



"""simple docstring"""




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
     from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor


class 	a__		(    lowercase_  ):


   def 							lowercase					(				self   :				int      )     ->							Dict:
         lowercase	:   Optional[int]											= self.config_class(**self.inputs_dict      )
         self.parent.assertTrue(hasattr(_lowerCAmelCase,  'hidden_sizes'      )      )
         self.parent.assertTrue(hasattr(_lowerCAmelCase,  'num_encoder_blocks'      )      )




class 	a__		:


   def __init__(				self   :				Tuple,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				Union[str, Any]=13,  lowerCAmelCase   :				Any=64,  lowerCAmelCase   :				int=3,  lowerCAmelCase   :				Optional[Any]=4,  lowerCAmelCase   :				Optional[int]=[2, 2, 2, 2],  lowerCAmelCase   :				Optional[Any]=[8, 4, 2, 1],  lowerCAmelCase   :				Union[str, Any]=[16, 32, 64, 128],  lowerCAmelCase   :				Optional[int]=[1, 4, 8, 16],  lowerCAmelCase   :				Union[str, Any]=False,  lowerCAmelCase   :				Dict=True,  lowerCAmelCase   :				str="gelu",  lowerCAmelCase   :				str=0.1,  lowerCAmelCase   :				Tuple=0.02,  lowerCAmelCase   :				Dict=3,  lowerCAmelCase   :				List[str]=None,  )     ->							Tuple:
         lowercase	:   int											= parent
         lowercase	:   Optional[int]											= batch_size
         lowercase	:   Union[str, Any]											= image_size
         lowercase	:   Tuple											= num_channels
         lowercase	:   str											= num_encoder_blocks
         lowercase	:   str											= sr_ratios
         lowercase	:   Tuple											= depths
         lowercase	:   List[Any]											= hidden_sizes
         lowercase	:   Dict											= downsampling_rates
         lowercase	:   Any											= is_training
         lowercase	:   Tuple											= use_labels
         lowercase	:   Any											= hidden_act
         lowercase	:   List[Any]											= hidden_dropout_prob
         lowercase	:   Dict											= initializer_range
         lowercase	:   Tuple											= num_labels
         lowercase	:   str											= scope


   def 							lowercase					(				self   :				str      )     ->							Optional[int]:
         lowercase	:   Any											= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         lowercase	:   Optional[Any]											= None
         if self.use_labels:
               lowercase	:   str											= ids_tensor([self.batch_size, self.image_size, self.image_size],  self.num_labels      )

         lowercase	:   Optional[Any]											= PoolFormerConfig(
             image_size=self.image_size,  num_channels=self.num_channels,  num_encoder_blocks=self.num_encoder_blocks,  depths=self.depths,  hidden_sizes=self.hidden_sizes,  hidden_act=self.hidden_act,  hidden_dropout_prob=self.hidden_dropout_prob,  initializer_range=self.initializer_range,  )

         return config, pixel_values, labels


   def 							lowercase					(				self   :				int,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				Dict      )     ->							Any:
         lowercase	:   List[Any]											= PoolFormerModel(config=_lowerCAmelCase      )
         model.to(_lowerCAmelCase      )
         model.eval()
         lowercase	:   Union[str, Any]											= model(_lowerCAmelCase      )
         lowercase	:   Dict											= self.image_size // 32.0
         self.parent.assertEqual(
             result.last_hidden_state.shape,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)      )






   def 							lowercase					(				self   :				str      )     ->							Optional[Any]:
         lowercase	:   Optional[Any]											= self.prepare_config_and_inputs()
         lowercase						,     lowercase						,     lowercase	:   Dict											= config_and_inputs
         lowercase	:   List[str]											= {'pixel_values': pixel_values}
         return config, inputs_dict




@require_torch
class 	a__		(    lowercase_,	lowercase_,	unittest.TestCase  ):
   _lowerCamelCase       				=					(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
   _lowerCamelCase       				=					(
       {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False


   def 							lowercase					(				self   :				Any      )     ->							str:
         lowercase	:   Union[str, Any]											= PoolFormerModelTester(self      )
         lowercase	:   Union[str, Any]											= PoolFormerConfigTester(self,  config_class=_lowerCAmelCase      )


   def 							lowercase					(				self   :				Union[str, Any]      )     ->							Dict:
         self.config_tester.run_common_tests()


   def 							lowercase					(				self   :				Tuple      )     ->							Union[str, Any]:
         lowercase	:   str											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase      )


   @unittest.skip('PoolFormer does not use inputs_embeds'      )
   def 							lowercase					(				self   :				Any      )     ->							List[Any]:
         pass


   @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'      )
   def 							lowercase					(				self   :				Optional[int]      )     ->							int:
         pass


   def 							lowercase					(				self   :				str      )     ->							str:
         def check_hidden_states_output(lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				str      ):
               lowercase	:   List[str]											= model_class(_lowerCAmelCase      )
               model.to(_lowerCAmelCase      )
               model.eval()

               with torch.no_grad():
                     lowercase	:   List[str]											= model(**self._prepare_for_class(_lowerCAmelCase,  _lowerCAmelCase      )      )

               lowercase	:   List[Any]											= outputs.hidden_states

               lowercase	:   Dict											= self.model_tester.num_encoder_blocks
               self.assertEqual(len(_lowerCAmelCase      ),  _lowerCAmelCase      )

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]      ),  [
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ],  )

         lowercase						,     lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase	:   Any											= True
               check_hidden_states_output(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase      )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowercase	:   Optional[Any]											= True

               check_hidden_states_output(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase      )


   def 							lowercase					(				self   :				int      )     ->							Any:
         if not self.model_tester.is_training:
               return

         lowercase						,     lowercase	:   Optional[int]											= self.model_tester.prepare_config_and_inputs_for_common()
         lowercase	:   Union[str, Any]											= True

         for model_class in self.all_model_classes:
               if model_class in get_values(_lowerCAmelCase      ):
                     continue
               lowercase	:   Optional[Any]											= model_class(_lowerCAmelCase      )
               model.to(_lowerCAmelCase      )
               model.train()
               lowercase	:   Dict											= self._prepare_for_class(_lowerCAmelCase,  _lowerCAmelCase,  return_labels=_lowerCAmelCase      )
               lowercase	:   Optional[Any]											= model(**_lowerCAmelCase      ).loss
               loss.backward()


   def 							lowercase					(				self   :				List[Any]      )     ->							List[Any]:
         lowercase						,     lowercase	:   List[Any]											= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase	:   Tuple											= model_class(_lowerCAmelCase      )
               lowercase	:   List[str]											= inspect.signature(model.forward      )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase	:   str											= [*signature.parameters.keys()]

               lowercase	:   Dict											= ['pixel_values']
               self.assertListEqual(arg_names[:1],  _lowerCAmelCase      )






   @slow
   def 							lowercase					(				self   :				Optional[Any]      )     ->							int:
         for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase	:   Any											= PoolFormerModel.from_pretrained(_lowerCAmelCase      )
               self.assertIsNotNone(_lowerCAmelCase      )




def 	lowercase__  (      )  ->      List[str]:






      '''simple docstring'''


      lowercase	:   Any											= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
      return image





@require_torch
class 	a__		(    unittest.TestCase  ):


   @slow
   def 							lowercase					(				self   :				Dict      )     ->							str:
         lowercase	:   Optional[Any]											= PoolFormerImageProcessor()
         lowercase	:   Any											= PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'      ).to(_lowerCAmelCase      )

         lowercase	:   List[str]											= image_processor(images=prepare_img(),  return_tensors='pt'      ).to(_lowerCAmelCase      )

         # forward pass
         with torch.no_grad():
               lowercase	:   Optional[int]											= model(**_lowerCAmelCase      )

         # verify the logits
         lowercase	:   List[Any]											= torch.Size((1, 1000)      )
         self.assertEqual(outputs.logits.shape,  _lowerCAmelCase      )

         lowercase	:   Optional[int]											= torch.tensor([-0.6113, 0.1685, -0.0492]      ).to(_lowerCAmelCase      )
         self.assertTrue(torch.allclose(outputs.logits[0, :3],  _lowerCAmelCase,  atol=1e-4      )      )







import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
						raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
A_        =  logging.get_logger(__name__)

A_        =  "Hello world! cécé herlolip"





def  __UpperCamelCase   (     a,  a,  a)			->Any:
					lowerCamelCase__      =							FairseqRobertaModel.from_pretrained(a)
					roberta.eval()  # disable dropout
					lowerCamelCase__      =							roberta.model.encoder.sentence_encoder
					lowerCamelCase__      =							RobertaConfig(
					    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,  hidden_size=roberta.args.encoder_embed_dim,  num_hidden_layers=roberta.args.encoder_layers,  num_attention_heads=roberta.args.encoder_attention_heads,  intermediate_size=roberta.args.encoder_ffn_embed_dim,  max_position_embeddings=514,  type_vocab_size=1,  layer_norm_eps=1E-5,  )
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:",  a)

					lowerCamelCase__      =							RobertaForSequenceClassification(a) if classification_head else RobertaForMaskedLM(a)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowerCamelCase__      =							roberta_sent_encoder.embed_tokens.weight
					lowerCamelCase__      =							roberta_sent_encoder.embed_positions.weight
					lowerCamelCase__      =							torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.
					lowerCamelCase__      =							roberta_sent_encoder.emb_layer_norm.weight
					lowerCamelCase__      =							roberta_sent_encoder.emb_layer_norm.bias

					for i in range(config.num_hidden_layers):
										# Encoder: start of layer
										lowerCamelCase__      =							model.roberta.encoder.layer[i]
										lowerCamelCase__      =							roberta_sent_encoder.layers[i]

										# self attention
										lowerCamelCase__      =							layer.attention.self
										assert (
										    roberta_layer.self_attn.k_proj.weight.data.shape
										    == roberta_layer.self_attn.q_proj.weight.data.shape
										    == roberta_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size))
										)

										lowerCamelCase__      =							roberta_layer.self_attn.q_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.q_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.bias

										# self-attention output
										lowerCamelCase__      =							layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										lowerCamelCase__      =							roberta_layer.self_attn.out_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.out_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn_layer_norm.weight
										lowerCamelCase__      =							roberta_layer.self_attn_layer_norm.bias

										# intermediate
										lowerCamelCase__      =							layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										lowerCamelCase__      =							roberta_layer.fca.weight
										lowerCamelCase__      =							roberta_layer.fca.bias

										# output
										lowerCamelCase__      =							layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										lowerCamelCase__      =							roberta_layer.fca.weight
										lowerCamelCase__      =							roberta_layer.fca.bias
										lowerCamelCase__      =							roberta_layer.final_layer_norm.weight
										lowerCamelCase__      =							roberta_layer.final_layer_norm.bias
										# end of layer

					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].dense.weight
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].dense.bias
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.bias
					else:
										# LM Head
										lowerCamelCase__      =							roberta.model.encoder.lm_head.dense.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.dense.bias
										lowerCamelCase__      =							roberta.model.encoder.lm_head.layer_norm.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.layer_norm.bias
										lowerCamelCase__      =							roberta.model.encoder.lm_head.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					lowerCamelCase__      =							roberta.encode(a).unsqueeze(0)  # batch of size 1

					lowerCamelCase__      =							model(a)[0]
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"](roberta.extract_features(a))
					else:
										lowerCamelCase__      =							roberta.model(a)[0]
					print(our_output.shape,  their_output.shape)
					lowerCamelCase__      =							torch.max(torch.abs(our_output - their_output)).item()
					print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
					lowerCamelCase__      =							torch.allclose(a,  a,  atol=1E-3)
					print("Do both models output the same tensors?",  "🔥" if success else "💩")
					if not success:
										raise Exception("Something went wRoNg")

					pathlib.Path(a).mkdir(parents=a,  exist_ok=a)
					print(f"Saving model to {pytorch_dump_folder_path}")
					model.save_pretrained(a)


if __name__ == "__main__":
						A_        =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--classification_head", action="store_true", help="Whether to convert a final classification head."
						)
						A_        =  parser.parse_args()
						convert_roberta_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)

