


import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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_ :List[Any]  							=			'platform'
    import jax
    import jax.numpy as jnp
    import numpy as np

    from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class 							lowercase  :
     lowerCamelCase			:			int      = PegasusConfig
     lowerCamelCase			:			int      = {}
     lowerCamelCase			:			Optional[Any]      = '''gelu'''







     def __init__(						self :						Tuple   ,   _lowercase :						Dict   ,   _lowercase :						Union[str, Any]=13   ,   _lowercase :						Dict=7   ,   _lowercase :						Optional[int]=True   ,   _lowercase :						Dict=False   ,   _lowercase :						Optional[int]=99   ,   _lowercase :						Optional[int]=32   ,   _lowercase :						List[str]=5   ,   _lowercase :						Optional[Any]=4   ,   _lowercase :						Union[str, Any]=37   ,   _lowercase :						Optional[int]=0.1   ,   _lowercase :						Dict=0.1   ,   _lowercase :						Dict=20   ,   _lowercase :						Dict=2   ,   _lowercase :						Union[str, Any]=1   ,   _lowercase :						List[Any]=0   ,   ):
           SCREAMING_SNAKE_CASE__       :  int						     =		parent
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		batch_size
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		seq_length
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		is_training
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		use_labels
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		vocab_size
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		hidden_size
           SCREAMING_SNAKE_CASE__       :  str						     =		num_hidden_layers
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		num_attention_heads
           SCREAMING_SNAKE_CASE__       :  int						     =		intermediate_size

           SCREAMING_SNAKE_CASE__       :  str						     =		hidden_dropout_prob
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		max_position_embeddings
           SCREAMING_SNAKE_CASE__       :  Dict						     =		eos_token_id
           SCREAMING_SNAKE_CASE__       :  Any						     =		pad_token_id
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		bos_token_id







     def 							lowercase__       (						self :						str      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		ids_tensor([self.batch_size, self.seq_length - 1]   ,   self.vocab_size      ).clip(3   ,   self.vocab_size      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		np.expand_dims(np.array([self.eos_token_id] * self.batch_size      )   ,   1      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		np.concatenate([input_ids, eos_tensor]   ,   axis=1      )

           SCREAMING_SNAKE_CASE__       :  str						     =		ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		self.config_cls(
               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_ids=[2]   ,   bos_token_id=self.bos_token_id   ,   pad_token_id=self.pad_token_id   ,   decoder_start_token_id=self.pad_token_id   ,   **self.config_updates   ,   )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		prepare_pegasus_inputs_dict(_lowercase   ,   _lowercase   ,   _lowercase      )
           return config, inputs_dict







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						Dict   ,   _lowercase :						Dict   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		20
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model_class_name(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model.encode(inputs_dict['''input_ids''']      )

           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		model.init_cache(decoder_input_ids.shape[0]   ,   _lowercase   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  int						     =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,   dtype='''i4'''      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]   ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,   )
           SCREAMING_SNAKE_CASE__       :  Any						     =		model.decode(
               decoder_input_ids[:, :-1]   ,   _lowercase   ,   decoder_attention_mask=_lowercase   ,   past_key_values=_lowercase   ,   decoder_position_ids=_lowercase   ,   )

           SCREAMING_SNAKE_CASE__       :  str						     =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,   dtype='''i4'''      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		model.decode(
               decoder_input_ids[:, -1:]   ,   _lowercase   ,   decoder_attention_mask=_lowercase   ,   past_key_values=outputs_cache.past_key_values   ,   decoder_position_ids=_lowercase   ,   )

           SCREAMING_SNAKE_CASE__       :  str						     =		model.decode(_lowercase   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		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 							lowercase__       (						self :						str   ,   _lowercase :						Any   ,   _lowercase :						str   ,   _lowercase :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		20
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model_class_name(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		model.encode(inputs_dict['''input_ids''']      )

           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

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

           SCREAMING_SNAKE_CASE__       :  int						     =		model.init_cache(decoder_input_ids.shape[0]   ,   _lowercase   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]   ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,   )

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

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model.decode(_lowercase   ,   _lowercase   ,   decoder_attention_mask=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  str						     =		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    a   (							A__      ,    A__      ,    A__      ,    A__=None      ,    A__=None      ,    )	-> List[str]:




      '''simple docstring'''

      if attention_mask is None:
            SCREAMING_SNAKE_CASE__       :  Any						     =		np.not_equal(A__      ,    config.pad_token_id							).astype(np.inta							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__       :  str						     =		np.concatenate(
                [
                    np.ones(decoder_input_ids[:, :1].shape      ,    dtype=np.inta							),
                    np.not_equal(decoder_input_ids[:, 1:]      ,    config.pad_token_id							).astype(np.inta							),
                ]      ,    axis=-1      ,    )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
      }





@require_flax
class 							lowercase  (						_UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			str      = (
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     lowerCamelCase			:			List[str]      = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     lowerCamelCase			:			str      = True
     lowerCamelCase			:			str      = False
     lowerCamelCase			:			List[str]      = False
     lowerCamelCase			:			Optional[Any]      = False







     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		FlaxPegasusModelTester(self      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		ConfigTester(self   ,   config_class=_lowercase      )







     def 							lowercase__       (						self :						List[str]      ):
           self.config_tester.run_common_tests()







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(_lowercase   ,   _lowercase   ,   _lowercase      )







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  int						     =		self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase   ,   _lowercase   ,   _lowercase      )







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__      ):
                       SCREAMING_SNAKE_CASE__       :  List[str]						     =		self._prepare_for_class(_lowercase   ,   _lowercase      )
                       SCREAMING_SNAKE_CASE__       :  Tuple						     =		model_class(_lowercase      )

                       @jax.jit
                       def encode_jitted(_lowercase :						Tuple   ,   _lowercase :						Any=None   ,   **_lowercase :						List[Any]      ):
                             return model.encode(input_ids=_lowercase   ,   attention_mask=_lowercase      )

                       with self.subTest('''JIT Enabled'''      ):
                             SCREAMING_SNAKE_CASE__       :  List[str]						     =		encode_jitted(**_lowercase      ).to_tuple()

                       with self.subTest('''JIT Disabled'''      ):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE__       :  List[str]						     =		encode_jitted(**_lowercase      ).to_tuple()

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







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__      ):
                       SCREAMING_SNAKE_CASE__       :  Dict						     =		model_class(_lowercase      )
                       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		model.encode(inputs_dict['''input_ids''']   ,   inputs_dict['''attention_mask''']      )

                       SCREAMING_SNAKE_CASE__       :  Dict						     =		{
                           '''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(_lowercase :						str   ,   _lowercase :						List[Any]   ,   _lowercase :						Dict      ):
                             return model.decode(
                                 decoder_input_ids=_lowercase   ,   decoder_attention_mask=_lowercase   ,   encoder_outputs=_lowercase   ,   )

                       with self.subTest('''JIT Enabled'''      ):
                             SCREAMING_SNAKE_CASE__       :  List[Any]						     =		decode_jitted(**_lowercase      ).to_tuple()

                       with self.subTest('''JIT Disabled'''      ):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE__       :  Dict						     =		decode_jitted(**_lowercase      ).to_tuple()

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







     @slow
     def 							lowercase__       (						self :						Optional[int]      ):
           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		model_class_name.from_pretrained('''google/pegasus-large'''   ,   from_pt=_lowercase      )
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		np.ones((1, 1)      )
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		model(_lowercase      )
                 self.assertIsNotNone(_lowercase      )







     @slow
     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''      )

           SCREAMING_SNAKE_CASE__       :  int						     =		[
               ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
               ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
           ]

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[
               '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
               '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
           ]

           SCREAMING_SNAKE_CASE__       :  int						     =		tokenizer(_lowercase   ,   return_tensors='''np'''   ,   truncation=_lowercase   ,   max_length=5_12   ,   padding=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  int						     =		model.generate(**_lowercase   ,   num_beams=2      ).sequences
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		tokenizer.batch_decode(_lowercase   ,   skip_special_tokens=_lowercase      )
           assert tgt_text == decoded









'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


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



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			List[nn.Module]     	= field(default_factory=a			)
  __lowerCAmelCase       :			list     	= field(default_factory=a			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						len(list(m.modules()  )  ) == 1 or isinstance(a_ ,			nn.Convad  ) or isinstance(a_ ,			nn.BatchNormad  )
    if has_not_submodules:
      self.traced.append(a_  )
  def __call__(  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    for m in self.module.modules():
      self.handles.append(m.register_forward_hook(self._forward_hook  )  )
    self.module(a_  )
    [x.remove() for x in self.handles]
    return self





  @property
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    # check the len of the state_dict keys to see if we have learnable params
    return list(filter(lambda a_  : len(list(x.state_dict().keys()  )  ) > 0 ,			self.traced  )  )




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			int     	= 0
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  def __call__(  self ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						Tracker(self.dest  )(a_  ).parametrized
    UpperCAmelCase    				=						Tracker(self.src  )(a_  ).parametrized

    UpperCAmelCase    				=						list(filter(lambda a_  : type(a_  ) not in self.src_skip ,			a_  )  )
    UpperCAmelCase    				=						list(filter(lambda a_  : type(a_  ) not in self.dest_skip ,			a_  )  )

    if len(a_  ) != len(a_  ):
      raise Exception(
          F'''Numbers of operations are different. Source module has {len(a_  )} operations while'''
          F''' destination module has {len(a_  )}.'''  )

    for dest_m, src_m in zip(a_ ,			a_  ):
      dest_m.load_state_dict(src_m.state_dict()  )
      if self.verbose == 1:
        print(F'''Transfered from={src_m} to={dest_m}'''  )




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : ResNetConfig							, SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : bool = True    ):
  print(f'''Converting {name}...'''    )
  with torch.no_grad():
    UpperCAmelCase    				=						timm.create_model(SCREAMING_SNAKE_CASE							, pretrained=SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ResNetForImageClassification(SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ModuleTransfer(src=SCREAMING_SNAKE_CASE							, dest=SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						torch.randn((1, 3, 224, 224)    )
    module_transfer(SCREAMING_SNAKE_CASE    )

  assert torch.allclose(from_model(SCREAMING_SNAKE_CASE    )							, our_model(SCREAMING_SNAKE_CASE    ).logits    ), "The model logits don't match the original one."

  UpperCAmelCase    				=						f'''resnet{'-'.join(name.split('resnet'    )    )}'''
  print(SCREAMING_SNAKE_CASE    )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							, commit_message='Add model'							, use_temp_dir=SCREAMING_SNAKE_CASE							, )

    # we can use the convnext one
    UpperCAmelCase    				=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							, commit_message='Add image processor'							, use_temp_dir=SCREAMING_SNAKE_CASE							, )

    print(f'''Pushed {checkpoint_name}'''    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : str = None							, SCREAMING_SNAKE_CASE   : bool = True    ):
  UpperCAmelCase    				=						'imagenet-1k-id2label.json'
  UpperCAmelCase    				=						1000
  UpperCAmelCase    				=						(1, num_labels)

  UpperCAmelCase    				=						'huggingface/label-files'
  UpperCAmelCase    				=						num_labels
  UpperCAmelCase    				=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, repo_type='dataset'    )							, 'r'    )    )
  UpperCAmelCase    				=						{int(SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}

  UpperCAmelCase    				=						idalabel
  UpperCAmelCase    				=						{v: k for k, v in idalabel.items()}

  UpperCAmelCase    				=						partial(SCREAMING_SNAKE_CASE							, num_labels=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid=SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						{
      'resnet18': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							, hidden_sizes=[64, 128, 256, 512]							, layer_type='basic'    ),
      'resnet26': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
      'resnet34': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							, hidden_sizes=[64, 128, 256, 512]							, layer_type='basic'    ),
      'resnet50': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
      'resnet101': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
      'resnet152': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
  }

  if model_name:
    convert_weight_and_push(SCREAMING_SNAKE_CASE							, names_to_config[model_name]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  return config, expected_shape


if __name__ == "__main__":
  _a					:			List[str]							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help=(
          'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
          ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=Path,
      required=True,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      default=True,
      type=bool,
      required=False,
      help='If True, push model and image processor to the hub.',
  )

  _a					:			Tuple							=    parser.parse_args()
  _a					:			Path							=    args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


