





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.generation import (
         FlaxForcedBOSTokenLogitsProcessor,
         FlaxForcedEOSTokenLogitsProcessor,
         FlaxLogitsProcessorList,
         FlaxMinLengthLogitsProcessor,
         FlaxTemperatureLogitsWarper,
         FlaxTopKLogitsWarper,
         FlaxTopPLogitsWarper,
     )

@require_flax
class      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=jnp.ones((batch_size, length)       ) / length
         return scores



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=None
         UpperCAmelCase_											=20

         UpperCAmelCase_											=self._get_uniform_logits(batch_size=2		,  length=_lowerCAmelCase       )

         # tweak scores to not be uniform anymore
         UpperCAmelCase_											=scores.at[1, 5].set((1 / length) + 0.1       )  # peak, 1st batch
         UpperCAmelCase_											=scores.at[1, 10].set((1 / length) - 0.4       )  # valley, 1st batch

         # compute softmax
         UpperCAmelCase_											=jax.nn.softmax(_lowerCAmelCase		,  axis=-1       )

         UpperCAmelCase_											=FlaxTemperatureLogitsWarper(temperature=0.5       )
         UpperCAmelCase_											=FlaxTemperatureLogitsWarper(temperature=1.3       )

         UpperCAmelCase_											=jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase		,  scores.copy()		,  cur_len=_lowerCAmelCase       )		,  axis=-1       )
         UpperCAmelCase_											=jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase		,  scores.copy()		,  cur_len=_lowerCAmelCase       )		,  axis=-1       )

         # uniform distribution stays uniform
         self.assertTrue(jnp.allclose(probs[0, :]		,  warped_prob_sharp[0, :]		,  atol=1e-3       )       )
         self.assertTrue(jnp.allclose(probs[0, :]		,  warped_prob_smooth[0, :]		,  atol=1e-3       )       )

         # sharp peaks get higher, valleys get lower
         self.assertLess(probs[1, :].max()		,  warped_prob_sharp[1, :].max()       )
         self.assertGreater(probs[1, :].min()		,  warped_prob_sharp[1, :].min()       )

         # smooth peaks get lower, valleys get higher
         self.assertGreater(probs[1, :].max()		,  warped_prob_smooth[1, :].max()       )
         self.assertLess(probs[1, :].min()		,  warped_prob_smooth[1, :].min()       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=None
         UpperCAmelCase_											=10
         UpperCAmelCase_											=2

         # create ramp distribution
         UpperCAmelCase_											=np.broadcast_to(np.arange(_lowerCAmelCase       )[None, :]		,  (batch_size, vocab_size)       ).copy()
         UpperCAmelCase_											=ramp_logits[1:, : vocab_size // 2] + vocab_size

         UpperCAmelCase_											=FlaxTopKLogitsWarper(3       )

         UpperCAmelCase_											=top_k_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )

         # check that correct tokens are filtered
         self.assertListEqual(jnp.isinf(scores[0]       ).tolist()		,  7 * [True] + 3 * [False]       )
         self.assertListEqual(jnp.isinf(scores[1]       ).tolist()		,  2 * [True] + 3 * [False] + 5 * [True]       )

         # check special case
         UpperCAmelCase_											=5
         UpperCAmelCase_											=FlaxTopKLogitsWarper(top_k=1		,  filter_value=0.0		,  min_tokens_to_keep=3       )

         UpperCAmelCase_											=np.broadcast_to(np.arange(_lowerCAmelCase       )[None, :]		,  (batch_size, length)       ).copy()
         UpperCAmelCase_											=top_k_warp_safety_check(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )

         # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
         self.assertListEqual((scores == 0.0).sum(axis=-1       ).tolist()		,  [2, 2]       )



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=None
         UpperCAmelCase_											=10
         UpperCAmelCase_											=2

         # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
         UpperCAmelCase_											=np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]       )       )

         UpperCAmelCase_											=FlaxTopPLogitsWarper(0.8       )
         UpperCAmelCase_											=np.exp(top_p_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )       )

         # dist should be filtered to keep min num values so that sum is >= top_p
         # exp (-inf) => 0
         UpperCAmelCase_											=np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]       )
         self.assertTrue(np.allclose(_lowerCAmelCase		,  _lowerCAmelCase		,  atol=1e-3       )       )

         # check edge cases with negative and extreme logits
         UpperCAmelCase_											=np.broadcast_to(np.arange(_lowerCAmelCase       )[None, :]		,  (batch_size, vocab_size)       ).copy() - (
             vocab_size // 2
         )

         # make ramp_logits more extreme
         UpperCAmelCase_											=ramp_logits[1] * 1_00.0

         # make sure at least 2 tokens are kept
         UpperCAmelCase_											=FlaxTopPLogitsWarper(0.9		,  min_tokens_to_keep=2		,  filter_value=0.0       )
         UpperCAmelCase_											=top_p_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )

         # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
         self.assertListEqual((filtered_dist != 0.0).sum(axis=-1       ).tolist()		,  [3, 2]       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=20
         UpperCAmelCase_											=4
         UpperCAmelCase_											=0

         UpperCAmelCase_											=FlaxMinLengthLogitsProcessor(min_length=10		,  eos_token_id=_lowerCAmelCase       )

         # check that min length is applied at length 5
         UpperCAmelCase_											=ids_tensor((batch_size, 20)		,  vocab_size=20       )
         UpperCAmelCase_											=5
         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=min_dist_processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()		,  4 * [-float("inf"       )]       )

         # check that min length is not applied anymore at length 15
         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=15
         UpperCAmelCase_											=min_dist_processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         self.assertFalse(jnp.isinf(_lowerCAmelCase       ).any()       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=20
         UpperCAmelCase_											=4
         UpperCAmelCase_											=0

         UpperCAmelCase_											=FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase       )

         # check that all scores are -inf except the bos_token_id score
         UpperCAmelCase_											=ids_tensor((batch_size, 1)		,  vocab_size=20       )
         UpperCAmelCase_											=1
         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=logits_processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]       ).all()       )
         self.assertListEqual(scores[:, bos_token_id].tolist()		,  4 * [0]       )  # score for bos_token_id shold be zero

         # check that bos_token_id is not forced if current length is greater than 1
         UpperCAmelCase_											=3
         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=logits_processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         self.assertFalse(jnp.isinf(_lowerCAmelCase       ).any()       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=20
         UpperCAmelCase_											=4
         UpperCAmelCase_											=0
         UpperCAmelCase_											=5

         UpperCAmelCase_											=FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,  eos_token_id=_lowerCAmelCase       )

         # check that all scores are -inf except the eos_token_id when max_length is reached
         UpperCAmelCase_											=ids_tensor((batch_size, 4)		,  vocab_size=20       )
         UpperCAmelCase_											=4
         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=logits_processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]       ).all()       )
         self.assertListEqual(scores[:, eos_token_id].tolist()		,  4 * [0]       )  # score for eos_token_id should be zero

         # check that eos_token_id is not forced if max_length is not reached
         UpperCAmelCase_											=3
         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=logits_processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         self.assertFalse(jnp.isinf(_lowerCAmelCase       ).any()       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=4
         UpperCAmelCase_											=10
         UpperCAmelCase_											=15
         UpperCAmelCase_											=2
         UpperCAmelCase_											=1
         UpperCAmelCase_											=15

         # dummy input_ids and scores
         UpperCAmelCase_											=ids_tensor((batch_size, sequence_length)		,  _lowerCAmelCase       )
         UpperCAmelCase_											=input_ids.copy()

         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=scores.copy()

         # instantiate all dist processors
         UpperCAmelCase_											=FlaxTemperatureLogitsWarper(temperature=0.5       )
         UpperCAmelCase_											=FlaxTopKLogitsWarper(3       )
         UpperCAmelCase_											=FlaxTopPLogitsWarper(0.8       )

         # instantiate all logits processors
         UpperCAmelCase_											=FlaxMinLengthLogitsProcessor(min_length=10		,  eos_token_id=_lowerCAmelCase       )
         UpperCAmelCase_											=FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase       )
         UpperCAmelCase_											=FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,  eos_token_id=_lowerCAmelCase       )

         UpperCAmelCase_											=10

         # no processor list
         UpperCAmelCase_											=temp_dist_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         UpperCAmelCase_											=top_k_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         UpperCAmelCase_											=top_p_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         UpperCAmelCase_											=min_dist_proc(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         UpperCAmelCase_											=bos_dist_proc(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
         UpperCAmelCase_											=eos_dist_proc(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )

         # with processor list
         UpperCAmelCase_											=FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
         UpperCAmelCase_											=processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )

         # scores should be equal
         self.assertTrue(jnp.allclose(_lowerCAmelCase		,  _lowerCAmelCase		,  atol=1e-3       )       )

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()		,  input_ids_comp.tolist()       )






      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=4
         UpperCAmelCase_											=10
         UpperCAmelCase_											=15
         UpperCAmelCase_											=2
         UpperCAmelCase_											=1
         UpperCAmelCase_											=15

         # dummy input_ids and scores
         UpperCAmelCase_											=ids_tensor((batch_size, sequence_length)		,  _lowerCAmelCase       )
         UpperCAmelCase_											=input_ids.copy()

         UpperCAmelCase_											=self._get_uniform_logits(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=scores.copy()

         # instantiate all dist processors
         UpperCAmelCase_											=FlaxTemperatureLogitsWarper(temperature=0.5       )
         UpperCAmelCase_											=FlaxTopKLogitsWarper(3       )
         UpperCAmelCase_											=FlaxTopPLogitsWarper(0.8       )

         # instantiate all logits processors
         UpperCAmelCase_											=FlaxMinLengthLogitsProcessor(min_length=10		,  eos_token_id=_lowerCAmelCase       )
         UpperCAmelCase_											=FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase       )
         UpperCAmelCase_											=FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,  eos_token_id=_lowerCAmelCase       )

         UpperCAmelCase_											=10

         # no processor list
         def run_no_processor_list(_lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Tuple       ):
            UpperCAmelCase_											=temp_dist_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
            UpperCAmelCase_											=top_k_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
            UpperCAmelCase_											=top_p_warp(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
            UpperCAmelCase_											=min_dist_proc(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
            UpperCAmelCase_											=bos_dist_proc(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
            UpperCAmelCase_											=eos_dist_proc(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
            return scores

         # with processor list
         def run_processor_list(_lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[Any]       ):
            UpperCAmelCase_											=FlaxLogitsProcessorList(
                [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
            UpperCAmelCase_											=processor(_lowerCAmelCase		,  _lowerCAmelCase		,  cur_len=_lowerCAmelCase       )
            return scores

         UpperCAmelCase_											=jax.jit(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.jit(_lowerCAmelCase       )

         UpperCAmelCase_											=jitted_run_no_processor_list(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=jitted_run_processor_list(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

         # scores should be equal
         self.assertTrue(jnp.allclose(_lowerCAmelCase		,  _lowerCAmelCase		,  atol=1e-3       )       )

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()		,  input_ids_comp.tolist()       )











import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowercase  :   str						      =[]
for i in range(6):
     # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
     rename_keys.append(
         (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
     )
     rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
     rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
     rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
     rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
     rename_keys.append(
         (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
     )
     rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
     rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
     rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
     # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
     )
     rename_keys.append(
         (
             f"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
             f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
         )
     )
     rename_keys.append(
         (
             f"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
             f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
         )
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

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

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

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=state_dict.pop(lowercase__  )
   UpperCAmelCase_											=val




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         UpperCAmelCase_											=key.replace("backbone.0.body"  ,    "backbone.conv_encoder.model"  )
         UpperCAmelCase_											=value
      else:
         UpperCAmelCase_											=value

   return new_state_dict




def     a__ (					lowercase__  ,    lowercase__=False  ):






   '''simple docstring'''






   UpperCAmelCase_											=""
   if is_panoptic:
      UpperCAmelCase_											="conditional_detr."

   # first: transformer encoder
   for i in range(6  ):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      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[:2_5_6, :]
      UpperCAmelCase_											=in_proj_bias[:2_5_6]
      UpperCAmelCase_											=in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase_											=in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase_											=in_proj_weight[-2_5_6:, :]
      UpperCAmelCase_											=in_proj_bias[-2_5_6:]




def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_											=Image.open(requests.get(lowercase__  ,    stream=lowercase__  ).raw  )

   return im




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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

   # load image processor
   UpperCAmelCase_											="coco_panoptic" if is_panoptic else "coco_detection"
   UpperCAmelCase_											=ConditionalDetrImageProcessor(format=lowercase__  )

   # prepare image
   UpperCAmelCase_											=prepare_img()
   UpperCAmelCase_											=image_processor(images=lowercase__  ,    return_tensors="pt"  )
   UpperCAmelCase_											=encoding["pixel_values"]

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

   # load original model from torch hub
   UpperCAmelCase_											=torch.hub.load("DeppMeng/ConditionalDETR"  ,    lowercase__  ,    pretrained=lowercase__  ).eval()
   UpperCAmelCase_											=conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         UpperCAmelCase_											="conditional_detr." + src
      rename_key(lowercase__  ,    lowercase__  ,    lowercase__  )
   UpperCAmelCase_											=rename_backbone_keys(lowercase__  )
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowercase__  ,    is_panoptic=lowercase__  )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   UpperCAmelCase_											="conditional_detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("conditional_detr"  )
             and not key.startswith("class_labels_classifier"  )
             and not key.startswith("bbox_predictor"  )
         ):
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
         elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
            continue
         else:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
      else:
         if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
    # finally, create HuggingFace model and load state dict
   UpperCAmelCase_											=ConditionalDetrForSegmentation(lowercase__  ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__  )
   model.load_state_dict(lowercase__  )
   model.eval()
   model.push_to_hub(repo_id=lowercase__  ,    organization="DepuMeng"  ,    commit_message="Add model"  )
   # verify our conversion
   UpperCAmelCase_											=conditional_detr(lowercase__  )
   UpperCAmelCase_											=model(lowercase__  )
   assert torch.allclose(outputs.logits  ,    original_outputs["pred_logits"]  ,    atol=1E-4  )
   assert torch.allclose(outputs.pred_boxes  ,    original_outputs["pred_boxes"]  ,    atol=1E-4  )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks  ,    original_outputs["pred_masks"]  ,    atol=1E-4  )

   # Save model and image processor
   logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'  )
   Path(lowercase__  ).mkdir(exist_ok=lowercase__  )
   model.save_pretrained(lowercase__  )
   image_processor.save_pretrained(lowercase__  )


if __name__ == "__main__":
     __lowercase  :   Dict						      =argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""",
         default="""conditional_detr_resnet50""",
         type=str,
         help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
     )
     __lowercase  :   Any						      =parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




