



"""simple docstring"""

from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         AutoTokenizer,
         TFAutoModelForCausalLM,
         TFAutoModelForSeqaSeqLM,
         TFAutoModelForSpeechSeqaSeq,
         TFAutoModelForVisionaSeq,
         TFBartForConditionalGeneration,
         TFLogitsProcessorList,
         TFMinLengthLogitsProcessor,
         tf_top_k_top_p_filtering,
     )

if is_tensorflow_text_available():
     import tensorflow_text as text




@require_tf
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     List[str]				=	tf.convert_to_tensor(
         [
             [
                 8.2_22_09_91,  # 3rd highest value; idx. 0
                 -0.5_62_00_44,
                 5.23_22_97_52,
                 4.0_38_63_93,
                 -6.8_79_83_78,
                 -0.54_78_58_02,
                 -3.2_01_21_53,
                 2.92_77_71_76,
                 1.88_17_19_53,
                 7.35_34_12_76,  # 5th highest value; idx. 9
                 8.43_20_78_33,  # 2nd highest value; idx. 10
                 -9.85_71_18_36,
                 -5.96_20_92_36,
                 -1.13_03_91_61,
                 -7.1_11_52_94,
                 -0.8_36_96_33,
                 -5.3_18_64_08,
                 7.06_42_74_07,
                 0.81_36_93_44,
                 -0.82_02_38_17,
                 -5.9_17_97_96,
                 0.58_81_34_43,
                 -6.99_77_84_38,
                 4.71_55_11_89,
                 -0.18_77_16_37,
                 7.44_02_07_59,  # 4th highest value; idx. 25
                 9.38_45_09_87,  # 1st highest value; idx. 26
                 2.12_66_29_41,
                 -9.32_56_20_38,
                 2.35_65_25_22,
             ],  # cummulative prob of 5 highest values <= 0.6
             [
                 0.58_42_55_18,
                 4.53_13_92_38,
                 -5.57_51_04_64,
                 -6.28_03_06_99,
                 -7.19_52_95_03,
                 -4.02_12_25_51,
                 1.39_33_70_37,
                 -6.06_70_70_57,
                 1.59_48_05_17,
                 -9.64_31_19,
                 0.03_90_77_99,
                 0.67_23_17_62,
                 -8.88_20_67_26,
                 6.27_11_59_22,  # 4th highest value; idx. 13
                 2.28_52_07_23,
                 4.82_76_75_06,
                 4.30_42_13_68,
                 8.8_27_53_13,  # 2nd highest value; idx. 17
                 5.44_02_99_58,  # 5th highest value; idx. 18
                 -4.4_73_57_94,
                 7.38_57_95_36,  # 3rd highest value; idx. 20
                 -2.91_05_16_63,
                 2.61_94_60_77,
                 -2.5_67_47_62,
                 -9.48_95_93_02,
                 -4.02_92_26_45,
                 -1.35_41_69_18,
                 9.67_70_23_23,  # 1st highest value; idx. 27
                 -5.89_47_85_53,
                 1.85_37_04_67,
             ],  # cummulative prob of 5 highest values <= 0.6
         ]  ,     dtype=tf.floataa  ,     )

     snake_case_ :     int				=	tf.convert_to_tensor(
         [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]  ,     dtype=tf.intaa  ,     )  # expected non filtered idx as noted above

     snake_case_ :     List[Any]				=	tf.convert_to_tensor(
         [8.22_20_99, 7.3_53_41_26, 8.43_20_78, 7.4_40_20_75, 9.3_84_51, 6.27_11_59, 8.82_75_31, 5.4_40_29_95, 7.3_85_79_56, 9.67_70_23]  ,     dtype=tf.floataa  ,     )  # expected non filtered values as noted above

     snake_case_ :     List[str]				=	tf_top_k_top_p_filtering(lowercase_  ,     top_k=10  ,     top_p=0.6  ,     min_tokens_to_keep=4				)

     snake_case_ :     Dict				=	output[output != -float('''inf'''				)]
     snake_case_ :     Any				=	tf.cast(
         tf.where(tf.not_equal(lowercase_  ,     tf.constant(-float('''inf'''				)  ,     dtype=tf.floataa				)				)				)  ,     dtype=tf.intaa  ,     )

     tf.debugging.assert_near(lowercase_  ,     lowercase_  ,     rtol=1E-12				)
     tf.debugging.assert_equal(lowercase_  ,     lowercase_				)





@require_tf
class        _UpperCAmelCase       (			unittest.TestCase    ,     lowerCAmelCase__):
 # setting framework_dependent_parameters needs to be gated, just like its contents' imports
 if is_tf_available():
  _lowerCAmelCase   :			str							=		{
      """AutoModelForCausalLM""": TFAutoModelForCausalLM,
      """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
      """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
      """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
      """LogitsProcessorList""": TFLogitsProcessorList,
      """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
      """create_tensor_fn""": tf.convert_to_tensor,
      """floats_tensor""": floats_tensor,
      """return_tensors""": """tf""",
  }

 @slow
 def 						_snake_case					(     self		:       Any				):
     # TF-only test: tf.saved_model export
     snake_case_ :     int				=	TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
     snake_case_ :     List[str]				=	2
     snake_case_ :     List[Any]				=	2




     class        _UpperCAmelCase       (			tf.Module):

      def __init__(     self		:       str  ,     lowercase_		:       int				):
          super(lowercase_  ,     self				).__init__()
          snake_case_ :     List[Any]				=	model






      @tf.function(
          input_signature=(
              tf.TensorSpec((None, input_length)  ,     tf.intaa  ,     name='''input_ids'''				),
              tf.TensorSpec((None, input_length)  ,     tf.intaa  ,     name='''attention_mask'''				),
          )  ,     jit_compile=lowercase_  ,     )
      def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       List[str]  ,     lowercase_		:       Dict				):
          snake_case_ :     Any				=	self.model.generate(
              input_ids=lowercase_  ,     attention_mask=lowercase_  ,     max_new_tokens=lowercase_  ,     return_dict_in_generate=lowercase_  ,     )
          return {"sequences": outputs["sequences"]}





     snake_case_ :     str				=	[[2, 0], [102, 103]]
     snake_case_ :     Optional[Any]				=	[[1, 0], [1, 1]]
     snake_case_ :     Any				=	DummyModel(model=lowercase_				)
     with tempfile.TemporaryDirectory() as tmp_dir:
         tf.saved_model.save(lowercase_  ,     lowercase_  ,     signatures={'''serving_default''': dummy_model.serving}				)
         snake_case_ :     Union[str, Any]				=	tf.saved_model.load(lowercase_				).signatures['''serving_default''']
         for batch_size in range(1  ,     len(lowercase_				) + 1				):
             snake_case_ :     List[str]				=	{
                 '''input_ids''': tf.constant(dummy_input_ids[:batch_size]				),
                 '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]				),
             }
             snake_case_ :     Tuple				=	serving_func(**lowercase_				)['''sequences''']
             snake_case_ :     str				=	test_model.generate(**lowercase_  ,     max_new_tokens=lowercase_				)
             tf.debugging.assert_equal(lowercase_  ,     lowercase_				)

 @slow
 def 						_snake_case					(     self		:       Optional[int]				):
     # TF-only test: tf.saved_model export
     snake_case_ :     List[Any]				=	TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
     snake_case_ :     List[Any]				=	1
     snake_case_ :     List[Any]				=	2




     class        _UpperCAmelCase       (			tf.Module):

      def __init__(     self		:       Dict  ,     lowercase_		:       Optional[int]				):
          super(lowercase_  ,     self				).__init__()
          snake_case_ :     Any				=	model






      @tf.function(
          input_signature=(
              tf.TensorSpec((batch_size, None)  ,     tf.intaa  ,     name='''input_ids'''				),
              tf.TensorSpec((batch_size, None)  ,     tf.intaa  ,     name='''attention_mask'''				),
          )  ,     jit_compile=lowercase_  ,     )
      def 						_snake_case					(     self		:       Any  ,     lowercase_		:       Dict  ,     lowercase_		:       List[Any]				):
          snake_case_ :     List[str]				=	self.model.generate(
              input_ids=lowercase_  ,     attention_mask=lowercase_  ,     max_new_tokens=lowercase_  ,     return_dict_in_generate=lowercase_  ,     )
          return {"sequences": outputs["sequences"]}





     snake_case_ :     Union[str, Any]				=	[[2], [102, 103]]
     snake_case_ :     Union[str, Any]				=	[[1], [1, 1]]
     snake_case_ :     Optional[Any]				=	DummyModel(model=lowercase_				)
     with tempfile.TemporaryDirectory() as tmp_dir:
         tf.saved_model.save(lowercase_  ,     lowercase_  ,     signatures={'''serving_default''': dummy_model.serving}				)
         snake_case_ :     str				=	tf.saved_model.load(lowercase_				).signatures['''serving_default''']
         for input_row in range(len(lowercase_				)				):
             snake_case_ :     Any				=	{
                 '''input_ids''': tf.constant([dummy_input_ids[input_row]]				),
                 '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]				),
             }
             snake_case_ :     Tuple				=	serving_func(**lowercase_				)['''sequences''']
             snake_case_ :     Optional[Any]				=	test_model.generate(**lowercase_  ,     max_new_tokens=lowercase_				)
             tf.debugging.assert_equal(lowercase_  ,     lowercase_				)

 @slow
 @require_tensorflow_text
 def 						_snake_case					(     self		:       Union[str, Any]				):
     # TF-only test: tf.saved_model export
     with tempfile.TemporaryDirectory() as tmp_dir:
         # file needed to load the TF tokenizer
         hf_hub_download(repo_id='''google/flan-t5-small'''  ,     filename='''spiece.model'''  ,     local_dir=lowercase_				)




         class        _UpperCAmelCase       (			tf.keras.layers.Layer):

          def __init__(     self		:       str				):
              super().__init__()
              snake_case_ :     Union[str, Any]				=	text.SentencepieceTokenizer(
                  model=tf.io.gfile.GFile(os.path.join(lowercase_  ,     '''spiece.model'''				)  ,     '''rb'''				).read()				)
              snake_case_ :     Optional[Any]				=	TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)






          def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       List[str]  ,     *lowercase_		:       Union[str, Any]  ,     **lowercase_		:       Union[str, Any]				):
              snake_case_ :     str				=	self.tokenizer.tokenize(lowercase_				)
              snake_case_,	snake_case_ :     Union[str, Any]				=	text.pad_model_inputs(
                  lowercase_  ,     max_seq_length=64  ,     pad_value=self.model.config.pad_token_id				)
              snake_case_ :     Union[str, Any]				=	self.model.generate(input_ids=lowercase_  ,     attention_mask=lowercase_				)
              return self.tokenizer.detokenize(lowercase_				)





         snake_case_ :     Union[str, Any]				=	CompleteSentenceTransformer()
         snake_case_ :     Dict				=	tf.keras.layers.Input(shape=(1,)  ,     dtype=tf.string  ,     name='''inputs'''				)
         snake_case_ :     Tuple				=	complete_model(lowercase_				)
         snake_case_ :     int				=	tf.keras.Model(lowercase_  ,     lowercase_				)
         keras_model.save(lowercase_				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     # Has PT equivalent: this test relies on random sampling
     snake_case_ :     int				=	{
         '''do_sample''': True,
         '''num_beams''': 1,
         '''top_p''': 0.7,
         '''top_k''': 10,
         '''temperature''': 0.7,
     }
     snake_case_ :     Any				=	14

     snake_case_ :     Dict				=	AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
     snake_case_ :     Optional[int]				=	'''Hello, my dog is cute and'''
     snake_case_ :     Union[str, Any]				=	tokenizer(lowercase_  ,     return_tensors='''tf'''				)
     snake_case_ :     Tuple				=	TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)

     snake_case_ :     Tuple				=	638
     # forces the generation to happen on CPU, to avoid GPU-related quirks
     with tf.device(''':/CPU:0'''				):
         tf.random.set_seed(0				)
         snake_case_ :     Any				=	model.generate(**lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     self.assertTrue(expectation == len(generated_tokens[0]				)				)

     snake_case_ :     Optional[Any]				=	[638, 198]
     with tf.device(''':/CPU:0'''				):
         tf.random.set_seed(0				)
         snake_case_ :     List[Any]				=	model.generate(**lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     self.assertTrue(expectation == len(generated_tokens[0]				)				)






 def 						_snake_case					(     self		:       int				):
     # Has PT equivalent: ample use of framework-specific code
     snake_case_ :     Tuple				=	AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''				)
     snake_case_ :     List[Any]				=	'''Hugging Face is a technology company based in New York and Paris.'''
     snake_case_ :     List[Any]				=	bart_tokenizer(lowercase_  ,     return_tensors='''tf'''				).input_ids
     snake_case_ :     List[str]				=	TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''				)
     snake_case_ :     Union[str, Any]				=	bart_model.generate(lowercase_				).numpy()




     class        _UpperCAmelCase       (			lowerCAmelCase__):

      def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       str=None  ,     **lowercase_		:       Optional[int]				):
          return super().call(lowercase_  ,     **lowercase_				)





     snake_case_ :     Dict				=	FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''				)
     snake_case_ :     Optional[int]				=	bart_model.generate(lowercase_  ,     foo='''bar'''				).numpy()
     self.assertTrue(np.array_equal(lowercase_  ,     lowercase_				)				)




     class        _UpperCAmelCase       (			bart_model.model.encoder.__class__):

      def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Union[str, Any]  ,     **lowercase_		:       Any				):
          return super().call(lowercase_  ,     **lowercase_				)





     snake_case_ :     List[str]				=	FakeEncoder(bart_model.config  ,     bart_model.model.shared				)
     snake_case_ :     Any				=	fake_encoder

     # Normal generation still works (the output will be different because the encoder weights are different)
     snake_case_ :     Union[str, Any]				=	bart_model.generate(lowercase_				).numpy()
     with self.assertRaises(lowercase_				):
         # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
         bart_model.generate(lowercase_  ,     foo='''bar'''				)










"""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()
lowercase__    :  Dict							    =	logging.get_logger(__name__)




def __lowercase			(							_a		,			_a=False    ):
    snake_case_ :     List[str]				=	[]

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




def __lowercase			(							_a    ):
    snake_case_ :     Dict				=	['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(_a		,			_a    )




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Union[str, Any]				=	dct.pop(_a    )
    snake_case_ :     Union[str, Any]				=	val




def __lowercase			(							):
    snake_case_ :     Any				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_ :     Tuple				=	Image.open(requests.get(_a		,			stream=_a    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a=False    ):
    snake_case_ :     str				=	BitConfig(
        global_padding='''same'''		,			layer_type='''bottleneck'''		,			depths=(3, 4, 9)		,			out_features=['''stage3''']		,			embedding_dynamic_padding=_a		,			)
    snake_case_ :     Tuple				=	ViTHybridConfig(backbone_config=_a		,			image_size=384		,			num_labels=1_000    )
    snake_case_ :     int				=	False

    # load original model from timm
    snake_case_ :     str				=	timm.create_model(_a		,			pretrained=_a    )
    timm_model.eval()

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

    snake_case_ :     Optional[Any]				=	'''huggingface/label-files'''
    snake_case_ :     Any				=	'''imagenet-1k-id2label.json'''
    snake_case_ :     Dict				=	json.load(open(hf_hub_download(_a		,			_a		,			repo_type='''dataset'''    )		,			'''r'''    )    )
    snake_case_ :     Dict				=	{int(_a    ): v for k, v in idalabel.items()}
    snake_case_ :     Optional[int]				=	idalabel
    snake_case_ :     Optional[Any]				=	{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        snake_case_ :     Optional[Any]				=	ViTHybridModel(_a    ).eval()
    else:
        snake_case_ :     Any				=	ViTHybridForImageClassification(_a    ).eval()
    model.load_state_dict(_a    )

    # create image processor
    snake_case_ :     Optional[Any]				=	create_transform(**resolve_data_config({}		,			model=_a    )    )
    snake_case_ :     List[Any]				=	transform.transforms

    snake_case_ :     Optional[Any]				=	{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

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

    snake_case_ :     Optional[int]				=	prepare_img()
    snake_case_ :     Optional[int]				=	transform(_a    ).unsqueeze(0    )
    snake_case_ :     int				=	processor(_a		,			return_tensors='''pt'''    ).pixel_values

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

    # verify logits
    with torch.no_grad():
        snake_case_ :     List[str]				=	model(_a    )
        snake_case_ :     Any				=	outputs.logits

    print('''Predicted class:'''		,			logits.argmax(-1    ).item()    )
    if base_model:
        snake_case_ :     Optional[Any]				=	timm_model.forward_features(_a    )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(_a		,			outputs.pooler_output		,			atol=1E-3    )
    else:
        snake_case_ :     int				=	timm_model(_a    )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(_a		,			outputs.logits		,			atol=1E-3    )
    print('''Looks ok!'''    )

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

    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__":
     lowercase__    :  int							    =	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.'''
     )

     lowercase__    :  Any							    =	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





