake_case    :  str					=					1

    # first forward pass
    _snake_case    :  Any					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		use_cache=lowerCAmelCase)

    _snake_case	,    _snake_case    :  List[str]					=					outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    _snake_case    :  List[str]					=					ids_tensor((self.batch_size, 3)				,		config.vocab_size)
    _snake_case    :  Optional[Any]					=					tf.cast(ids_tensor((self.batch_size, 3)				,		2)				,		tf.inta)

    # append to next input_ids and
    _snake_case    :  Tuple					=					tf.concat([input_ids, next_tokens]				,		axis=-1)
    _snake_case    :  Optional[int]					=					tf.concat([attention_mask, next_attn_mask]				,		axis=-1)

    _snake_case    :  Dict					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase)[0]
    _snake_case    :  List[Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		past_key_values=lowerCAmelCase)[0]

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

    # select random slice
    _snake_case    :  List[Any]					=					int(ids_tensor((1,)				,		output_from_past.shape[-1]))
    _snake_case    :  Optional[Any]					=					output_from_no_past[:, -3:, random_slice_idx]
    _snake_case    :  Tuple					=					output_from_past[:, :, random_slice_idx]

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Tuple=None				,       SCREAMING_SNAKE_CASE__       :	List[str]=None				,       SCREAMING_SNAKE_CASE__       :	Tuple=None				,       SCREAMING_SNAKE_CASE__       :	Any=None				,       )  ->     List[str]:
 if attention_mask is None:
  _snake_case    :  Optional[Any]					=					tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__				,       config.pad_token_id				)				,       tf.inta				)
 if decoder_attention_mask is None:
  _snake_case    :  Dict					=					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:
  _snake_case    :  str					=					tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
 if decoder_head_mask is None:
  _snake_case    :  int					=					tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
     "decoder_input_ids": decoder_input_ids,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
 }







@require_tf
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Optional[int]          =		(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
   snake_case_					: str          =		(TFLEDForConditionalGeneration,) if is_tf_available() else ()
   snake_case_					: Optional[int]          =		(
       {
           """conversational""": TFLEDForConditionalGeneration,
           """feature-extraction""": TFLEDModel,
           """summarization""": TFLEDForConditionalGeneration,
           """text2text-generation""": TFLEDForConditionalGeneration,
           """translation""": TFLEDForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   snake_case_					: Optional[int]          =		True
   snake_case_					: str          =		False
   snake_case_					: Dict          =		False
   snake_case_					: str          =		False

   def      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					TFLEDModelTester(self)
    _snake_case    :  Union[str, Any]					=					ConfigTester(self				,		config_class=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[str]:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def      UpperCamelCase_  (					self							:			Any)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case	,    _snake_case    :  int					=					self.model_tester.prepare_config_and_inputs_for_common()
    _snake_case    :  Union[str, Any]					=					tf.zeros_like(inputs_dict["""attention_mask"""])
    _snake_case    :  Tuple					=					2
    _snake_case    :  Dict					=					tf.where(
        tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices				,		1				,		inputs_dict["""global_attention_mask"""]				,		)

    _snake_case    :  Tuple					=					True
    _snake_case    :  Optional[int]					=					self.model_tester.seq_length
    _snake_case    :  Union[str, Any]					=					self.model_tester.encoder_seq_length

    def check_decoder_attentions_output(lowerCAmelCase							:			Optional[Any]):
     _snake_case    :  List[Any]					=					outputs.decoder_attentions
     self.assertEqual(len(lowerCAmelCase)				,		self.model_tester.num_hidden_layers)
     self.assertListEqual(
         list(decoder_attentions[0].shape[-3:])				,		[self.model_tester.num_attention_heads, seq_length, seq_length]				,		)

    def check_encoder_attentions_output(lowerCAmelCase							:			Optional[Any]):
     _snake_case    :  int					=					[t.numpy() for t in outputs.encoder_attentions]
     _snake_case    :  List[str]					=					[t.numpy() for t in outputs.encoder_global_attentions]
     self.assertEqual(len(lowerCAmelCase)				,		self.model_tester.num_hidden_layers)
     self.assertEqual(len(lowerCAmelCase)				,		self.model_tester.num_hidden_layers)
     self.assertListEqual(
         list(attentions[0].shape[-3:])				,		[self.model_tester.num_attention_heads, seq_length, seq_length]				,		)
     self.assertListEqual(
         list(global_attentions[0].shape[-3:])				,		[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]				,		)

    for model_class in self.all_model_classes:
     _snake_case    :  Tuple					=					True
     _snake_case    :  List[str]					=					False
     _snake_case    :  Dict					=					False
     _snake_case    :  List[str]					=					model_class(lowerCAmelCase)
     _snake_case    :  Optional[Any]					=					model(self._prepare_for_class(lowerCAmelCase				,		lowerCAmelCase))
     _snake_case    :  Optional[int]					=					len(lowerCAmelCase)
     self.assertEqual(config.output_hidden_states				,		lowerCAmelCase)
     check_encoder_attentions_output(lowerCAmelCase)

     if self.is_encoder_decoder:
      _snake_case    :  List[str]					=					model_class(lowerCAmelCase)
      _snake_case    :  List[str]					=					model(self._prepare_for_class(lowerCAmelCase				,		lowerCAmelCase))
      self.assertEqual(config.output_hidden_states				,		lowerCAmelCase)
      check_decoder_attentions_output(lowerCAmelCase)

     # Check that output attentions can also be changed via the config
     del inputs_dict["output_attentions"]
     _snake_case    :  str					=					True
     _snake_case    :  str					=					model_class(lowerCAmelCase)
     _snake_case    :  Dict					=					model(self._prepare_for_class(lowerCAmelCase				,		lowerCAmelCase))
     self.assertEqual(config.output_hidden_states				,		lowerCAmelCase)
     check_encoder_attentions_output(lowerCAmelCase)

     # Check attention is always last and order is fine
     _snake_case    :  Any					=					True
     _snake_case    :  List[str]					=					True
     _snake_case    :  Union[str, Any]					=					model_class(lowerCAmelCase)
     _snake_case    :  Optional[Any]					=					model(self._prepare_for_class(lowerCAmelCase				,		lowerCAmelCase))

     self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)				,		len(lowerCAmelCase))
     self.assertEqual(model.config.output_hidden_states				,		lowerCAmelCase)
     check_encoder_attentions_output(lowerCAmelCase)

   @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing.""")
   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    pass


   def      UpperCamelCase_  (					self							:			Optional[int])				->				Optional[int]:

    """simple docstring"""







    pass



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				)  ->     List[str]:
 return tf.constant(SCREAMING_SNAKE_CASE__				,       dtype=tf.intaa				)


a__    =	1E-4







@slow
@require_tf
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			int)				->				Any:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""").led

    # change to intended input here
    _snake_case    :  List[Any]					=					_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
    _snake_case    :  Optional[Any]					=					_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
    _snake_case    :  Tuple					=					prepare_led_inputs_dict(model.config				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  int					=					model(**lowerCAmelCase)[0]
    _snake_case    :  Dict					=					(1, 1024, 768)
    self.assertEqual(output.shape				,		lowerCAmelCase)
    # change to expected output here
    _snake_case    :  List[Any]					=					tf.convert_to_tensor(
        [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]				,		)
    tf.debugging.assert_near(output[:, :3, :3]				,		lowerCAmelCase				,		atol=1E-3)


   def      UpperCamelCase_  (					self							:			Dict)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""")

    # change to intended input here
    _snake_case    :  Optional[int]					=					_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
    _snake_case    :  Dict					=					_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
    _snake_case    :  Dict					=					prepare_led_inputs_dict(model.config				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					model(**lowerCAmelCase)[0]
    _snake_case    :  Union[str, Any]					=					(1, 1024, model.config.vocab_size)
    self.assertEqual(output.shape				,		lowerCAmelCase)
    # change to expected output here
    _snake_case    :  str					=					tf.convert_to_tensor(
        [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]				,		)
    tf.debugging.assert_near(output[:, :3, :3]				,		lowerCAmelCase				,		atol=1E-3				,		rtol=1E-3)





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()
a__    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Optional[Any]:
 # initialize config
 if "resnet-50" in model_name:
  _snake_case    :  List[Any]					=					ResNetConfig.from_pretrained("""microsoft/resnet-50"""				)
 elif "resnet-101" in model_name:
  _snake_case    :  Any					=					ResNetConfig.from_pretrained("""microsoft/resnet-101"""				)
 else:
  raise ValueError("""Model name should include either resnet50 or resnet101"""				)

 _snake_case    :  Union[str, Any]					=					DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__				,       backbone_config=SCREAMING_SNAKE_CASE__				)

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

 return config, is_panoptic



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[int]:
 # here we list all keys to be renamed (original name on the left, our name on the right)
 _snake_case    :  Optional[Any]					=					[]

 # 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       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     Optional[Any]:
 _snake_case    :  str					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					val



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	str=False				)  ->     Union[str, Any]:
 _snake_case    :  Optional[Any]					=					""""""
 if is_panoptic:
  _snake_case    :  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)
  _snake_case    :  Any					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  List[str]					=					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
  _snake_case    :  Optional[Any]					=					in_proj_weight[:256, :]
  _snake_case    :  Any					=					in_proj_bias[:256]
  _snake_case    :  List[Any]					=					in_proj_weight[256:512, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Union[str, Any]					=					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
  _snake_case    :  Tuple					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  Dict					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  Optional[int]					=					in_proj_weight[:256, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[:256]
  _snake_case    :  Any					=					in_proj_weight[256:512, :]
  _snake_case    :  int					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Tuple					=					in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  _snake_case    :  List[str]					=					state_dict.pop(
      F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
  _snake_case    :  Optional[Any]					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) of cross-attention to the state dict
  _snake_case    :  int					=					in_proj_weight_cross_attn[:256, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[:256]
  _snake_case    :  int					=					in_proj_weight_cross_attn[256:512, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[256:512]
  _snake_case    :  Dict					=					in_proj_weight_cross_attn[-256:, :]
  _snake_case    :  Union[str, Any]					=					in_proj_bias_cross_attn[-256:]



def       lowercase       (   )  ->     Optional[Any]:
 _snake_case    :  Dict					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Dict					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)

 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Dict=None				,       SCREAMING_SNAKE_CASE__       :	Any=False				)  ->     List[Any]:
 _snake_case	,    _snake_case    :  Union[str, Any]					=					get_detr_config(SCREAMING_SNAKE_CASE__				)

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

    # finally, create HuggingFace model and load state dict
 _snake_case    :  int					=					DetrForSegmentation(SCREAMING_SNAKE_CASE__				) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__				)
 model.load_state_dict(SCREAMING_SNAKE_CASE__				)
 model.eval()

 # verify our conversion on an image
 _snake_case    :  List[Any]					=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
 _snake_case    :  int					=					DetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					processor(images=prepare_img()				,       return_tensors="""pt"""				)
 _snake_case    :  str					=					encoding["""pixel_values"""]

 _snake_case    :  Tuple					=					detr(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					model(SCREAMING_SNAKE_CASE__				)

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

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