

import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
           CTRLForSequenceClassification,
           CTRLLMHeadModel,
           CTRLModel,
       )



class       __snake_case  :

  def __init__(					self						,		_A						,		_A=14						,		_A=7						,		_A=True						,		_A=True						,		_A=True						,		_A=True						,		_A=True						,		_A=99						,		_A=32						,		_A=5						,		_A=4						,		_A=37						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=512						,		_A=16						,		_A=2						,		_A=0.0_2						,		_A=3						,		_A=4						,		_A=None						,		):
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						seq_length
      SCREAMING_SNAKE_CASE_									=						is_training
      SCREAMING_SNAKE_CASE_									=						use_token_type_ids
      SCREAMING_SNAKE_CASE_									=						use_input_mask
      SCREAMING_SNAKE_CASE_									=						use_labels
      SCREAMING_SNAKE_CASE_									=						use_mc_token_ids
      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_act
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						type_vocab_size
      SCREAMING_SNAKE_CASE_									=						type_sequence_label_size
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						num_labels
      SCREAMING_SNAKE_CASE_									=						num_choices
      SCREAMING_SNAKE_CASE_									=						scope
      SCREAMING_SNAKE_CASE_									=						self.vocab_size - 1

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size)

      SCREAMING_SNAKE_CASE_									=						None
      if self.use_input_mask:
          SCREAMING_SNAKE_CASE_									=						random_attention_mask([self.batch_size, self.seq_length])

      SCREAMING_SNAKE_CASE_									=						None
      if self.use_token_type_ids:
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.seq_length]						,		self.type_vocab_size)

      SCREAMING_SNAKE_CASE_									=						None
      if self.use_mc_token_ids:
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.num_choices]						,		self.seq_length)

      SCREAMING_SNAKE_CASE_									=						None
      SCREAMING_SNAKE_CASE_									=						None
      SCREAMING_SNAKE_CASE_									=						None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size]						,		self.type_sequence_label_size)
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.seq_length]						,		self.num_labels)
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size]						,		self.num_choices)

      SCREAMING_SNAKE_CASE_									=						self.get_config()

      SCREAMING_SNAKE_CASE_									=						ids_tensor([self.num_hidden_layers, self.num_attention_heads]						,		2)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )

  def 						lowerCAmelCase__     (					self):
      return CTRLConfig(
          vocab_size=self.vocab_size						,		n_embd=self.hidden_size						,		n_layer=self.num_hidden_layers						,		n_head=self.num_attention_heads						,		n_positions=self.max_position_embeddings						,		pad_token_id=self.pad_token_id						,		)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		*_A):
      SCREAMING_SNAKE_CASE_									=						CTRLModel(config=_A)
      model.to(_A)
      model.eval()

      model(_A						,		token_type_ids=_A						,		head_mask=_A)
      model(_A						,		token_type_ids=_A)
      SCREAMING_SNAKE_CASE_									=						model(_A)
      self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, self.seq_length, self.hidden_size))
      self.parent.assertEqual(len(result.past_key_values)						,		config.n_layer)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		*_A):
      SCREAMING_SNAKE_CASE_									=						CTRLLMHeadModel(_A)
      model.to(_A)
      model.eval()

      SCREAMING_SNAKE_CASE_									=						model(_A						,		token_type_ids=_A						,		labels=_A)
      self.parent.assertEqual(result.loss.shape						,		())
      self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.vocab_size))

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()

      (
          (
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			(
          SCREAMING_SNAKE_CASE_
      )							,			
      )									=						config_and_inputs

      SCREAMING_SNAKE_CASE_									=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

      return config, inputs_dict


  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		*_A):
      SCREAMING_SNAKE_CASE_									=						self.num_labels
      SCREAMING_SNAKE_CASE_									=						CTRLForSequenceClassification(_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size]						,		self.type_sequence_label_size)
      SCREAMING_SNAKE_CASE_									=						model(_A						,		token_type_ids=_A						,		labels=_A)
      self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels))






@require_torch
class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Dict						 =  (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
  __lowerCAmelCase     :	Dict						 =  (CTRLLMHeadModel,) if is_torch_available() else ()
  __lowerCAmelCase     :	Any						 =  (
      {
          'feature-extraction': CTRLModel,
          'text-classification': CTRLForSequenceClassification,
          'text-generation': CTRLLMHeadModel,
          'zero-shot': CTRLForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  __lowerCAmelCase     :	Tuple						 =  True
  __lowerCAmelCase     :	Dict						 =  False
  __lowerCAmelCase     :	str						 =  False

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A):
      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
          # config could not be created.
          return True

      return False

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						CTRLModelTester(self)
      SCREAMING_SNAKE_CASE_									=						ConfigTester(self						,		config_class=_A						,		n_embd=37)

  def 						lowerCAmelCase__     (					self):
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()

  def 						lowerCAmelCase__     (					self):
      self.config_tester.run_common_tests()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(*_A)

  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
  def 						lowerCAmelCase__     (					self):
      pass

  @slow
  def 						lowerCAmelCase__     (					self):
      for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          SCREAMING_SNAKE_CASE_									=						CTRLModel.from_pretrained(_A)
          self.assertIsNotNone(_A)


  @unittest.skip('The model doesn\'t support left padding')  # and it's not used enough to be worth fixing :)
  def 						lowerCAmelCase__     (					self):
      pass






@require_torch
class       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()


  @slow
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						CTRLLMHeadModel.from_pretrained('ctrl')
      model.to(_A)
      SCREAMING_SNAKE_CASE_									=						torch.tensor(
          [[11859, 0, 1611, 8]]						,		dtype=torch.long						,		device=_A)  # Legal the president is
      SCREAMING_SNAKE_CASE_									=						[
          11859,
          0,
          1611,
          8,
          5,
          150,
          26449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          20740,
          246533,
          246533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      SCREAMING_SNAKE_CASE_									=						model.generate(_A						,		do_sample=_A)
      self.assertListEqual(output_ids[0].tolist()						,		_A)





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__  :      Optional[int]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[Any]			=    torch.device("cpu")


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )
    return im


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						dct.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for k in state_dict.keys():
        SCREAMING_SNAKE_CASE_									=						k
        if ".pwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.pwconv' ,   '.point_wise_conv'       )
        if ".dwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
        if ".Proj." in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.Proj.' ,   '.proj.'       )
        if "patch_embed" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
        if "network" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.split('.'       )
            if ls[2].isdigit():
                SCREAMING_SNAKE_CASE_									=						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
            else:
                SCREAMING_SNAKE_CASE_									=						k_new.replace('network' ,   'swiftformer.encoder.network'       )
        rename_keys.append((k, k_new)       )
    return rename_keys


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    SCREAMING_SNAKE_CASE_									=						1_000
    SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
    SCREAMING_SNAKE_CASE_									=						'imagenet-1k-id2label.json'
    SCREAMING_SNAKE_CASE_									=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   repo_type='dataset'       ) ,   'r'       )       )
    SCREAMING_SNAKE_CASE_									=						{int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE_									=						idalabel
    SCREAMING_SNAKE_CASE_									=						{v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 6, 4]
        SCREAMING_SNAKE_CASE_									=						[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 9, 6]
        SCREAMING_SNAKE_CASE_									=						[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        SCREAMING_SNAKE_CASE_									=						[4, 3, 10, 5]
        SCREAMING_SNAKE_CASE_									=						[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        SCREAMING_SNAKE_CASE_									=						[4, 4, 12, 6]
        SCREAMING_SNAKE_CASE_									=						[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https'       ):
            SCREAMING_SNAKE_CASE_									=						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE ,   map_location='cpu' ,   check_hash=_SCREAMING_SNAKE_CASE       )
        else:
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE ,   map_location='cpu'       )
    SCREAMING_SNAKE_CASE_									=						checkpoint

    SCREAMING_SNAKE_CASE_									=						create_rename_keys(_SCREAMING_SNAKE_CASE       )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_									=						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE       ).eval()
    hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

    # prepare test inputs
    SCREAMING_SNAKE_CASE_									=						prepare_img()
    SCREAMING_SNAKE_CASE_									=						ViTImageProcessor.from_pretrained('preprocessor_config'       )
    SCREAMING_SNAKE_CASE_									=						processor(images=_SCREAMING_SNAKE_CASE ,   return_tensors='pt'       )

    # compare outputs from both models
    SCREAMING_SNAKE_CASE_									=						get_expected_output(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						hf_model(inputs['pixel_values']       ).logits

    assert hf_logits.shape == torch.Size([1, 1_000]       )
    assert torch.allclose(hf_logits[0, 0:5] ,   _SCREAMING_SNAKE_CASE ,   atol=1E-3       )

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


if __name__ == "__main__":
       UpperCamelCase__  :      str			=    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__  :      Union[str, Any]			=    parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


