





'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    a__							:							Optional[Any]			='''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import AutoTokenizer
    from transformers.models.mbart.modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        shift_tokens_right,
    )
def     lowercase__       (       __lowercase						:  Union[str, Any]    ,  __lowercase						:  str    ,  __lowercase						:  Tuple=None    ,  __lowercase						:  Optional[Any]=None    ,  __lowercase						:  Union[str, Any]=None    ,  __lowercase						:  List[str]=None    ,  __lowercase						:  int=None    ,  __lowercase						:  List[str]=None    ,  )			->				List[str]:



       """simple docstring"""






       if attention_mask is None:
              __UpperCamelCase	=  np.where(input_ids != config.pad_token_id    ,  1    ,  0					)
       if decoder_attention_mask is None:
              __UpperCamelCase	=  np.where(decoder_input_ids != config.pad_token_id    ,  1    ,  0					)
       if head_mask is None:
              __UpperCamelCase	=  np.ones((config.encoder_layers, config.encoder_attention_heads)					)
       if decoder_head_mask is None:
              __UpperCamelCase	=  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
       if cross_attn_head_mask is None:
              __UpperCamelCase	=  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }

class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 def __init__(	self	:							List[str]							,     __A	:							Union[str, Any]							,     __A	:							List[str]=1_3							,     __A	:							int=7							,     __A	:							List[Any]=True							,     __A	:							Optional[Any]=False							,     __A	:							Optional[int]=9_9							,     __A	:							Any=1_6							,     __A	:							List[str]=2							,     __A	:							str=4							,     __A	:							Any=4							,     __A	:							Any="gelu"							,     __A	:							Dict=0.1							,     __A	:							Tuple=0.1							,     __A	:							Optional[int]=3_2							,     __A	:							Any=2							,     __A	:							Optional[int]=1							,     __A	:							Dict=0							,     __A	:							Optional[Any]=2							,     __A	:							Union[str, Any]=0.02							,     ):
        __UpperCamelCase	=  parent
        __UpperCamelCase	=  batch_size
        __UpperCamelCase	=  seq_length
        __UpperCamelCase	=  is_training
        __UpperCamelCase	=  use_labels
        __UpperCamelCase	=  vocab_size
        __UpperCamelCase	=  hidden_size
        __UpperCamelCase	=  num_hidden_layers
        __UpperCamelCase	=  num_attention_heads
        __UpperCamelCase	=  intermediate_size
        __UpperCamelCase	=  hidden_act
        __UpperCamelCase	=  hidden_dropout_prob
        __UpperCamelCase	=  attention_probs_dropout_prob
        __UpperCamelCase	=  max_position_embeddings
        __UpperCamelCase	=  eos_token_id
        __UpperCamelCase	=  pad_token_id
        __UpperCamelCase	=  bos_token_id
        __UpperCamelCase	=  decoder_start_token_id
        __UpperCamelCase	=  initializer_range






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size     )							,     3							,     self.vocab_size     )
        __UpperCamelCase	=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,     dtype=np.intaa     ))							,     -1     )

        __UpperCamelCase	=  shift_tokens_right(__A							,     1     )

        __UpperCamelCase	=  MBartConfig(
            vocab_size=self.vocab_size							,     d_model=self.hidden_size							,     encoder_layers=self.num_hidden_layers							,     decoder_layers=self.num_hidden_layers							,     encoder_attention_heads=self.num_attention_heads							,     decoder_attention_heads=self.num_attention_heads							,     encoder_ffn_dim=self.intermediate_size							,     decoder_ffn_dim=self.intermediate_size							,     dropout=self.hidden_dropout_prob							,     attention_dropout=self.attention_probs_dropout_prob							,     max_position_embeddings=self.max_position_embeddings							,     eos_token_id=self.eos_token_id							,     bos_token_id=self.bos_token_id							,     pad_token_id=self.pad_token_id							,     decoder_start_token_id=self.decoder_start_token_id							,     initializer_range=self.initializer_range							,     use_cache=__A							,     )
        __UpperCamelCase	=  prepare_mbart_inputs_dict(__A							,     __A							,     __A     )
        return config, inputs_dict






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.prepare_config_and_inputs()
        return config, inputs_dict






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							Union[str, Any]							,     __A	:							Union[str, Any]							,     __A	:							Tuple     ):
        __UpperCamelCase	=  2_0
        __UpperCamelCase	=  model_class_name(__A     )

        __UpperCamelCase	=  model.encode(inputs_dict['input_ids']     )

        __UpperCamelCase	,						__UpperCamelCase	=  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        __UpperCamelCase	=  model.init_cache(decoder_input_ids.shape[0]							,     __A							,     __A     )
        __UpperCamelCase	=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,     dtype='i4'     )

        __UpperCamelCase	=  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]							,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,     )
        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, :-1]							,     __A							,     decoder_attention_mask=__A							,     past_key_values=__A							,     decoder_position_ids=__A							,     )

        __UpperCamelCase	=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,     dtype='i4'     )
        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, -1:]							,     __A							,     decoder_attention_mask=__A							,     past_key_values=outputs_cache.past_key_values							,     decoder_position_ids=__A							,     )

        __UpperCamelCase	=  model.decode(__A							,     __A     )

        __UpperCamelCase	=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3							,     msg=f'''Max diff is {diff}'''     )






 def 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							Any							,     __A	:							int							,     __A	:							Union[str, Any]     ):
        __UpperCamelCase	=  2_0
        __UpperCamelCase	=  model_class_name(__A     )

        __UpperCamelCase	=  model.encode(inputs_dict['input_ids']     )

        __UpperCamelCase	,						__UpperCamelCase	=  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        __UpperCamelCase	=  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
            ]							,     axis=-1							,     )

        __UpperCamelCase	=  model.init_cache(decoder_input_ids.shape[0]							,     __A							,     __A     )
        __UpperCamelCase	=  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]							,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,     )

        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, :-1]							,     __A							,     decoder_attention_mask=__A							,     past_key_values=__A							,     decoder_position_ids=__A							,     )
        __UpperCamelCase	=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,     dtype='i4'     )
        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, -1:]							,     __A							,     past_key_values=outputs_cache.past_key_values							,     decoder_attention_mask=__A							,     decoder_position_ids=__A							,     )

        __UpperCamelCase	=  model.decode(__A							,     __A							,     decoder_attention_mask=__A     )

        __UpperCamelCase	=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3							,     msg=f'''Max diff is {diff}'''     )






@require_flax
class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Any      		=99






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  np.array(
            [
                [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
            ]							,     dtype=np.intaa							,     )

        __UpperCamelCase	=  input_ids.shape[0]
        __UpperCamelCase	=  MBartConfig(
            vocab_size=self.vocab_size							,     d_model=2_4							,     encoder_layers=2							,     decoder_layers=2							,     encoder_attention_heads=2							,     decoder_attention_heads=2							,     encoder_ffn_dim=3_2							,     decoder_ffn_dim=3_2							,     max_position_embeddings=4_8							,     eos_token_id=2							,     pad_token_id=1							,     bos_token_id=0							,     )
        return config, input_ids, batch_size






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  self._get_config_and_data()
        __UpperCamelCase	=  FlaxMBartForSequenceClassification(__A     )
        __UpperCamelCase	=  model(input_ids=__A							,     decoder_input_ids=__A     )
        __UpperCamelCase	=  (batch_size, config.num_labels)
        self.assertEqual(outputs['logits'].shape							,     __A     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  self._get_config_and_data()
        __UpperCamelCase	=  FlaxMBartForQuestionAnswering(__A     )
        __UpperCamelCase	=  model(input_ids=__A     )

        self.assertEqual(outputs['start_logits'].shape							,     input_ids.shape     )
        self.assertEqual(outputs['end_logits'].shape							,     input_ids.shape     )






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  self._get_config_and_data()
        __UpperCamelCase	=  FlaxMBartForConditionalGeneration(__A     )
        __UpperCamelCase	=  lm_model(input_ids=__A     )
        __UpperCamelCase	=  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['logits'].shape							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  MBartConfig(
            vocab_size=self.vocab_size							,     d_model=1_4							,     encoder_layers=2							,     decoder_layers=2							,     encoder_attention_heads=2							,     decoder_attention_heads=2							,     encoder_ffn_dim=8							,     decoder_ffn_dim=8							,     max_position_embeddings=4_8							,     )
        __UpperCamelCase	=  FlaxMBartForConditionalGeneration(__A     )
        __UpperCamelCase	=  np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,     dtype=np.intaa     )
        __UpperCamelCase	=  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,     dtype=np.intaa     )
        __UpperCamelCase	=  lm_model(input_ids=__A							,     decoder_input_ids=__A     )
        __UpperCamelCase	=  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['logits'].shape							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	=  np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,     dtype=np.intaa     )
        __UpperCamelCase	=  shift_tokens_right(__A							,     1     )
        __UpperCamelCase	=  np.equal(__A							,     1     ).astype(np.floataa     ).sum()
        __UpperCamelCase	=  np.equal(__A							,     1     ).astype(np.floataa     ).sum()
        self.assertEqual(shifted.shape							,     input_ids.shape     )
        self.assertEqual(__A							,     n_pad_before - 1     )
        self.assertTrue(np.equal(shifted[:, 0]							,     2     ).all()     )






@require_flax
class 					snake_case					(   __lowerCamelCase      ,	unittest.TestCase      ,	__lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=True
 SCREAMING_SNAKE_CASE_     : Tuple      		=(
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_     : Dict      		=(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  FlaxMBartModelTester(self     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(__A							,     __A							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(__A							,     __A							,     __A     )






 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__     ):
                      __UpperCamelCase	=  self._prepare_for_class(__A							,     __A     )
                      __UpperCamelCase	=  model_class(__A     )

                      @jax.jit
                      def encode_jitted(__A	:							Optional[int]							,     __A	:							List[str]=None							,     **__A	:							List[str]     ):
                             return model.encode(input_ids=__A							,     attention_mask=__A     )

                      with self.subTest('JIT Enabled'     ):
                             __UpperCamelCase	=  encode_jitted(**__A     ).to_tuple()

                      with self.subTest('JIT Disabled'     ):
                             with jax.disable_jit():
                                    __UpperCamelCase	=  encode_jitted(**__A     ).to_tuple()

                      self.assertEqual(len(__A     )							,     len(__A     )     )
                      for jitted_output, output in zip(__A							,     __A     ):
                             self.assertEqual(jitted_output.shape							,     output.shape     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__     ):
                      __UpperCamelCase	=  model_class(__A     )
                      __UpperCamelCase	=  model.encode(inputs_dict['input_ids']							,     inputs_dict['attention_mask']     )

                      __UpperCamelCase	=  {
                          'decoder_input_ids': inputs_dict['decoder_input_ids'],
                          'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                          'encoder_outputs': encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(__A	:							Union[str, Any]							,     __A	:							int							,     __A	:							List[Any]     ):
                             return model.decode(
                                 decoder_input_ids=__A							,     decoder_attention_mask=__A							,     encoder_outputs=__A							,     )

                      with self.subTest('JIT Enabled'     ):
                             __UpperCamelCase	=  decode_jitted(**__A     ).to_tuple()

                      with self.subTest('JIT Disabled'     ):
                             with jax.disable_jit():
                                    __UpperCamelCase	=  decode_jitted(**__A     ).to_tuple()

                      self.assertEqual(len(__A     )							,     len(__A     )     )
                      for jitted_output, output in zip(__A							,     __A     ):
                             self.assertEqual(jitted_output.shape							,     output.shape     )






 @slow
 def 	_lowerCamelCase (	self	:							str     ):
        for model_class_name in self.all_model_classes:
               __UpperCamelCase	=  model_class_name.from_pretrained('facebook/mbart-large-cc25'							,     from_pt=__A     )
               # FlaxMBartForSequenceClassification expects eos token in input_ids
               __UpperCamelCase	=  np.ones((1, 1)     ) * model.config.eos_token_id
               __UpperCamelCase	=  model(__A     )
               self.assertIsNotNone(__A     )






@require_flax
@require_sentencepiece
@require_tokenizers
class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[Any]      		=[
     " UN Chief Says There Is No Military Solution in Syria",
 ]
 SCREAMING_SNAKE_CASE_     : List[str]      		=[
     "Şeful ONU declară că nu există o soluţie militară în Siria",
 ]
 SCREAMING_SNAKE_CASE_     : Union[str, Any]      		="facebook/mbart-large-en-ro"






 @cached_property
 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        return AutoTokenizer.from_pretrained(self.model_name     )






 @cached_property
 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,     from_pt=__A     )
        return model






 def 	_lowerCamelCase (	self	:							List[Any]							,     **__A	:							Dict     ):
        __UpperCamelCase	=  self.translate_src_text(**__A     )
        self.assertListEqual(self.expected_text							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[int]							,     **__A	:							Optional[int]     ):
        __UpperCamelCase	=  self.tokenizer(self.src_text							,     **__A							,     return_tensors='np'     )
        __UpperCamelCase	=  self.model.generate(
            model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']							,     early_stopping=__A							,     num_beams=2							,     ).sequences
        __UpperCamelCase	=  self.tokenizer.batch_decode(__A							,     skip_special_tokens=__A     )
        return generated_words






 @slow
 def 	_lowerCamelCase (	self	:							Tuple     ):
        self._assert_generated_batch_equal_expected()







'''simple docstring'''
import argparse
import json
import os

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

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a__							:							Union[str, Any]			=logging.get_logger(__name__)
def     lowercase__       (       __lowercase						:  Union[str, Any]					)			->				Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase	=  ConvNextVaConfig()

       if "atto" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [40, 80, 160, 320]
       if "femto" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [48, 96, 192, 384]
       if "pico" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [64, 128, 256, 512]
       if "nano" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 8, 2]
              __UpperCamelCase	=  [80, 160, 320, 640]
       if "tiny" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 9, 3]
              __UpperCamelCase	=  [96, 192, 384, 768]
       if "base" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [128, 256, 512, 1024]
       if "large" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [192, 384, 768, 1536]
       if "huge" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [352, 704, 1408, 2816]

       __UpperCamelCase	=  1000
       __UpperCamelCase	=  'imagenet-1k-id2label.json'
       __UpperCamelCase	=  (1, 1000)

       __UpperCamelCase	=  'huggingface/label-files'
       __UpperCamelCase	=  num_labels
       __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()}
       __UpperCamelCase	=  hidden_sizes
       __UpperCamelCase	=  depths

       return config, expected_shape
def     lowercase__       (       __lowercase						:  Tuple					)			->				Optional[Any]:



       """simple docstring"""






       if "downsample_layers.0.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.0.0'    ,  'embeddings.patch_embeddings'					)
       if "downsample_layers.0.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.0.1'    ,  'embeddings.norm'					)  # we rename to layernorm later on
       if "downsample_layers.1.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.1.0'    ,  'stages.1.downsampling_layer.0'					)
       if "downsample_layers.1.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.1.1'    ,  'stages.1.downsampling_layer.1'					)
       if "downsample_layers.2.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.2.0'    ,  'stages.2.downsampling_layer.0'					)
       if "downsample_layers.2.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.2.1'    ,  'stages.2.downsampling_layer.1'					)
       if "downsample_layers.3.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.3.0'    ,  'stages.3.downsampling_layer.0'					)
       if "downsample_layers.3.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.3.1'    ,  'stages.3.downsampling_layer.1'					)
       if "stages" in name and "downsampling_layer" not in name:
              # stages.0.0. for instance should be renamed to stages.0.layers.0.
              __UpperCamelCase	=  name[: len('stages.0'					)] + '.layers' + name[len('stages.0'					) :]
       if "gamma" in name:
              __UpperCamelCase	=  name.replace('gamma'    ,  'weight'					)
       if "beta" in name:
              __UpperCamelCase	=  name.replace('beta'    ,  'bias'					)
       if "stages" in name:
              __UpperCamelCase	=  name.replace('stages'    ,  'encoder.stages'					)
       if "norm" in name:
              __UpperCamelCase	=  name.replace('norm'    ,  'layernorm'					)
       if "head" in name:
              __UpperCamelCase	=  name.replace('head'    ,  'classifier'					)

       return name
def     lowercase__       (       )			->				Any:



       """simple docstring"""






       __UpperCamelCase	=  'http://images.cocodataset.org/val2017/000000039769.jpg'
       __UpperCamelCase	=  Image.open(requests.get(__lowercase    ,  stream=__lowercase					).raw					)
       return im
def     lowercase__       (       __lowercase						:  Optional[Any]					)			->				Dict:



       """simple docstring"""






       if "224" in checkpoint_url:
              __UpperCamelCase	=  224
              __UpperCamelCase	=  224 / 256
       elif "384" in checkpoint_url:
              __UpperCamelCase	=  384
              __UpperCamelCase	=  None
       else:
              __UpperCamelCase	=  512
              __UpperCamelCase	=  None

       return ConvNextImageProcessor(
           size=__lowercase    ,  crop_pct=__lowercase    ,  image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]    ,  image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    ,  resample=PILImageResampling.BICUBIC    ,  )
@torch.no_grad()
def     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict					)			->				Optional[int]:



       """simple docstring"""






       print('Downloading original model from checkpoint...'					)
       # define ConvNeXTV2 configuration based on URL
       __UpperCamelCase	,						__UpperCamelCase	=  get_convnextva_config(__lowercase					)
       # load original state_dict from URL
       __UpperCamelCase	=  torch.hub.load_state_dict_from_url(__lowercase					)['model']

       print('Converting model parameters...'					)
       # rename keys
       for key in state_dict.copy().keys():
              __UpperCamelCase	=  state_dict.pop(__lowercase					)
              __UpperCamelCase	=  val
       # add prefix to all keys expect classifier head
       for key in state_dict.copy().keys():
              __UpperCamelCase	=  state_dict.pop(__lowercase					)
              if not key.startswith('classifier'					):
                     __UpperCamelCase	=  'convnextv2.' + key
              __UpperCamelCase	=  val

       # load HuggingFace model
       __UpperCamelCase	=  ConvNextVaForImageClassification(__lowercase					)
       model.load_state_dict(__lowercase					)
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       __UpperCamelCase	=  convert_preprocessor(__lowercase					)
       __UpperCamelCase	=  preprocessor(images=prepare_img()    ,  return_tensors='pt'					)
       __UpperCamelCase	=  model(**__lowercase					).logits

       # note: the logits below were obtained without center cropping
       if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]					)
       else:
              raise ValueError(F'''Unknown URL: {checkpoint_url}'''					)

       assert torch.allclose(logits[0, :5]    ,  __lowercase    ,  atol=1e-3					)
       assert logits.shape == expected_shape
       print('Model outputs match the original results!'					)

       if save_model:
              print('Saving model to local...'					)
              # Create folder to save model
              if not os.path.isdir(__lowercase					):
                     os.mkdir(__lowercase					)

              model.save_pretrained(__lowercase					)
              preprocessor.save_pretrained(__lowercase					)

       __UpperCamelCase	=  'convnextv2'
       if "atto" in checkpoint_url:
              model_name += "-atto"
       if "femto" in checkpoint_url:
              model_name += "-femto"
       if "pico" in checkpoint_url:
              model_name += "-pico"
       if "nano" in checkpoint_url:
              model_name += "-nano"
       elif "tiny" in checkpoint_url:
              model_name += "-tiny"
       elif "base" in checkpoint_url:
              model_name += "-base"
       elif "large" in checkpoint_url:
              model_name += "-large"
       elif "huge" in checkpoint_url:
              model_name += "-huge"
       if "22k" in checkpoint_url and "1k" not in checkpoint_url:
              model_name += "-22k"
       elif "22k" in checkpoint_url and "1k" in checkpoint_url:
              model_name += "-22k-1k"
       elif "1k" in checkpoint_url:
              model_name += "-1k"
       if "224" in checkpoint_url:
              model_name += "-224"
       elif "384" in checkpoint_url:
              model_name += "-384"
       elif "512" in checkpoint_url:
              model_name += "-512"

       if push_to_hub:
              print(F'''Pushing {model_name} to the hub...'''					)
              model.push_to_hub(__lowercase					)
              preprocessor.push_to_hub(__lowercase					)


if __name__ == "__main__":
    a__							:							str			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--checkpoint_url''',
        default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
        type=str,
        help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''model''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

    a__							:							Optional[Any]			=parser.parse_args()
    convert_convnextva_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
    )
