




import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


_A			=						get_logger(__name__)


_A			=						R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'






class    UpperCAmelCase__	:



    """simple docstring"""






    @add_start_docstrings(A_   )
    def __call__(      self ,       A_ ,       A_   )  ->	jnp.ndarray:
         raise NotImplementedError(
             f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )






class    UpperCAmelCase__	:



    """simple docstring"""






    @add_start_docstrings(A_   )
    def __call__(      self ,       A_ ,       A_   )  ->	jnp.ndarray:
         raise NotImplementedError(
             f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    @add_start_docstrings(A_   )
    def __call__(      self ,       A_ ,       A_ ,       A_ ,       **A_   )  ->	jnp.ndarray:
         for processor in self:
              __UpperCamelCase   							=inspect.signature(processor.__call__   ).parameters
              if len(A_   ) > 3:
                   if not all(arg in kwargs for arg in list(function_args.keys()   )[2:]   ):
                        raise ValueError(
                            f'Make sure that all the required parameters: {list(function_args.keys()   )} for '
                            f'{processor.__class__} are passed to the logits processor.'   )
                   __UpperCamelCase   							=processor(A_ ,       A_ ,       A_ ,       **A_   )
              else:
                   __UpperCamelCase   							=processor(A_ ,       A_ ,       A_   )
         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	List[str]:
         if not isinstance(A_ ,       A_   ) or not (temperature > 0):
              raise ValueError(f'`temperature` has to be a strictly positive float, but is {temperature}'   )

         __UpperCamelCase   							=temperature






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         __UpperCamelCase   							=scores / self.temperature
         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ = -float('Inf'   ) ,       A_ = 1   )  ->	Dict:
         if not isinstance(A_ ,       A_   ) or (top_p < 0 or top_p > 1.0):
              raise ValueError(f'`top_p` has to be a float > 0 and < 1, but is {top_p}'   )
         if not isinstance(A_ ,       A_   ) or (min_tokens_to_keep < 1):
              raise ValueError(f'`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'   )

         __UpperCamelCase   							=top_p
         __UpperCamelCase   							=filter_value
         __UpperCamelCase   							=min_tokens_to_keep






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         __UpperCamelCase							,				__UpperCamelCase   							=lax.top_k(A_ ,       scores.shape[-1]   )

         __UpperCamelCase   							=jnp.full_like(A_ ,       self.filter_value   )
         __UpperCamelCase   							=jax.nn.softmax(A_ ,       axis=-1   ).cumsum(axis=-1   )
         __UpperCamelCase   							=cumulative_probs < self.top_p

         # include the token that is higher than top_p as well
         __UpperCamelCase   							=jnp.roll(A_ ,       1   )
         score_mask |= score_mask.at[:, 0].set(A_   )

         # min tokens to keep
         __UpperCamelCase   							=score_mask.at[:, : self.min_tokens_to_keep].set(A_   )

         __UpperCamelCase   							=jnp.where(A_ ,       A_ ,       A_   )
         __UpperCamelCase   							=jax.lax.sort_key_val(A_ ,       A_   )[-1]

         return next_scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ = -float('Inf'   ) ,       A_ = 1   )  ->	Dict:
         if not isinstance(A_ ,       A_   ) or top_k <= 0:
              raise ValueError(f'`top_k` has to be a strictly positive integer, but is {top_k}'   )

         __UpperCamelCase   							=max(A_ ,       A_   )
         __UpperCamelCase   							=filter_value






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         __UpperCamelCase							,				__UpperCamelCase   							=scores.shape
         __UpperCamelCase   							=jnp.full(batch_size * vocab_size ,       self.filter_value   )

         __UpperCamelCase   							=min(self.top_k ,       scores.shape[-1]   )  # Safety check
         __UpperCamelCase							,				__UpperCamelCase   							=lax.top_k(A_ ,       A_   )
         __UpperCamelCase   							=jnp.broadcast_to((jnp.arange(A_   ) * vocab_size)[:, None] ,       (batch_size, topk)   ).flatten()
         __UpperCamelCase   							=topk_scores.flatten()
         __UpperCamelCase   							=topk_indices.flatten() + shift

         __UpperCamelCase   							=next_scores_flat.at[topk_indices_flat].set(A_   )
         __UpperCamelCase   							=next_scores_flat.reshape(A_ ,       A_   )
         return next_scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	int:
         __UpperCamelCase   							=bos_token_id






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         __UpperCamelCase   							=jnp.full(scores.shape ,       -float('inf'   )   )

         __UpperCamelCase   							=1 - jnp.bool_(cur_len - 1   )

         __UpperCamelCase   							=jnp.where(A_ ,       new_scores.at[:, self.bos_token_id].set(0   ) ,       A_   )

         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_   )  ->	Optional[int]:
         __UpperCamelCase   							=max_length
         __UpperCamelCase   							=eos_token_id






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         __UpperCamelCase   							=jnp.full(scores.shape ,       -float('inf'   )   )

         __UpperCamelCase   							=1 - jnp.bool_(cur_len - self.max_length + 1   )

         __UpperCamelCase   							=jnp.where(A_ ,       new_scores.at[:, self.eos_token_id].set(0   ) ,       A_   )

         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_   )  ->	Union[str, Any]:
         if not isinstance(A_ ,       A_   ) or min_length < 0:
              raise ValueError(f'`min_length` has to be a positive integer, but is {min_length}'   )

         if not isinstance(A_ ,       A_   ) or eos_token_id < 0:
              raise ValueError(f'`eos_token_id` has to be a positive integer, but is {eos_token_id}'   )

         __UpperCamelCase   							=min_length
         __UpperCamelCase   							=eos_token_id






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         # create boolean flag to decide if min length penalty should be applied
         __UpperCamelCase   							=1 - jnp.clip(cur_len - self.min_length ,       0 ,       1   )

         __UpperCamelCase   							=jnp.where(A_ ,       scores.at[:, self.eos_token_id].set(-float('inf'   )   ) ,       A_   )

         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_   )  ->	Tuple:
         __UpperCamelCase   							=list(A_   )
         __UpperCamelCase   							=begin_index






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=1 - jnp.bool_(cur_len - self.begin_index   )

         __UpperCamelCase   							=jnp.where(A_ ,       scores.at[:, self.begin_suppress_tokens].set(-float('inf'   )   ) ,       A_   )

         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Optional[Any]:
         __UpperCamelCase   							=list(A_   )






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         __UpperCamelCase   							=scores.at[..., self.suppress_tokens].set(-float('inf'   )   )

         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	str:
         __UpperCamelCase   							=dict(A_   )
         # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
         # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
         # Indexes without forced tokens will have a negative value.
         __UpperCamelCase   							=jnp.ones((max(force_token_map.keys()   ) + 1) ,       dtype=jnp.intaa   ) * -1
         for index, token in force_token_map.items():
              if token is not None:
                   __UpperCamelCase   							=force_token_array.at[index].set(A_   )
         __UpperCamelCase   							=jnp.intaa(A_   )






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	jnp.ndarray:
         def _force_token(A_   ):
              __UpperCamelCase   							=scores.shape[0]
              __UpperCamelCase   							=self.force_token_array[generation_idx]

              __UpperCamelCase   							=jnp.ones_like(A_ ,       dtype=scores.dtype   ) * -float('inf'   )
              __UpperCamelCase   							=jnp.zeros((batch_size, 1) ,       dtype=scores.dtype   )
              __UpperCamelCase   							=lax.dynamic_update_slice(A_ ,       A_ ,       (0, current_token)   )
              return new_scores

         __UpperCamelCase   							=lax.cond(
             cur_len >= self.force_token_array.shape[0] ,       lambda: scores ,       lambda: lax.cond(
                 self.force_token_array[cur_len] >= 0 ,       lambda: _force_token(A_   ) ,       lambda: scores ,       ) ,       )
         return scores






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							=generate_config.eos_token_id
         __UpperCamelCase   							=generate_config.no_timestamps_token_id
         __UpperCamelCase   							=generate_config.no_timestamps_token_id + 1

         __UpperCamelCase   							=decoder_input_length + 1

         if generate_config.is_multilingual:
              # room for language token and task token
              self.begin_index += 2
         if hasattr(A_ ,       'max_initial_timestamp_index'   ):
              __UpperCamelCase   							=generate_config.max_initial_timestamp_index
         else:
              __UpperCamelCase   							=model_config.vocab_size
         if self.max_initial_timestamp_index is None:
              __UpperCamelCase   							=model_config.vocab_size






    def __call__(      self ,       A_ ,       A_ ,       A_   )  ->	int:
         # suppress <|notimestamps|> which is handled by without_timestamps
         __UpperCamelCase   							=scores.at[:, self.no_timestamps_token_id].set(-float('inf'   )   )

         def handle_pairs(A_ ,       A_   ):
              __UpperCamelCase   							=jnp.where((cur_len - self.begin_index) >= 1 ,       A_ ,       A_   )
              __UpperCamelCase   							=jnp.where(
                  input_ids_k[cur_len - 1] >= self.timestamp_begin ,       True and last_was_timestamp ,       A_ ,       )

              __UpperCamelCase   							=jnp.where((cur_len - self.begin_index) < 2 ,       A_ ,       A_   )
              __UpperCamelCase   							=jnp.where(
                  input_ids_k[cur_len - 2] >= self.timestamp_begin ,       A_ ,       A_ ,       )

              return jnp.where(
                  A_ ,       jnp.where(
                      penultimate_was_timestamp > 0 ,       scores_k.at[self.timestamp_begin :].set(-float('inf'   )   ) ,       scores_k.at[: self.eos_token_id].set(-float('inf'   )   ) ,       ) ,       A_ ,       )

         __UpperCamelCase   							=jax.vmap(A_   )(A_ ,       A_   )

         __UpperCamelCase   							=jnp.where(cur_len == self.begin_index ,       A_ ,       A_   )
         __UpperCamelCase   							=jnp.where(
             self.max_initial_timestamp_index is not None ,       True and apply_max_initial_timestamp ,       A_ ,       )

         __UpperCamelCase   							=self.timestamp_begin + self.max_initial_timestamp_index

         __UpperCamelCase   							=jnp.where(
             A_ ,       scores.at[:, last_allowed + 1 :].set(-float('inf'   )   ) ,       A_ ,       )

         # if sum of probability over timestamps is above any other token, sample timestamp
         __UpperCamelCase   							=jax.nn.log_softmax(A_ ,       axis=-1   )

         def handle_cumulative_probs(A_ ,       A_   ):
              __UpperCamelCase   							=jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] ,       axis=-1   )
              __UpperCamelCase   							=jnp.max(logprobs_k[: self.timestamp_begin]   )
              return jnp.where(
                  timestamp_logprob > max_text_token_logprob ,       scores_k.at[: self.timestamp_begin].set(-float('inf'   )   ) ,       A_ ,       )

         __UpperCamelCase   							=jax.vmap(A_   )(A_ ,       A_   )

         return scores









import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=SwinConfig(
         embed_dim=1_92      ,					depths=(2, 2, 18, 2)      ,					num_heads=(6, 12, 24, 48)      ,					window_size=12      ,					out_features=['stage2', 'stage3', 'stage4']      ,					)

     __UpperCamelCase   							=DetaConfig(
         backbone_config=SCREAMING_SNAKE_CASE__      ,					num_queries=9_00      ,					encoder_ffn_dim=20_48      ,					decoder_ffn_dim=20_48      ,					num_feature_levels=5      ,					assign_first_stage=SCREAMING_SNAKE_CASE__      ,					with_box_refine=SCREAMING_SNAKE_CASE__      ,					two_stage=SCREAMING_SNAKE_CASE__      ,					)

     # set labels
     __UpperCamelCase   							='huggingface/label-files'
     if "o365" in model_name:
          __UpperCamelCase   							=3_66
          __UpperCamelCase   							='object365-id2label.json'
     else:
          __UpperCamelCase   							=91
          __UpperCamelCase   							='coco-detection-id2label.json'

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

     return config

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=[]

     # stem
     # fmt: off
     rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')    )
     rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')    )
     rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')    )
     rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')    )
     # stages
     for i in range(len(config.backbone_config.depths    )    ):
          for j in range(config.backbone_config.depths[i]    ):
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')    )
               rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')    )

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

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

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

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

     # fmt: on

     return rename_keys

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : List[Any]    ):
     __UpperCamelCase   							=dct.pop(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=val

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : str    ):
     __UpperCamelCase   							=[int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
     for i in range(len(backbone_config.depths    )    ):
          __UpperCamelCase   							=num_features[i]
          for j in range(backbone_config.depths[i]    ):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               __UpperCamelCase   							=state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'    )
               __UpperCamelCase   							=state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'    )
               # next, add query, keys and values (in that order) to the state dict
               __UpperCamelCase   							=in_proj_weight[:dim, :]
               __UpperCamelCase   							=in_proj_bias[: dim]
               __UpperCamelCase   							=in_proj_weight[
                   dim : dim * 2, :
               ]
               __UpperCamelCase   							=in_proj_bias[
                   dim : dim * 2
               ]
               __UpperCamelCase   							=in_proj_weight[
                   -dim :, :
               ]
               __UpperCamelCase   							=in_proj_bias[-dim :]
               # fmt: on

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

def 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							='http://images.cocodataset.org/val2017/000000039769.jpg'
     __UpperCamelCase   							=Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,					stream=SCREAMING_SNAKE_CASE__    ).raw    )

     return im

@torch.no_grad()
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=get_deta_config(SCREAMING_SNAKE_CASE__    )

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

     __UpperCamelCase   							=torch.load(SCREAMING_SNAKE_CASE__      ,					map_location='cpu'    )['model']

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

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

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

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

     __UpperCamelCase   							='cuda' if torch.cuda.is_available() else 'cpu'
     model.to(SCREAMING_SNAKE_CASE__    )

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

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

     # verify logits
     print('Logits:'      ,					outputs.logits[0, :3, :3]    )
     print('Boxes:'      ,					outputs.pred_boxes[0, :3, :3]    )
     if model_name == "deta-swin-large":
          __UpperCamelCase   							=torch.tensor(
              [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]    )
          __UpperCamelCase   							=torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]    )
     elif model_name == "deta-swin-large-o365":
          __UpperCamelCase   							=torch.tensor(
              [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]    )
          __UpperCamelCase   							=torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]    )
     assert torch.allclose(outputs.logits[0, :3, :3]      ,					expected_logits.to(SCREAMING_SNAKE_CASE__    )      ,					atol=1E-4    )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,					expected_boxes.to(SCREAMING_SNAKE_CASE__    )      ,					atol=1E-4    )
     print('Everything ok!'    )

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

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


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       type=str,
       default='deta-swin-large',
       choices=['deta-swin-large', 'deta-swin-large-o365'],
       help='Name of the model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       help='Path to the folder to output PyTorch model.',
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )
   _A			=						parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





