




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     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    jnp.ones((batch_size, length)							) / length
							return scores


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    20

							__lowerCamelCase		       =    self._get_uniform_logits(batch_size=2			,						length=UpperCamelCase__							)

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

							# compute softmax
							__lowerCamelCase		       =    jax.nn.softmax(UpperCamelCase__			,						axis=-1							)

							__lowerCamelCase		       =    FlaxTemperatureLogitsWarper(temperature=0.5							)
							__lowerCamelCase		       =    FlaxTemperatureLogitsWarper(temperature=1.3							)

							__lowerCamelCase		       =    jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase__			,						scores.copy()			,						cur_len=UpperCamelCase__							)			,						axis=-1							)
							__lowerCamelCase		       =    jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase__			,						scores.copy()			,						cur_len=UpperCamelCase__							)			,						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       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    10
							__lowerCamelCase		       =    2

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

							__lowerCamelCase		       =    FlaxTopKLogitsWarper(3							)

							__lowerCamelCase		       =    top_k_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)

							# 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
							__lowerCamelCase		       =    5
							__lowerCamelCase		       =    FlaxTopKLogitsWarper(top_k=1			,						filter_value=0.0			,						min_tokens_to_keep=3							)

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

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


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



							'''simple docstring'''
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    10
							__lowerCamelCase		       =    2

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

							__lowerCamelCase		       =    FlaxTopPLogitsWarper(0.8							)
							__lowerCamelCase		       =    np.exp(top_p_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)							)

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

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

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

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

							# 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       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    20
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    0

							__lowerCamelCase		       =    FlaxMinLengthLogitsProcessor(min_length=10			,						eos_token_id=UpperCamelCase__							)

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

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


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    20
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    0

							__lowerCamelCase		       =    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__							)

							# check that all scores are -inf except the bos_token_id score
							__lowerCamelCase		       =    ids_tensor((batch_size, 1)			,						vocab_size=20							)
							__lowerCamelCase		       =    1
							__lowerCamelCase		       =    self._get_uniform_logits(UpperCamelCase__			,						UpperCamelCase__							)
							__lowerCamelCase		       =    logits_processor(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							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
							__lowerCamelCase		       =    3
							__lowerCamelCase		       =    self._get_uniform_logits(UpperCamelCase__			,						UpperCamelCase__							)
							__lowerCamelCase		       =    logits_processor(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							self.assertFalse(jnp.isinf(UpperCamelCase__							).any()							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    20
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    5

							__lowerCamelCase		       =    FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__			,						eos_token_id=UpperCamelCase__							)

							# check that all scores are -inf except the eos_token_id when max_length is reached
							__lowerCamelCase		       =    ids_tensor((batch_size, 4)			,						vocab_size=20							)
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    self._get_uniform_logits(UpperCamelCase__			,						UpperCamelCase__							)
							__lowerCamelCase		       =    logits_processor(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							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
							__lowerCamelCase		       =    3
							__lowerCamelCase		       =    self._get_uniform_logits(UpperCamelCase__			,						UpperCamelCase__							)
							__lowerCamelCase		       =    logits_processor(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							self.assertFalse(jnp.isinf(UpperCamelCase__							).any()							)


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    10
							__lowerCamelCase		       =    15
							__lowerCamelCase		       =    2
							__lowerCamelCase		       =    1
							__lowerCamelCase		       =    15

							# dummy input_ids and scores
							__lowerCamelCase		       =    ids_tensor((batch_size, sequence_length)			,						UpperCamelCase__							)
							__lowerCamelCase		       =    input_ids.copy()

							__lowerCamelCase		       =    self._get_uniform_logits(UpperCamelCase__			,						UpperCamelCase__							)
							__lowerCamelCase		       =    scores.copy()

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

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

							__lowerCamelCase		       =    10

							# no processor list
							__lowerCamelCase		       =    temp_dist_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							__lowerCamelCase		       =    top_k_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							__lowerCamelCase		       =    top_p_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							__lowerCamelCase		       =    min_dist_proc(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							__lowerCamelCase		       =    bos_dist_proc(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
							__lowerCamelCase		       =    eos_dist_proc(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)

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

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

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






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



							'''simple docstring'''
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    10
							__lowerCamelCase		       =    15
							__lowerCamelCase		       =    2
							__lowerCamelCase		       =    1
							__lowerCamelCase		       =    15

							# dummy input_ids and scores
							__lowerCamelCase		       =    ids_tensor((batch_size, sequence_length)			,						UpperCamelCase__							)
							__lowerCamelCase		       =    input_ids.copy()

							__lowerCamelCase		       =    self._get_uniform_logits(UpperCamelCase__			,						UpperCamelCase__							)
							__lowerCamelCase		       =    scores.copy()

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

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

							__lowerCamelCase		       =    10

							# no processor list
							def run_no_processor_list(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    temp_dist_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
											__lowerCamelCase		       =    top_k_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
											__lowerCamelCase		       =    top_p_warp(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
											__lowerCamelCase		       =    min_dist_proc(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
											__lowerCamelCase		       =    bos_dist_proc(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
											__lowerCamelCase		       =    eos_dist_proc(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
											return scores

							# with processor list
							def run_processor_list(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    FlaxLogitsProcessorList(
											    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]							)
											__lowerCamelCase		       =    processor(UpperCamelCase__			,						UpperCamelCase__			,						cur_len=UpperCamelCase__							)
											return scores

							__lowerCamelCase		       =    jax.jit(UpperCamelCase__							)
							__lowerCamelCase		       =    jax.jit(UpperCamelCase__							)

							__lowerCamelCase		       =    jitted_run_no_processor_list(UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__							)
							__lowerCamelCase		       =    jitted_run_processor_list(UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__							)

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

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








import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       :      str  =   logging.get_logger(__name__)

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> Any:
       lowerCamelCase   :      Any			 =					[]
       for i in range(config.num_hidden_layers    ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')    )
              rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')    )
              rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')    )
              rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')    )
              rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')    )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ("cls_token", "deit.embeddings.cls_token"),
               ("dist_token", "deit.embeddings.distillation_token"),
               ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
               ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
               ("pos_embed", "deit.embeddings.position_embeddings"),
           ]    )

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                      ("pre_logits.fc.weight", "pooler.dense.weight"),
                      ("pre_logits.fc.bias", "pooler.dense.bias"),
                  ]    )

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              lowerCamelCase   :      Union[str, Any]			 =					[(pair[0], pair[1][4:]) if pair[1].startswith("deit"    ) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ("norm.weight", "deit.layernorm.weight"),
                      ("norm.bias", "deit.layernorm.bias"),
                      ("head.weight", "cls_classifier.weight"),
                      ("head.bias", "cls_classifier.bias"),
                      ("head_dist.weight", "distillation_classifier.weight"),
                      ("head_dist.bias", "distillation_classifier.bias"),
                  ]    )

       return rename_keys

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> str:
       for i in range(config.num_hidden_layers    ):
              if base_model:
                     lowerCamelCase   :      Optional[int]			 =					""
              else:
                     lowerCamelCase   :      List[str]			 =					"deit."
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowerCamelCase   :      List[str]			 =					state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''    )
              lowerCamelCase   :      Optional[int]			 =					state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''    )
              # next, add query, keys and values (in that order) to the state dict
              lowerCamelCase   :      List[Any]			 =					in_proj_weight[
                  : config.hidden_size, :
              ]
              lowerCamelCase   :      Any			 =					in_proj_bias[: config.hidden_size]
              lowerCamelCase   :      List[str]			 =					in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowerCamelCase   :      Optional[Any]			 =					in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowerCamelCase   :      List[str]			 =					in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowerCamelCase   :      List[Any]			 =					in_proj_bias[-config.hidden_size :]

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      List[str]			 =					dct.pop(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					val

def      A      (      )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      str			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       return im

@torch.no_grad()
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Union[str, Any]			 =					DeiTConfig()
       # all deit models have fine-tuned heads
       lowerCamelCase   :      Optional[int]			 =					False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       lowerCamelCase   :      Dict			 =					1000
       lowerCamelCase   :      Tuple			 =					"huggingface/label-files"
       lowerCamelCase   :      List[str]			 =					"imagenet-1k-id2label.json"
       lowerCamelCase   :      List[Any]			 =					json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset"    ) ,"r"    )    )
       lowerCamelCase   :      Optional[int]			 =					{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
       lowerCamelCase   :      Tuple			 =					idalabel
       lowerCamelCase   :      str			 =					{v: k for k, v in idalabel.items()}
       lowerCamelCase   :      Dict			 =					int(deit_name[-6:-4]    )
       lowerCamelCase   :      Optional[Any]			 =					int(deit_name[-3:]    )
       # size of the architecture
       if deit_name[9:].startswith("tiny"    ):
              lowerCamelCase   :      Optional[Any]			 =					192
              lowerCamelCase   :      List[str]			 =					768
              lowerCamelCase   :      Tuple			 =					12
              lowerCamelCase   :      Optional[Any]			 =					3
       elif deit_name[9:].startswith("small"    ):
              lowerCamelCase   :      str			 =					384
              lowerCamelCase   :      Optional[Any]			 =					1536
              lowerCamelCase   :      Dict			 =					12
              lowerCamelCase   :      Optional[int]			 =					6
       if deit_name[9:].startswith("base"    ):
              pass
       elif deit_name[4:].startswith("large"    ):
              lowerCamelCase   :      str			 =					1024
              lowerCamelCase   :      List[str]			 =					4096
              lowerCamelCase   :      Any			 =					24
              lowerCamelCase   :      Dict			 =					16

       # load original model from timm
       lowerCamelCase   :      List[Any]			 =					timm.create_model(_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowerCamelCase   :      Dict			 =					timm_model.state_dict()
       lowerCamelCase   :      Dict			 =					create_rename_keys(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       for src, dest in rename_keys:
              rename_key(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       read_in_q_k_v(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # load HuggingFace model
       lowerCamelCase   :      Optional[Any]			 =					DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE    ).eval()
       model.load_state_dict(_SCREAMING_SNAKE_CASE    )

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowerCamelCase   :      Any			 =					int(
           (256 / 224) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       lowerCamelCase   :      Union[str, Any]			 =					DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE ,crop_size=config.image_size    )
       lowerCamelCase   :      str			 =					image_processor(images=prepare_img() ,return_tensors="pt"    )
       lowerCamelCase   :      int			 =					encoding["pixel_values"]
       lowerCamelCase   :      Optional[Any]			 =					model(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					timm_model(_SCREAMING_SNAKE_CASE    )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(_SCREAMING_SNAKE_CASE ,outputs.logits ,atol=1e-3    )

       Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
       print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )
       print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
       image_processor.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--deit_name',
           default='vit_deit_base_distilled_patch16_224',
           type=str,
           help='Name of the DeiT timm model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )

       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






