





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
      SCREAMING_SNAKE_CASE_						       =	"""platform"""
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class 		UpperCamelCase__			:



     '''simple docstring'''

     __snake_case   :     Tuple		     =      PegasusConfig
     __snake_case   :     Any		     =      {}
     __snake_case   :     List[Any]		     =      "gelu"






     def __init__(							self						:   Dict						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   List[Any]=13						,lowerCamelCase__						:   Optional[int]=7						,lowerCamelCase__						:   Optional[Any]=True						,lowerCamelCase__						:   Dict=False						,lowerCamelCase__						:   str=99						,lowerCamelCase__						:   Tuple=32						,lowerCamelCase__						:   int=5						,lowerCamelCase__						:   int=4						,lowerCamelCase__						:   Any=37						,lowerCamelCase__						:   Union[str, Any]=0.1						,lowerCamelCase__						:   List[str]=0.1						,lowerCamelCase__						:   Dict=20						,lowerCamelCase__						:   Dict=2						,lowerCamelCase__						:   List[str]=1						,lowerCamelCase__						:   Tuple=0						,)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						seq_length
           SCREAMING_SNAKE_CASE		=						is_training
           SCREAMING_SNAKE_CASE		=						use_labels
           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						intermediate_size

           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						max_position_embeddings
           SCREAMING_SNAKE_CASE		=						eos_token_id
           SCREAMING_SNAKE_CASE		=						pad_token_id
           SCREAMING_SNAKE_CASE		=						bos_token_id






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						ids_tensor([self.batch_size, self.seq_length - 1]						,self.vocab_size	).clip(3						,self.vocab_size	)
           SCREAMING_SNAKE_CASE		=						np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)						,1	)
           SCREAMING_SNAKE_CASE		=						np.concatenate([input_ids, eos_tensor]						,axis=1	)

           SCREAMING_SNAKE_CASE		=						ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size	)

           SCREAMING_SNAKE_CASE		=						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		=						prepare_pegasus_inputs_dict(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
           return config, inputs_dict






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   List[Any]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						20
           SCREAMING_SNAKE_CASE		=						model_class_name(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						model.encode(inputs_dict["""input_ids"""]	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

           SCREAMING_SNAKE_CASE		=						model.init_cache(decoder_input_ids.shape[0]						,lowerCamelCase__						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,dtype="""i4"""	)

           SCREAMING_SNAKE_CASE		=						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		=						model.decode(
               decoder_input_ids[:, :-1]						,lowerCamelCase__						,decoder_attention_mask=lowerCamelCase__						,past_key_values=lowerCamelCase__						,decoder_position_ids=lowerCamelCase__						,)

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

           SCREAMING_SNAKE_CASE		=						model.decode(lowerCamelCase__						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Tuple	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						20
           SCREAMING_SNAKE_CASE		=						model_class_name(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						model.encode(inputs_dict["""input_ids"""]	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

           SCREAMING_SNAKE_CASE		=						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		=						model.init_cache(decoder_input_ids.shape[0]						,lowerCamelCase__						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						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		=						model.decode(
               decoder_input_ids[:, :-1]						,lowerCamelCase__						,decoder_attention_mask=lowerCamelCase__						,past_key_values=lowerCamelCase__						,decoder_position_ids=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,dtype="""i4"""	)
           SCREAMING_SNAKE_CASE		=						model.decode(
               decoder_input_ids[:, -1:]						,lowerCamelCase__						,past_key_values=outputs_cache.past_key_values						,decoder_attention_mask=lowerCamelCase__						,decoder_position_ids=lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						model.decode(lowerCamelCase__						,lowerCamelCase__						,decoder_attention_mask=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						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       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None			,						)			->       List[str]:


      '''simple docstring'''


      if attention_mask is None:
            SCREAMING_SNAKE_CASE		=						np.not_equal(_SCREAMING_SNAKE_CASE			,						config.pad_token_id     ).astype(np.inta     )
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE		=						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 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      (
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     __snake_case   :     Dict		     =      (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     __snake_case   :     Optional[int]		     =      True
     __snake_case   :     str		     =      False
     __snake_case   :     List[Any]		     =      False
     __snake_case   :     Dict		     =      False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxPegasusModelTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Optional[Any]:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						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(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						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		=						self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

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

                       with self.subTest("""JIT Enabled"""	):
                             SCREAMING_SNAKE_CASE		=						encode_jitted(**lowerCamelCase__	).to_tuple()

                       with self.subTest("""JIT Disabled"""	):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE		=						encode_jitted(**lowerCamelCase__	).to_tuple()

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						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		=						model_class(lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						model.encode(inputs_dict["""input_ids"""]						,inputs_dict["""attention_mask"""]	)

                       SCREAMING_SNAKE_CASE		=						{
                           """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(lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   List[Any]	):
                             return model.decode(
                                 decoder_input_ids=lowerCamelCase__						,decoder_attention_mask=lowerCamelCase__						,encoder_outputs=lowerCamelCase__						,)

                       with self.subTest("""JIT Enabled"""	):
                             SCREAMING_SNAKE_CASE		=						decode_jitted(**lowerCamelCase__	).to_tuple()

                       with self.subTest("""JIT Disabled"""	):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE		=						decode_jitted(**lowerCamelCase__	).to_tuple()

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






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Optional[Any]:




           '''simple docstring'''


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE		=						model_class_name.from_pretrained("""google/pegasus-large"""						,from_pt=lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						np.ones((1, 1)	)
                 SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)
                 self.assertIsNotNone(lowerCamelCase__	)






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""	)
           SCREAMING_SNAKE_CASE		=						PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""	)

           SCREAMING_SNAKE_CASE		=						[
               """ 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		=						[
               """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		=						tokenizer(lowerCamelCase__						,return_tensors="""np"""						,truncation=lowerCamelCase__						,max_length=512						,padding=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model.generate(**lowerCamelCase__						,num_beams=2	).sequences
           SCREAMING_SNAKE_CASE		=						tokenizer.batch_decode(lowerCamelCase__						,skip_special_tokens=lowerCamelCase__	)
           assert tgt_text == decoded









from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_						       =	"""RegNetConfig"""

# Base docstring
SCREAMING_SNAKE_CASE_						       =	"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE_						       =	[1, 1_0_8_8, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_						       =	"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE_						       =	"""tabby, tabby cat"""

SCREAMING_SNAKE_CASE_						       =	[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   str						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 3						,lowerCamelCase__						:   int = 1						,lowerCamelCase__						:   int = 1						,lowerCamelCase__						:   Optional[str] = "relu"						,)					->		Union[str, Any]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						nn.Convad(
               lowerCamelCase__						,lowerCamelCase__						,kernel_size=lowerCamelCase__						,stride=lowerCamelCase__						,padding=kernel_size // 2						,groups=lowerCamelCase__						,bias=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						nn.BatchNormad(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						ACTaFN[activation] if activation is not None else nn.Identity()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   Tuple	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.convolution(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.normalization(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   RegNetConfig	)					->		List[str]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						RegNetConvLayer(
               config.num_channels						,config.embedding_size						,kernel_size=3						,stride=2						,activation=config.hidden_act	)
           SCREAMING_SNAKE_CASE		=						config.num_channels






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						pixel_values.shape[1]
           if num_channels != self.num_channels:
                 raise ValueError(
                     """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""	)
           SCREAMING_SNAKE_CASE		=						self.embedder(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 2	)					->		List[str]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1						,stride=lowerCamelCase__						,bias=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						nn.BatchNormad(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Tensor	)					->		Tensor:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.convolution(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.normalization(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int	)					->		int:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE		=						nn.AdaptiveAvgPoolad((1, 1)	)
           SCREAMING_SNAKE_CASE		=						nn.Sequential(
               nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1	)						,nn.ReLU()						,nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1	)						,nn.Sigmoid()						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Any	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.pooler(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.attention(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						hidden_state * attention
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 1	)					->		str:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						in_channels != out_channels or stride != 1
           SCREAMING_SNAKE_CASE		=						max(1						,out_channels // config.groups_width	)
           SCREAMING_SNAKE_CASE		=						(
               RegNetShortCut(lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__	) if should_apply_shortcut else nn.Identity()
           )
           SCREAMING_SNAKE_CASE		=						nn.Sequential(
               RegNetConvLayer(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1						,activation=config.hidden_act	)						,RegNetConvLayer(lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__						,groups=lowerCamelCase__						,activation=config.hidden_act	)						,RegNetConvLayer(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1						,activation=lowerCamelCase__	)						,)
           SCREAMING_SNAKE_CASE		=						ACTaFN[config.hidden_act]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Optional[Any]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						hidden_state
           SCREAMING_SNAKE_CASE		=						self.layer(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.shortcut(lowerCamelCase__	)
           hidden_state += residual
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   Dict						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 1	)					->		Optional[int]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						in_channels != out_channels or stride != 1
           SCREAMING_SNAKE_CASE		=						max(1						,out_channels // config.groups_width	)
           SCREAMING_SNAKE_CASE		=						(
               RegNetShortCut(lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__	) if should_apply_shortcut else nn.Identity()
           )
           SCREAMING_SNAKE_CASE		=						nn.Sequential(
               RegNetConvLayer(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1						,activation=config.hidden_act	)						,RegNetConvLayer(lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__						,groups=lowerCamelCase__						,activation=config.hidden_act	)						,RegNetSELayer(lowerCamelCase__						,reduced_channels=int(round(in_channels / 4	)	)	)						,RegNetConvLayer(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1						,activation=lowerCamelCase__	)						,)
           SCREAMING_SNAKE_CASE		=						ACTaFN[config.hidden_act]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Tuple	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						hidden_state
           SCREAMING_SNAKE_CASE		=						self.layer(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.shortcut(lowerCamelCase__	)
           hidden_state += residual
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 2						,lowerCamelCase__						:   int = 2						,)					->		Tuple:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE		=						RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

           SCREAMING_SNAKE_CASE		=						nn.Sequential(
               # downsampling is done in the first layer with stride of 2
               layer(
                   lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,stride=lowerCamelCase__						,)						,*[layer(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	) for _ in range(depth - 1	)]						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Union[str, Any]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.layers(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   List[Any]						,lowerCamelCase__						:   RegNetConfig	)					->		str:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						nn.ModuleList([]	)
           # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
           self.stages.append(
               RegNetStage(
                   lowerCamelCase__						,config.embedding_size						,config.hidden_sizes[0]						,stride=2 if config.downsample_in_first_stage else 1						,depth=config.depths[0]						,)	)
           SCREAMING_SNAKE_CASE		=						zip(config.hidden_sizes						,config.hidden_sizes[1:]	)
           for (in_channels, out_channels), depth in zip(lowerCamelCase__						,config.depths[1:]	):
                 self.stages.append(RegNetStage(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,depth=lowerCamelCase__	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Tensor						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   bool = True	)					->		BaseModelOutputWithNoAttention:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						() if output_hidden_states else None

           for stage_module in self.stages:
                 if output_hidden_states:
                       SCREAMING_SNAKE_CASE		=						hidden_states + (hidden_state,)

                 SCREAMING_SNAKE_CASE		=						stage_module(lowerCamelCase__	)

           if output_hidden_states:
                 SCREAMING_SNAKE_CASE		=						hidden_states + (hidden_state,)

           if not return_dict:
                 return tuple(v for v in [hidden_state, hidden_states] if v is not None	)

           return BaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase__						,hidden_states=lowerCamelCase__	)

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     List[Any]		     =      RegNetConfig
     __snake_case   :     Union[str, Any]		     =      "regnet"
     __snake_case   :     Optional[Any]		     =      "pixel_values"
     __snake_case   :     List[Any]		     =      True






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   int	)					->		Any:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,nn.Convad	):
                 nn.init.kaiming_normal_(module.weight						,mode="""fan_out"""						,nonlinearity="""relu"""	)
           elif isinstance(lowerCamelCase__						,(nn.BatchNormad, nn.GroupNorm)	):
                 nn.init.constant_(module.weight						,1	)
                 nn.init.constant_(module.bias						,0	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   str=False	)					->		str:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,lowerCamelCase__	):
                 SCREAMING_SNAKE_CASE		=						value
SCREAMING_SNAKE_CASE_						       =	r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

SCREAMING_SNAKE_CASE_						       =	r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."							,				lowerCAmelCase_							,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   str						,lowerCamelCase__						:   str	)					->		Any:




           '''simple docstring'''


           super().__init__(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						config
           SCREAMING_SNAKE_CASE		=						RegNetEmbeddings(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RegNetEncoder(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						nn.AdaptiveAvgPoolad((1, 1)	)
           # Initialize weights and apply final processing
           self.post_init()






     @add_start_docstrings_to_model_forward(lowerCamelCase__	)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC						,output_type=lowerCamelCase__						,config_class=_CONFIG_FOR_DOC						,modality="""vision"""						,expected_output=_EXPECTED_OUTPUT_SHAPE						,)
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Tensor						,lowerCamelCase__						:   Optional[bool] = None						,lowerCamelCase__						:   Optional[bool] = None	)					->		BaseModelOutputWithPoolingAndNoAttention:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           SCREAMING_SNAKE_CASE		=						return_dict if return_dict is not None else self.config.use_return_dict

           SCREAMING_SNAKE_CASE		=						self.embedder(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.encoder(
               lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,return_dict=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						encoder_outputs[0]

           SCREAMING_SNAKE_CASE		=						self.pooler(lowerCamelCase__	)

           if not return_dict:
                 return (last_hidden_state, pooled_output) + encoder_outputs[1:]

           return BaseModelOutputWithPoolingAndNoAttention(
               last_hidden_state=lowerCamelCase__						,pooler_output=lowerCamelCase__						,hidden_states=encoder_outputs.hidden_states						,)

@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "							,				lowerCAmelCase_							,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   Any						,lowerCamelCase__						:   Optional[int]	)					->		List[Any]:




           '''simple docstring'''


           super().__init__(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						config.num_labels
           SCREAMING_SNAKE_CASE		=						RegNetModel(lowerCamelCase__	)
           # classification head
           SCREAMING_SNAKE_CASE		=						nn.Sequential(
               nn.Flatten()						,nn.Linear(config.hidden_sizes[-1]						,config.num_labels	) if config.num_labels > 0 else nn.Identity()						,)
           # initialize weights and apply final processing
           self.post_init()






     @add_start_docstrings_to_model_forward(lowerCamelCase__	)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT						,output_type=lowerCamelCase__						,config_class=_CONFIG_FOR_DOC						,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Optional[torch.FloatTensor] = None						,lowerCamelCase__						:   Optional[torch.LongTensor] = None						,lowerCamelCase__						:   Optional[bool] = None						,lowerCamelCase__						:   Optional[bool] = None						,)					->		ImageClassifierOutputWithNoAttention:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						return_dict if return_dict is not None else self.config.use_return_dict

           SCREAMING_SNAKE_CASE		=						self.regnet(lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,return_dict=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						outputs.pooler_output if return_dict else outputs[1]

           SCREAMING_SNAKE_CASE		=						self.classifier(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						None

           if labels is not None:
                 if self.config.problem_type is None:
                       if self.num_labels == 1:
                             SCREAMING_SNAKE_CASE		=						"""regression"""
                       elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                             SCREAMING_SNAKE_CASE		=						"""single_label_classification"""
                       else:
                             SCREAMING_SNAKE_CASE		=						"""multi_label_classification"""
                 if self.config.problem_type == "regression":
                       SCREAMING_SNAKE_CASE		=						MSELoss()
                       if self.num_labels == 1:
                             SCREAMING_SNAKE_CASE		=						loss_fct(logits.squeeze()						,labels.squeeze()	)
                       else:
                             SCREAMING_SNAKE_CASE		=						loss_fct(lowerCamelCase__						,lowerCamelCase__	)
                 elif self.config.problem_type == "single_label_classification":
                       SCREAMING_SNAKE_CASE		=						CrossEntropyLoss()
                       SCREAMING_SNAKE_CASE		=						loss_fct(logits.view(-1						,self.num_labels	)						,labels.view(-1	)	)
                 elif self.config.problem_type == "multi_label_classification":
                       SCREAMING_SNAKE_CASE		=						BCEWithLogitsLoss()
                       SCREAMING_SNAKE_CASE		=						loss_fct(lowerCamelCase__						,lowerCamelCase__	)

           if not return_dict:
                 SCREAMING_SNAKE_CASE		=						(logits,) + outputs[2:]
                 return (loss,) + output if loss is not None else output

           return ImageClassifierOutputWithNoAttention(loss=lowerCamelCase__						,logits=lowerCamelCase__						,hidden_states=outputs.hidden_states	)


