

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

__UpperCAmelCase								=			torch.device("""cpu""")





def      snake_case_				()      ->     List[Any]:
   __lowerCAmelCase  :      int    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __lowerCAmelCase  :      Optional[int]    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   return im





def      snake_case_				(__A					:						Any					)      ->     List[Any]:
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]					)

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]					)

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]					)

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]					)





def      snake_case_				(__A					:						Optional[int] ,       __A					:						int ,       __A					:						Optional[Any]					)      ->     str:
   __lowerCAmelCase  :      int    =						dct.pop(__A					)
   __lowerCAmelCase  :      str    =						val





def      snake_case_				(__A					:						Optional[Any]					)      ->     Optional[int]:
   __lowerCAmelCase  :      List[str]    =						[]
   for k in state_dict.keys():
      __lowerCAmelCase  :      Optional[Any]    =						k
      if ".pwconv" in k:
         __lowerCAmelCase  :      List[Any]    =						k_new.replace(""".pwconv""" ,       """.point_wise_conv"""					)
      if ".dwconv" in k:
         __lowerCAmelCase  :      Union[str, Any]    =						k_new.replace(""".dwconv""" ,       """.depth_wise_conv"""					)
      if ".Proj." in k:
         __lowerCAmelCase  :      str    =						k_new.replace(""".Proj.""" ,       """.proj."""					)
      if "patch_embed" in k_new:
         __lowerCAmelCase  :      Tuple    =						k_new.replace("""patch_embed""" ,       """swiftformer.patch_embed.patch_embedding"""					)
      if "network" in k_new:
         __lowerCAmelCase  :      Optional[int]    =						k_new.split("""."""					)
         if ls[2].isdigit():
            __lowerCAmelCase  :      Union[str, Any]    =						"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]					)
         else:
            __lowerCAmelCase  :      Tuple    =						k_new.replace("""network""" ,       """swiftformer.encoder.network"""					)
      rename_keys.append((k, k_new)					)
   return rename_keys





@torch.no_grad()
def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						Tuple ,       __A					:						Union[str, Any]					)      ->     str:
   __lowerCAmelCase  :      int    =						SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   __lowerCAmelCase  :      Union[str, Any]    =						1_0_0_0
   __lowerCAmelCase  :      Dict    =						"""huggingface/label-files"""
   __lowerCAmelCase  :      List[str]    =						"""imagenet-1k-id2label.json"""
   __lowerCAmelCase  :      Union[str, Any]    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
   __lowerCAmelCase  :      str    =						{int(__A					): v for k, v in idalabel.items()}
   __lowerCAmelCase  :      Dict    =						idalabel
   __lowerCAmelCase  :      Union[str, Any]    =						{v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      __lowerCAmelCase  :      List[str]    =						[3, 3, 6, 4]
      __lowerCAmelCase  :      Union[str, Any]    =						[4_8, 5_6, 1_1_2, 2_2_0]

   elif swiftformer_name == "swiftformer_s":
      __lowerCAmelCase  :      Optional[Any]    =						[3, 3, 9, 6]
      __lowerCAmelCase  :      str    =						[4_8, 6_4, 1_6_8, 2_2_4]

   elif swiftformer_name == "swiftformer_l1":
      __lowerCAmelCase  :      Tuple    =						[4, 3, 1_0, 5]
      __lowerCAmelCase  :      Optional[Any]    =						[4_8, 9_6, 1_9_2, 3_8_4]

   elif swiftformer_name == "swiftformer_l3":
      __lowerCAmelCase  :      Union[str, Any]    =						[4, 4, 1_2, 6]
      __lowerCAmelCase  :      Union[str, Any]    =						[6_4, 1_2_8, 3_2_0, 5_1_2]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith("""https"""					):
         __lowerCAmelCase  :      Any    =						torch.hub.load_state_dict_from_url(__A ,       map_location="""cpu""" ,       check_hash=__A					)
      else:
         __lowerCAmelCase  :      str    =						torch.load(__A ,       map_location="""cpu"""					)
   __lowerCAmelCase  :      Dict    =						checkpoint

   __lowerCAmelCase  :      str    =						create_rename_keys(__A					)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(__A ,       __A ,       __A					)

   # load HuggingFace model
   __lowerCAmelCase  :      Dict    =						SwiftFormerForImageClassification(__A					).eval()
   hf_model.load_state_dict(__A					)

   # prepare test inputs
   __lowerCAmelCase  :      Union[str, Any]    =						prepare_img()
   __lowerCAmelCase  :      Union[str, Any]    =						ViTImageProcessor.from_pretrained("""preprocessor_config"""					)
   __lowerCAmelCase  :      List[str]    =						processor(images=__A ,       return_tensors="""pt"""					)

   # compare outputs from both models
   __lowerCAmelCase  :      Any    =						get_expected_output(__A					)
   __lowerCAmelCase  :      Optional[Any]    =						hf_model(inputs["""pixel_values"""]					).logits

   assert hf_logits.shape == torch.Size([1, 1_0_0_0]					)
   assert torch.allclose(hf_logits[0, 0:5] ,       __A ,       atol=1e-3					)

   Path(__A					).mkdir(exist_ok=__A					)
   print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''					)
   hf_model.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swiftformer_name""",
       default="""swiftformer_xs""",
       choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
       type=str,
       help="""Name of the SwiftFormer model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""./converted_outputs/""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

   __UpperCAmelCase								=			parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel



@require_tf
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=MBartConfig
  lowerCamelCase   :      Optional[Any]										={}
  lowerCamelCase   :      Dict										="gelu"







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






     """simple docstring"""







     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      int    =						batch_size
     __lowerCAmelCase  :      int    =						seq_length
     __lowerCAmelCase  :      Tuple    =						is_training
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Dict    =						num_hidden_layers
     __lowerCAmelCase  :      int    =						num_attention_heads
     __lowerCAmelCase  :      Any    =						intermediate_size
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[Any]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Tuple    =						max_position_embeddings
     __lowerCAmelCase  :      Union[str, Any]    =						eos_token_id
     __lowerCAmelCase  :      Optional[Any]    =						pad_token_id
     __lowerCAmelCase  :      int    =						bos_token_id







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size   )
     __lowerCAmelCase  :      Optional[Any]    =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )	,							1   )
     __lowerCAmelCase  :      int    =						tf.concat([input_ids, eos_tensor]	,							axis=1   )

     __lowerCAmelCase  :      Any    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						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	,							)
     __lowerCAmelCase  :      Tuple    =						prepare_mbart_inputs_dict(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						TFMBartModel(config=lowerCAmelCase   ).get_decoder()
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""input_ids"""]

     __lowerCAmelCase  :      Optional[Any]    =						input_ids[:1, :]
     __lowerCAmelCase  :      Union[str, Any]    =						inputs_dict["""attention_mask"""][:1, :]
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""head_mask"""]
     __lowerCAmelCase  :      Any    =						1

     # first forward pass
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							head_mask=lowerCAmelCase	,							use_cache=lowerCAmelCase   )

     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						outputs.to_tuple()
     __lowerCAmelCase  :      Union[str, Any]    =						past_key_values[1]






def      snake_case_				(__A					:						str ,       __A					:						Union[str, Any] ,       __A					:						Tuple ,       __A					:						Tuple=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[int]=None ,       __A					:						Optional[Any]=None ,       )      ->     int:
   if attention_mask is None:
      __lowerCAmelCase  :      Dict    =						tf.cast(tf.math.not_equal(__A ,       config.pad_token_id					) ,       tf.inta					)
   if decoder_attention_mask is None:
      __lowerCAmelCase  :      str    =						tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape ,       dtype=tf.inta					),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,       config.pad_token_id					) ,       tf.inta					),
          ] ,       axis=-1 ,       )
   if head_mask is None:
      __lowerCAmelCase  :      Tuple    =						tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      __lowerCAmelCase  :      List[Any]    =						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      __lowerCAmelCase  :      Any    =						tf.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,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }



@require_tf
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Optional[int]										=(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  lowerCamelCase   :      List[str]										=(TFMBartForConditionalGeneration,) if is_tf_available() else ()
  lowerCamelCase   :      Union[str, Any]										=(
      {
          "conversational": TFMBartForConditionalGeneration,
          "feature-extraction": TFMBartModel,
          "summarization": TFMBartForConditionalGeneration,
          "text2text-generation": TFMBartForConditionalGeneration,
          "translation": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase   :      str										=True
  lowerCamelCase   :      Tuple										=False
  lowerCamelCase   :      Dict										=False







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






     """simple docstring"""







     if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
        # Exception encountered when calling layer '...'
        return True

     return False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						TFMBartModelTester(self   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase   )







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






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase   )






@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=[
      " UN Chief Says There Is No Military Solution in Syria",
  ]
  lowerCamelCase   :      Tuple										=[
      "Şeful ONU declară că nu există o soluţie militară în Siria",
  ]
  lowerCamelCase   :      List[Any]										="facebook/mbart-large-en-ro"







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Any:






     """simple docstring"""







     return AutoTokenizer.from_pretrained(self.model_name   )







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
     return model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							**lowerCAmelCase			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.translate_src_text(**lowerCAmelCase   )
     self.assertListEqual(self.expected_text	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							**lowerCAmelCase			:   int   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.tokenizer(self.src_text	,							**lowerCAmelCase	,							return_tensors="""tf"""   )
     __lowerCAmelCase  :      Union[str, Any]    =						self.model.generate(
         model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2   )
     __lowerCAmelCase  :      List[str]    =						self.tokenizer.batch_decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   )
     return generated_words







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






     """simple docstring"""







     self._assert_generated_batch_equal_expected()


