





'''simple docstring'''

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    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :int						,       lowercase_      :int							)->							List[str]:
     A__				=			jnp.ones((batch_size, length)							) / length
     return scores
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Any:
     A__				=			None
     A__				=			20

     A__				=			self._get_uniform_logits(batch_size=2						,       length=lowercase_							)

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

     # compute softmax
     A__				=			jax.nn.softmax(lowercase_						,       axis=-1							)

     A__				=			FlaxTemperatureLogitsWarper(temperature=0.5							)
     A__				=			FlaxTemperatureLogitsWarper(temperature=1.3							)

     A__				=			jax.nn.softmax(temp_dist_warper_sharper(lowercase_						,       scores.copy()						,       cur_len=lowercase_							)						,       axis=-1							)
     A__				=			jax.nn.softmax(temp_dist_warper_smoother(lowercase_						,       scores.copy()						,       cur_len=lowercase_							)						,       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      UpperCAmelCase_							(				self      :List[str]							)->							str:
     A__				=			None
     A__				=			10
     A__				=			2

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

     A__				=			FlaxTopKLogitsWarper(3							)

     A__				=			top_k_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)

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

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

     # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
     self.assertListEqual((scores == 0.0).sum(axis=-1							).tolist()						,       [2, 2]							)
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     A__				=			None
     A__				=			10
     A__				=			2

     # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
     A__				=			np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]							)							)

     A__				=			FlaxTopPLogitsWarper(0.8							)
     A__				=			np.exp(top_p_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)							)

     # dist should be filtered to keep min num values so that sum is >= top_p
     # exp (-inf) => 0
     A__				=			np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]							)
     self.assertTrue(np.allclose(lowercase_						,       lowercase_						,       atol=1E-3							)							)

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

     # make ramp_logits more extreme
     A__				=			ramp_logits[1] * 1_0_0.0

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

     # 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      UpperCAmelCase_							(				self      :Dict							)->							Optional[Any]:
     A__				=			20
     A__				=			4
     A__				=			0

     A__				=			FlaxMinLengthLogitsProcessor(min_length=10						,       eos_token_id=lowercase_							)

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

     # check that min length is not applied anymore at length 15
     A__				=			self._get_uniform_logits(lowercase_						,       lowercase_							)
     A__				=			15
     A__				=			min_dist_processor(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     self.assertFalse(jnp.isinf(lowercase_							).any()							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Union[str, Any]:
     A__				=			20
     A__				=			4
     A__				=			0

     A__				=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowercase_							)

     # check that all scores are -inf except the bos_token_id score
     A__				=			ids_tensor((batch_size, 1)						,       vocab_size=20							)
     A__				=			1
     A__				=			self._get_uniform_logits(lowercase_						,       lowercase_							)
     A__				=			logits_processor(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     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
     A__				=			3
     A__				=			self._get_uniform_logits(lowercase_						,       lowercase_							)
     A__				=			logits_processor(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     self.assertFalse(jnp.isinf(lowercase_							).any()							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Optional[int]:
     A__				=			20
     A__				=			4
     A__				=			0
     A__				=			5

     A__				=			FlaxForcedEOSTokenLogitsProcessor(max_length=lowercase_						,       eos_token_id=lowercase_							)

     # check that all scores are -inf except the eos_token_id when max_length is reached
     A__				=			ids_tensor((batch_size, 4)						,       vocab_size=20							)
     A__				=			4
     A__				=			self._get_uniform_logits(lowercase_						,       lowercase_							)
     A__				=			logits_processor(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     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
     A__				=			3
     A__				=			self._get_uniform_logits(lowercase_						,       lowercase_							)
     A__				=			logits_processor(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     self.assertFalse(jnp.isinf(lowercase_							).any()							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							str:
     A__				=			4
     A__				=			10
     A__				=			15
     A__				=			2
     A__				=			1
     A__				=			15

     # dummy input_ids and scores
     A__				=			ids_tensor((batch_size, sequence_length)						,       lowercase_							)
     A__				=			input_ids.copy()

     A__				=			self._get_uniform_logits(lowercase_						,       lowercase_							)
     A__				=			scores.copy()

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

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

     A__				=			10

     # no processor list
     A__				=			temp_dist_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     A__				=			top_k_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     A__				=			top_p_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     A__				=			min_dist_proc(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     A__				=			bos_dist_proc(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
     A__				=			eos_dist_proc(lowercase_						,       lowercase_						,       cur_len=lowercase_							)

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

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

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



 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Optional[Any]:
     A__				=			4
     A__				=			10
     A__				=			15
     A__				=			2
     A__				=			1
     A__				=			15

     # dummy input_ids and scores
     A__				=			ids_tensor((batch_size, sequence_length)						,       lowercase_							)
     A__				=			input_ids.copy()

     A__				=			self._get_uniform_logits(lowercase_						,       lowercase_							)
     A__				=			scores.copy()

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

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

     A__				=			10

     # no processor list
     def run_no_processor_list(lowercase_      :List[Any]						,       lowercase_      :Optional[int]						,       lowercase_      :List[Any]							):
         A__				=			temp_dist_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
         A__				=			top_k_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
         A__				=			top_p_warp(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
         A__				=			min_dist_proc(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
         A__				=			bos_dist_proc(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
         A__				=			eos_dist_proc(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
         return scores

     # with processor list
     def run_processor_list(lowercase_      :Any						,       lowercase_      :Dict						,       lowercase_      :int							):
         A__				=			FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]							)
         A__				=			processor(lowercase_						,       lowercase_						,       cur_len=lowercase_							)
         return scores

     A__				=			jax.jit(lowercase_							)
     A__				=			jax.jit(lowercase_							)

     A__				=			jitted_run_no_processor_list(lowercase_						,       lowercase_						,       lowercase_							)
     A__				=			jitted_run_processor_list(lowercase_						,       lowercase_						,       lowercase_							)

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

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










'''simple docstring'''

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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:	List[Any]			=logging.get_logger(__name__)





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Union[str, Any]=False       ):
    A__				=			[]

    # fmt: off
    # stem:
    rename_keys.append(("cls_token", "vit.embeddings.cls_token")       )
    rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")       )

    rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")       )
    rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")       )

    # backbone
    rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")       )
    rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")       )
    rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")       )

    for stage_idx in range(len(config.backbone_config.depths       )       ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")       )

        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")       )
        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")       )
        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")       )

    # transformer encoder
    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"vit.encoder.layer.{i}.layernorm_before.weight")       )
        rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")       )
        rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")       )
        rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")       )
        rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")       )
        rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")       )
        rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")       )
        rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")       )
        rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")       )
        rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")       )

    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 "vit" from all keys that start with "vit"
        A__				=			[(pair[0], pair[1][4:]) if pair[1].startswith("vit"       ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("norm.weight", "vit.layernorm.weight"),
                ("norm.bias", "vit.layernorm.bias"),
                ("head.weight", "classifier.weight"),
                ("head.bias", "classifier.bias"),
            ]       )
    # fmt: on

    return rename_keys





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





def 						UpperCamelCase				(							_lowerCamelCase					:					int       ):
    A__				=			["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,       _lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			dct.pop(_lowerCamelCase       )
    A__				=			val





def 						UpperCamelCase				(							):
    A__				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
    A__				=			Image.open(requests.get(_lowerCamelCase						,       stream=_lowerCamelCase       ).raw       )
    return im





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Optional[Any]=False       ):
    A__				=			BitConfig(
        global_padding="same"						,       layer_type="bottleneck"						,       depths=(3, 4, 9)						,       out_features=["stage3"]						,       embedding_dynamic_padding=_lowerCamelCase						,       )
    A__				=			ViTHybridConfig(backbone_config=_lowerCamelCase						,       image_size=3_84						,       num_labels=10_00       )
    A__				=			False

    # load original model from timm
    A__				=			timm.create_model(_lowerCamelCase						,       pretrained=_lowerCamelCase       )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    A__				=			timm_model.state_dict()
    if base_model:
        remove_classification_head_(_lowerCamelCase       )
    A__				=			create_rename_keys(_lowerCamelCase						,       _lowerCamelCase       )
    for src, dest in rename_keys:
        rename_key(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    read_in_q_k_v(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			"huggingface/label-files"
    A__				=			"imagenet-1k-id2label.json"
    A__				=			json.load(open(hf_hub_download(_lowerCamelCase						,       _lowerCamelCase						,       repo_type="dataset"       )						,       "r"       )       )
    A__				=			{int(_lowerCamelCase       ): v for k, v in idalabel.items()}
    A__				=			idalabel
    A__				=			{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        A__				=			ViTHybridModel(_lowerCamelCase       ).eval()
    else:
        A__				=			ViTHybridForImageClassification(_lowerCamelCase       ).eval()
    model.load_state_dict(_lowerCamelCase       )

    # create image processor
    A__				=			create_transform(**resolve_data_config({}						,       model=_lowerCamelCase       )       )
    A__				=			transform.transforms

    A__				=			{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    A__				=			ViTHybridImageProcessor(
        do_resize=_lowerCamelCase						,       size={"shortest_edge": timm_transforms[0].size}						,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,       do_center_crop=_lowerCamelCase						,       crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}						,       do_normalize=_lowerCamelCase						,       image_mean=timm_transforms[-1].mean.tolist()						,       image_std=timm_transforms[-1].std.tolist()						,       )

    A__				=			prepare_img()
    A__				=			transform(_lowerCamelCase       ).unsqueeze(0       )
    A__				=			processor(_lowerCamelCase						,       return_tensors="pt"       ).pixel_values

    # verify pixel values
    assert torch.allclose(_lowerCamelCase						,       _lowerCamelCase       )

    # verify logits
    with torch.no_grad():
        A__				=			model(_lowerCamelCase       )
        A__				=			outputs.logits

    print("Predicted class:"						,       logits.argmax(-1       ).item()       )
    if base_model:
        A__				=			timm_model.forward_features(_lowerCamelCase       )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(_lowerCamelCase						,       outputs.pooler_output						,       atol=1e-3       )
    else:
        A__				=			timm_model(_lowerCamelCase       )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(_lowerCamelCase						,       outputs.logits						,       atol=1e-3       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        Path(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
        print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"       )
        model.save_pretrained(_lowerCamelCase       )
        print(F"Saving processor to {pytorch_dump_folder_path}"       )
        processor.save_pretrained(_lowerCamelCase       )

    if push_to_hub:
        print(F"Pushing model and processor to the hub {vit_name}"       )
        model.push_to_hub(F"ybelkada/{vit_name}"       )
        processor.push_to_hub(F"ybelkada/{vit_name}"       )


if __name__ == "__main__":
  __lowerCAmelCase							:	List[str]			=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_r50_s16_384",
      type=str,
      help="Name of the hybrid ViT 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."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
  )

  __lowerCAmelCase							:	Optional[int]			=parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



