






"""simple docstring"""




import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
UpperCamelCase											=     logging.get_logger(__name__)

UpperCamelCase											=     """Hello world! cécé herlolip"""






def 							lowerCAmelCase			(   UpperCamelCase_:				str  ,			UpperCamelCase_:				str  ,			UpperCamelCase_:				bool       )							->				Tuple:







  '''simple docstring'''







  _a      ,						_a					      =						os.path.split(UpperCamelCase_       )
  _a					      =						DataaVecTextModel.from_pretrained(
      UpperCamelCase_  ,			checkpoint_file=UpperCamelCase_       )
  dataavec.eval()  # disable dropout
  _a					      =						dataavec.models[0]
  _a					      =						dataavec_model.encoder.sentence_encoder
  _a					      =						DataaVecTextConfig(
      vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings  ,			hidden_size=dataavec_model.args.encoder_embed_dim  ,			num_hidden_layers=dataavec_model.args.encoder_layers  ,			num_attention_heads=dataavec_model.args.encoder_attention_heads  ,			intermediate_size=dataavec_model.args.encoder_ffn_embed_dim  ,			max_position_embeddings=514  ,			type_vocab_size=1  ,			layer_norm_eps=1E-5  ,			)
  if classification_head:
    _a					      =						dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
  print("Our BERT config:"  ,			UpperCamelCase_       )

  _a					      =						DataaVecTextForSequenceClassification(UpperCamelCase_       ) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase_       )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  _a					      =						dataavec_sent_encoder.embed_tokens.weight
  _a					      =						dataavec_sent_encoder.embed_positions.weight
  _a					      =						torch.zeros_like(
      model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
  _a					      =						dataavec_sent_encoder.layernorm_embedding.weight
  _a					      =						dataavec_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers       ):
    # Encoder: start of layer
    _a					      =						model.dataavec_text.encoder.layer[i]
    _a					      =						dataavec_sent_encoder.layers[i]

    # self attention
    _a					      =						layer.attention.self
    assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)       ), (
        "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
        f''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
    )
    assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)       ), (
        "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
        f''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
    )
    assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)       ), (
        "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
        f''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
    )

    _a					      =						dataavec_layer.self_attn.q_proj.weight
    _a					      =						dataavec_layer.self_attn.q_proj.bias
    _a					      =						dataavec_layer.self_attn.k_proj.weight
    _a					      =						dataavec_layer.self_attn.k_proj.bias
    _a					      =						dataavec_layer.self_attn.v_proj.weight
    _a					      =						dataavec_layer.self_attn.v_proj.bias

    # self-attention output
    _a					      =						layer.attention.output
    assert (
        self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
    ), f'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
    _a					      =						dataavec_layer.self_attn.out_proj.weight
    _a					      =						dataavec_layer.self_attn.out_proj.bias
    _a					      =						dataavec_layer.self_attn_layer_norm.weight
    _a					      =						dataavec_layer.self_attn_layer_norm.bias

    # intermediate
    _a					      =						layer.intermediate
    assert (
        intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
    ), f'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
    _a					      =						dataavec_layer.fca.weight
    _a					      =						dataavec_layer.fca.bias

    # output
    _a					      =						layer.output
    assert (
        bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
    ), f'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
    _a					      =						dataavec_layer.fca.weight
    _a					      =						dataavec_layer.fca.bias
    _a					      =						dataavec_layer.final_layer_norm.weight
    _a					      =						dataavec_layer.final_layer_norm.bias
    # end of layer

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

  # Let's check that we get the same results.
  _a					      =						dataavec.encode(UpperCamelCase_       ).unsqueeze(0       )  # batch of size 1

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

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


if __name__ == "__main__":
     UpperCamelCase											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--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."""
     )
     UpperCamelCase											=     parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )










"""simple docstring"""




import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
     from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor




class  lowercase_     :






      def __init__(     self  ,  a_  ,  a_=1_3  ,  a_=3_2  ,  a_=1  ,  a_=3  ,  a_=1_6  ,  a_=[1, 2, 1]  ,  a_=[2, 2, 4]  ,  a_=2  ,  a_=2.0  ,  a_=True  ,  a_=0.0  ,  a_=0.0  ,  a_=0.1  ,  a_="gelu"  ,  a_=False  ,  a_=True  ,  a_=0.02  ,  a_=1E-5  ,  a_=True  ,  a_=None  ,  a_=False  ,  a_=2  ,  )						->str:







        '''simple docstring'''


        _a					      =						parent
        _a					      =						batch_size
        _a					      =						image_size
        _a					      =						patch_size
        _a					      =						num_channels
        _a					      =						embed_dim
        _a					      =						depths
        _a					      =						num_heads
        _a					      =						window_size
        _a					      =						mlp_ratio
        _a					      =						qkv_bias
        _a					      =						hidden_dropout_prob
        _a					      =						attention_probs_dropout_prob
        _a					      =						drop_path_rate
        _a					      =						hidden_act
        _a					      =						use_absolute_embeddings
        _a					      =						patch_norm
        _a					      =						layer_norm_eps
        _a					      =						initializer_range
        _a					      =						is_training
        _a					      =						scope
        _a					      =						use_labels
        _a					      =						upscale

        # here we set some attributes to make tests pass
        _a					      =						len(a_						)
        _a					      =						embed_dim
        _a					      =						(image_size // patch_size) ** 2






      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        _a					      =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        _a					      =						None
        if self.use_labels:
          _a					      =						ids_tensor([self.batch_size]  ,  self.type_sequence_label_size						)

        _a					      =						self.get_config()

        return config, pixel_values, labels






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







        '''simple docstring'''


        return SwinaSRConfig(
            image_size=self.image_size  ,  patch_size=self.patch_size  ,  num_channels=self.num_channels  ,  embed_dim=self.embed_dim  ,  depths=self.depths  ,  num_heads=self.num_heads  ,  window_size=self.window_size  ,  mlp_ratio=self.mlp_ratio  ,  qkv_bias=self.qkv_bias  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  drop_path_rate=self.drop_path_rate  ,  hidden_act=self.hidden_act  ,  use_absolute_embeddings=self.use_absolute_embeddings  ,  path_norm=self.patch_norm  ,  layer_norm_eps=self.layer_norm_eps  ,  initializer_range=self.initializer_range  ,  upscale=self.upscale  ,  )






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_						)						->Union[str, Any]:







        '''simple docstring'''


        _a					      =						SwinaSRModel(config=a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        self.parent.assertEqual(
            result.last_hidden_state.shape  ,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)						)






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						SwinaSRForImageSuperResolution(a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        _a					      =						self.image_size * self.upscale
        self.parent.assertEqual(
            result.reconstruction.shape  ,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)						)






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a					      =						self.prepare_config_and_inputs()
        _a      ,						_a      ,						_a					      =						config_and_inputs
        _a					      =						{"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class  lowercase_     (_UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase  ):
      A__						:		str							      =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      A__						:		Optional[Any]							      =					{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

      A__						:		List[str]							      =					False
      A__						:		Optional[Any]							      =					False
      A__						:		str							      =					False
      A__						:		Optional[int]							      =					False
      A__						:		List[Any]							      =					False






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







        '''simple docstring'''


        _a					      =						SwinaSRModelTester(self						)
        _a					      =						ConfigTester(self  ,  config_class=a_  ,  embed_dim=3_7						)






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







        '''simple docstring'''


        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()






      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_						)






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







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_super_resolution(*a_						)






      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."						)
      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not use inputs_embeds"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        pass






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _a					      =						model_class(a_						)
          self.assertIsInstance(model.get_input_embeddings()  ,  (nn.Module)						)
          _a					      =						model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(a_  ,  nn.Linear						)						)






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

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

          _a					      =						["pixel_values"]
          self.assertListEqual(arg_names[:1]  ,  a_						)






      @slow
      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a					      =						SwinaSRModel.from_pretrained(a_						)
          self.assertIsNotNone(a_						)






      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        _a					      =						_config_zero_init(a_						)
        for model_class in self.all_model_classes:
          _a					      =						model_class(config=a_						)
          for name, param in model.named_parameters():
            if "logit_scale" in name:
              continue
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()  ,  [0.0, 1.0]  ,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,  )






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()
        _a					      =						True

        for model_class in self.all_model_classes:
          _a					      =						True
          _a					      =						False
          _a					      =						True
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)
          _a					      =						outputs.attentions
          _a					      =						len(self.model_tester.depths						)
          self.assertEqual(len(a_						)  ,  a_						)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a					      =						True
          _a					      =						config.window_size**2
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)
          _a					      =						outputs.attentions
          self.assertEqual(len(a_						)  ,  a_						)

          self.assertListEqual(
              list(attentions[0].shape[-3:]						)  ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,  )
          _a					      =						len(a_						)

          # Check attention is always last and order is fine
          _a					      =						True
          _a					      =						True
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)

          self.assertEqual(out_len + 1  ,  len(a_						)						)

          _a					      =						outputs.attentions

          self.assertEqual(len(a_						)  ,  a_						)

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]						)  ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,  )







@require_vision
@require_torch
@slow
class  lowercase_     (unittest.TestCase  ):






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a					      =						SwinaSRImageProcessor()
        _a					      =						SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"						).to(a_						)

        _a					      =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        _a					      =						processor(images=a_  ,  return_tensors="pt"						).to(a_						)

        # forward pass
        with torch.no_grad():
          _a					      =						model(**a_						)

        # verify the logits
        _a					      =						torch.Size([1, 3, 9_7_6, 1_2_9_6]						)
        self.assertEqual(outputs.reconstruction.shape  ,  a_						)
        _a					      =						torch.tensor(
            [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]						).to(a_						)
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,  a_  ,  atol=1E-4						)						)


