

'''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				: Tuple									=						'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       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   List[str]=None       ,   SCREAMING_SNAKE_CASE      :   Tuple=None       ,   SCREAMING_SNAKE_CASE      :   List[Any]=None       ,   SCREAMING_SNAKE_CASE      :   Union[str, Any]=None       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]=None       ,   SCREAMING_SNAKE_CASE      :   List[str]=None       ,   ):







  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        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def __init__(     self   , a_   , a_=1_3   , a_=7   , a_=True   , a_=False   , a_=9_9   , a_=1_6   , a_=2   , a_=4   , a_=4   , a_="gelu"   , a_=0.1   , a_=0.1   , a_=3_2   , a_=2   , a_=1   , a_=0   , a_=2   , a_=0.02   , )							->				Any:


    """simple docstring"""
    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 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    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(__UpperCamelCase   , 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=__UpperCamelCase   , )
    UpperCAmelCase													=	prepare_mbart_inputs_dict(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase     )
    return config, inputs_dict
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase													=	self.prepare_config_and_inputs()
    return config, inputs_dict
  def 						snake_case_			(     self   , a_   , a_   , a_     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	2_0
    UpperCAmelCase													=	model_class_name(__UpperCamelCase     )

    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]   , __UpperCamelCase   , __UpperCamelCase     )
    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]   , __UpperCamelCase   , decoder_attention_mask=__UpperCamelCase   , past_key_values=__UpperCamelCase   , decoder_position_ids=__UpperCamelCase   , )

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

    UpperCAmelCase													=	model.decode(__UpperCamelCase   , __UpperCamelCase     )

    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 						snake_case_			(     self   , a_   , a_   , a_     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	2_0
    UpperCAmelCase													=	model_class_name(__UpperCamelCase     )

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

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

    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        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  __lowerCAmelCase					: Optional[int]      =   99
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    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 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase													=	self._get_config_and_data()
    UpperCAmelCase													=	FlaxMBartForSequenceClassification(__UpperCamelCase     )
    UpperCAmelCase													=	model(input_ids=__UpperCamelCase   , decoder_input_ids=__UpperCamelCase     )
    UpperCAmelCase													=	(batch_size, config.num_labels)
    self.assertEqual(outputs['logits'].shape   , __UpperCamelCase     )
  def 						snake_case_			(     self     )							->				Optional[Any]:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase													=	self._get_config_and_data()
    UpperCAmelCase													=	FlaxMBartForQuestionAnswering(__UpperCamelCase     )
    UpperCAmelCase													=	model(input_ids=__UpperCamelCase     )

    self.assertEqual(outputs['start_logits'].shape   , input_ids.shape     )
    self.assertEqual(outputs['end_logits'].shape   , input_ids.shape     )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase													=	self._get_config_and_data()
    UpperCAmelCase													=	FlaxMBartForConditionalGeneration(__UpperCamelCase     )
    UpperCAmelCase													=	lm_model(input_ids=__UpperCamelCase     )
    UpperCAmelCase													=	(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['logits'].shape   , __UpperCamelCase     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    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(__UpperCamelCase     )
    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=__UpperCamelCase   , decoder_input_ids=__UpperCamelCase     )
    UpperCAmelCase													=	(*summary.shape, config.vocab_size)
    self.assertEqual(outputs['logits'].shape   , __UpperCamelCase     )





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    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(__UpperCamelCase   , 1     )
    UpperCAmelCase													=	np.equal(__UpperCamelCase   , 1     ).astype(np.floataa     ).sum()
    UpperCAmelCase													=	np.equal(__UpperCamelCase   , 1     ).astype(np.floataa     ).sum()
    self.assertEqual(shifted.shape   , input_ids.shape     )
    self.assertEqual(__UpperCamelCase   , n_pad_before - 1     )
    self.assertTrue(np.equal(shifted[:, 0]   , 2     ).all()     )




@require_flax
class        lowercase_	(		UpperCAmelCase_ ,       unittest.TestCase ,       UpperCAmelCase_ ):

  '''simple docstring'''





  __lowerCAmelCase					: Tuple      =   True
  __lowerCAmelCase					: Dict      =   (
      (
          FlaxMBartModel,
          FlaxMBartForConditionalGeneration,
          FlaxMBartForSequenceClassification,
          FlaxMBartForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )
  __lowerCAmelCase					: Dict      =   (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
  def 						snake_case_			(     self     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase													=	FlaxMBartModelTester(self     )
  def 						snake_case_			(     self     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase													=	self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase     )
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    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(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase     )
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    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(__UpperCamelCase   , __UpperCamelCase     )
        UpperCAmelCase													=	model_class(__UpperCamelCase     )

        @jax.jit
        def encode_jitted(a_   , a_=None   , **a_     ):
          return model.encode(input_ids=__UpperCamelCase   , attention_mask=__UpperCamelCase     )

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

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

        self.assertEqual(len(__UpperCamelCase     )   , len(__UpperCamelCase     )     )
        for jitted_output, output in zip(__UpperCamelCase   , __UpperCamelCase     ):
          self.assertEqual(jitted_output.shape   , output.shape     )
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    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(__UpperCamelCase     )
        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_   , a_   , a_     ):
          return model.decode(
              decoder_input_ids=__UpperCamelCase   , decoder_attention_mask=__UpperCamelCase   , encoder_outputs=__UpperCamelCase   , )

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

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

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





  @slow
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    for model_class_name in self.all_model_classes:
      UpperCAmelCase													=	model_class_name.from_pretrained('facebook/mbart-large-cc25'   , from_pt=__UpperCamelCase     )
      # FlaxMBartForSequenceClassification expects eos token in input_ids
      UpperCAmelCase													=	np.ones((1, 1)     ) * model.config.eos_token_id
      UpperCAmelCase													=	model(__UpperCamelCase     )
      self.assertIsNotNone(__UpperCamelCase     )




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

  '''simple docstring'''





  __lowerCAmelCase					: List[Any]      =   [
      " UN Chief Says There Is No Military Solution in Syria",
  ]
  __lowerCAmelCase					: List[Any]      =   [
      "Şeful ONU declară că nu există o soluţie militară în Siria",
  ]
  __lowerCAmelCase					: str      =   "facebook/mbart-large-en-ro"
  @cached_property
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.model_name     )
  @cached_property
  def 						snake_case_			(     self     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	FlaxMBartForConditionalGeneration.from_pretrained(self.model_name   , from_pt=__UpperCamelCase     )
    return model
  def 						snake_case_			(     self   , **a_     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	self.translate_src_text(**__UpperCamelCase     )
    self.assertListEqual(self.expected_text   , __UpperCamelCase     )
  def 						snake_case_			(     self   , **a_     )							->				Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase													=	self.tokenizer(self.src_text   , **__UpperCamelCase   , 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=__UpperCamelCase   , num_beams=2   , ).sequences
    UpperCAmelCase													=	self.tokenizer.batch_decode(__UpperCamelCase   , skip_special_tokens=__UpperCamelCase     )
    return generated_words





  @slow
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    self._assert_generated_batch_equal_expected()





'''simple docstring'''




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_a				: Optional[int] 							=						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 )
  __lowerCAmelCase					: Dict[str, nn.Module]      =   field(default_factory=a )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_     )							->				Optional[int]:


    """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_     )
      UpperCAmelCase													=	m
  def __call__(     self   , a_     )							->				List[Any]:


    """simple docstring"""
    for name, m in self.module.named_modules():
      self.handles.append(m.register_forward_hook(partial(self._forward_hook   , name=a_     )     )     )
    self.module(a_     )
    [x.remove() for x in self.handles]
    return self





  @property
  def 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    # check the len of the state_dict keys to see if we have learnable params
    return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()     )     ) > 0}




class        lowercase_	(		nn.Module ):

  '''simple docstring'''





  def __init__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    super().__init__()

    UpperCAmelCase													=	[]
    # - get the stem
    feature_blocks.append(('conv1', model.stem)     )
    # - get all the feature blocks
    for k, v in model.trunk_output.named_children():
      assert k.startswith('block'     ), F'''Unexpected layer name {k}'''
      UpperCAmelCase													=	len(a_     ) + 1
      feature_blocks.append((F'''res{block_index}''', v)     )

    UpperCAmelCase													=	nn.ModuleDict(a_     )





  def 						snake_case_			(     self   , a_     )							->				Optional[Any]:


    """simple docstring"""
    return get_trunk_forward_outputs(
        a_   , out_feat_keys=a_   , feature_blocks=self._feature_blocks   , )




class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str						):
  UpperCAmelCase													=	RegNetConfig(depths=[2, 7, 17, 1]       ,   hidden_sizes=[8, 8, 8, 8]       ,   groups_width=8						)
  if "in1k" in model_name:
    UpperCAmelCase													=	RegNetForImageClassification(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	RegNetModel(SCREAMING_SNAKE_CASE						)
  # create from model (with small weights)
  UpperCAmelCase													=	FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27       ,   group_width=1010       ,   w_a=1744       ,   w_a=620.83       ,   w_m=2.52						)						)						)

  with torch.no_grad():
    UpperCAmelCase													=	from_model.eval()
    UpperCAmelCase													=	our_model.eval()

    UpperCAmelCase													=	torch.randn((1, 3, 32, 32)						)
    # trace both
    UpperCAmelCase													=	Tracker(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	dest_tracker(SCREAMING_SNAKE_CASE						).parametrized

    pprint(dest_tracker.nameamodule						)
    UpperCAmelCase													=	Tracker(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	src_tracker(SCREAMING_SNAKE_CASE						).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(SCREAMING_SNAKE_CASE      :   Tuple						):
    UpperCAmelCase													=	OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        UpperCAmelCase													=	param
    return params_dict

  UpperCAmelCase													=	{}

  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,   dst_state_dict.items()						):
    UpperCAmelCase													=	dest_key
    logger.info(f'''{src_key} -> {dest_key}'''						)
  # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  if "in1k" in model_name:
    UpperCAmelCase													=	'classifier.1.weight'
    UpperCAmelCase													=	'classifier.1.bias'

  return from_to_ours_keys


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

  UpperCAmelCase													=	'huggingface/label-files'
  UpperCAmelCase													=	num_labels
  UpperCAmelCase													=	json.load(open(cached_download(hf_hub_url(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													=	{
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
  }

  # add seer weights logic
  def load_using_classy_vision(SCREAMING_SNAKE_CASE      :   str						) -> Tuple[Dict, Dict]:
    UpperCAmelCase													=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE       ,   model_dir=str(SCREAMING_SNAKE_CASE						)       ,   map_location='cpu'						)
    # check if we have a head, if yes add it
    UpperCAmelCase													=	files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  UpperCAmelCase													=	{
      'regnet-y-10b-seer': partial(
          SCREAMING_SNAKE_CASE       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,   ),
      'regnet-y-10b-seer-in1k': partial(
          SCREAMING_SNAKE_CASE       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,   ),
  }

  UpperCAmelCase													=	get_from_to_our_keys(SCREAMING_SNAKE_CASE						)

  if not (save_directory / f'''{model_name}.pth''').exists():
    logger.info('Loading original state_dict.'						)
    UpperCAmelCase							,      UpperCAmelCase													=	names_to_from_model[model_name]()
    UpperCAmelCase													=	from_state_dict_trunk
    if "in1k" in model_name:
      # add the head
      UpperCAmelCase													=	{**from_state_dict_trunk, **from_state_dict_head}
    logger.info('Done!'						)

    UpperCAmelCase													=	{}

    UpperCAmelCase													=	list(from_state_dict.keys()						)
    UpperCAmelCase													=	R'\.block.-part.'
    # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
    for key in from_state_dict.keys():
      # remove the weird "block[0,1]-part" from the key
      UpperCAmelCase													=	re.sub(SCREAMING_SNAKE_CASE       ,   ''       ,   SCREAMING_SNAKE_CASE						)
      # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
      UpperCAmelCase													=	from_to_ours_keys[src_key]
      # store the parameter with our key
      UpperCAmelCase													=	from_state_dict[key]
      not_used_keys.remove(SCREAMING_SNAKE_CASE						)
    # check that all keys have been updated
    assert len(SCREAMING_SNAKE_CASE						) == 0, f'''Some keys where not used {','.join(SCREAMING_SNAKE_CASE						)}'''

    logger.info(f'''The following keys were not used: {','.join(SCREAMING_SNAKE_CASE						)}'''						)

    # save our state dict to disk
    torch.save(SCREAMING_SNAKE_CASE       ,   save_directory / f'''{model_name}.pth'''						)

    del converted_state_dict
  else:
    logger.info('The state_dict was already stored on disk.'						)
  if push_to_hub:
    logger.info(f'''Token is {os.environ['HF_TOKEN']}'''						)
    logger.info('Loading our model.'						)
    # create our model
    UpperCAmelCase													=	names_to_config[model_name]
    UpperCAmelCase													=	RegNetModel
    if "in1k" in model_name:
      UpperCAmelCase													=	RegNetForImageClassification
    UpperCAmelCase													=	our_model_func(SCREAMING_SNAKE_CASE						)
    # place our model to the meta device (so remove all the weights)
    our_model.to(torch.device('meta'						)						)
    logger.info('Loading state_dict in our model.'						)
    # load state dict
    UpperCAmelCase													=	our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   [save_directory / f'''{model_name}.pth''']						)
    logger.info('Finally, pushing!'						)
    # push it to hub
    our_model.push_to_hub(
        repo_path_or_name=save_directory / model_name       ,   commit_message='Add model'       ,   output_dir=save_directory / model_name       ,   )
    UpperCAmelCase													=	384
    # we can use the convnext one
    UpperCAmelCase													=	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,   size=SCREAMING_SNAKE_CASE						)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / model_name       ,   commit_message='Add image processor'       ,   output_dir=save_directory / model_name       ,   )


if __name__ == "__main__":
  _a				: Dict 							=						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 regnet* architecture,'
          ' currently: regnetx-*, regnety-*. 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				: Union[str, Any] 							=						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)


