


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
       import tensorflow as tf

       from transformers.models.ctrl.modeling_tf_ctrl import (
           TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFCTRLForSequenceClassification,
           TFCTRLLMHeadModel,
           TFCTRLModel,
       )







class        UpperCAmelCase       (				__lowerCAmelCase):

 def __init__(       self       :		Union[str, Any],    a_       :		str,    ):





    """simple docstring"""



    UpperCamelCase__         =  parent
    UpperCamelCase__         =  13
    UpperCamelCase__         =  7
    UpperCamelCase__         =  True
    UpperCamelCase__         =  True
    UpperCamelCase__         =  True
    UpperCamelCase__         =  True
    UpperCamelCase__         =  True
    UpperCamelCase__         =  99
    UpperCamelCase__         =  32
    UpperCamelCase__         =  2
    UpperCamelCase__         =  4
    UpperCamelCase__         =  37
    UpperCamelCase__         =  "gelu"
    UpperCamelCase__         =  0.1
    UpperCamelCase__         =  0.1
    UpperCamelCase__         =  512
    UpperCamelCase__         =  16
    UpperCamelCase__         =  2
    UpperCamelCase__         =  0.02
    UpperCamelCase__         =  3
    UpperCamelCase__         =  4
    UpperCamelCase__         =  None
    UpperCamelCase__         =  self.vocab_size - 1

 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



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

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

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

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

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

    UpperCamelCase__         =  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,    )

    UpperCamelCase__         =  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 						lowercase_  (       self       :		List[Any],    a_       :		Union[str, Any],    a_       :		Optional[Any],    a_       :		Any,    a_       :		str,    a_       :		str,    *a_       :		int    ):





    """simple docstring"""



    UpperCamelCase__         =  TFCTRLModel(config=__SCREAMING_SNAKE_CASE    )
    UpperCamelCase__         =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
    UpperCamelCase__         =  model(__SCREAMING_SNAKE_CASE    )

    UpperCamelCase__         =  [input_ids, None, input_mask]  # None is the input for 'past'
    UpperCamelCase__         =  model(__SCREAMING_SNAKE_CASE    )

    UpperCamelCase__         =  model(__SCREAMING_SNAKE_CASE    )

    self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)    )

 def 						lowercase_  (       self       :		List[str],    a_       :		Union[str, Any],    a_       :		Optional[Any],    a_       :		Tuple,    a_       :		int,    a_       :		Union[str, Any],    *a_       :		List[str]    ):





    """simple docstring"""



    UpperCamelCase__         =  TFCTRLLMHeadModel(config=__SCREAMING_SNAKE_CASE    )
    UpperCamelCase__         =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
    UpperCamelCase__         =  model(__SCREAMING_SNAKE_CASE    )
    self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.vocab_size)    )

 def 						lowercase_  (       self       :		List[str],    a_       :		Tuple,    a_       :		Tuple,    a_       :		Optional[Any],    a_       :		Any,    a_       :		Optional[int],    *a_       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  self.num_labels
    UpperCamelCase__         =  ids_tensor([self.batch_size],    self.type_sequence_label_size    )
    UpperCamelCase__         =  {
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
        "labels": sequence_labels,
    }
    UpperCamelCase__         =  TFCTRLForSequenceClassification(__SCREAMING_SNAKE_CASE    )
    UpperCamelCase__         =  model(__SCREAMING_SNAKE_CASE    )
    self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)    )

 def 						lowercase_  (       self       :		List[str]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.prepare_config_and_inputs()

    (
        (
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						(
        UpperCamelCase__      
    )  ,						      
    )         =  config_and_inputs

    UpperCamelCase__         =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
    return config, inputs_dict







@require_tf
class        UpperCAmelCase       (				__lowerCAmelCase						,	__lowerCAmelCase						,	unittest.TestCase):
 _lowerCamelCase       :    List[Any]			    =    (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
 _lowerCamelCase       :    Union[str, Any]			    =    (TFCTRLLMHeadModel,) if is_tf_available() else ()
 _lowerCamelCase       :    Optional[Any]			    =    (
     {
         """feature-extraction""": TFCTRLModel,
         """text-classification""": TFCTRLForSequenceClassification,
         """text-generation""": TFCTRLLMHeadModel,
         """zero-shot""": TFCTRLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 _lowerCamelCase       :    Optional[Any]			    =    False
 _lowerCamelCase       :    Optional[int]			    =    False

 def 						lowercase_  (       self       :		List[Any],    a_       :		Optional[int],    a_       :		Any,    a_       :		Dict,    a_       :		Union[str, Any],    a_       :		Dict    ):





    """simple docstring"""



    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 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  TFCTRLModelTester(self    )
    UpperCamelCase__         =  ConfigTester(self,    config_class=__SCREAMING_SNAKE_CASE,    n_embd=37    )

 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    self.config_tester.run_common_tests()

 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_model(*__SCREAMING_SNAKE_CASE    )

 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_lm_head(*__SCREAMING_SNAKE_CASE    )

 def 						lowercase_  (       self       :		Union[str, Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_for_sequence_classification(*__SCREAMING_SNAKE_CASE    )

 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    UpperCamelCase__  ,						UpperCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__         =  [TFCTRLLMHeadModel]
    UpperCamelCase__         =  [TFCTRLForSequenceClassification]

    for model_class in self.all_model_classes:
       UpperCamelCase__         =  model_class(__SCREAMING_SNAKE_CASE    )
       model.build()  # may be needed for the get_bias() call below
       assert isinstance(model.get_input_embeddings(),    tf.keras.layers.Layer    )

       if model_class in list_lm_models:
          UpperCamelCase__         =  model.get_output_embeddings()
          assert isinstance(__SCREAMING_SNAKE_CASE,    tf.keras.layers.Layer    )
          UpperCamelCase__         =  model.get_bias()
          assert isinstance(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE    )
          for k, v in name.items():
             assert isinstance(__SCREAMING_SNAKE_CASE,    tf.Variable    )
       elif model_class in list_other_models_with_output_ebd:
          UpperCamelCase__         =  model.get_output_embeddings()
          assert isinstance(__SCREAMING_SNAKE_CASE,    tf.keras.layers.Layer    )
          UpperCamelCase__         =  model.get_bias()
          assert name is None
       else:
          UpperCamelCase__         =  model.get_output_embeddings()
          assert x is None
          UpperCamelCase__         =  model.get_bias()
          assert name is None

 @slow
 def 						lowercase_  (       self       :		Tuple    ):





    """simple docstring"""



    for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__         =  TFCTRLModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
       self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )







@require_tf
class        UpperCAmelCase       (				unittest.TestCase):

 @slow
 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    UpperCamelCase__         =  TFCTRLLMHeadModel.from_pretrained("ctrl"    )
    UpperCamelCase__         =  tf.convert_to_tensor([[1_1859, 0, 1611, 8]],    dtype=tf.intaa    )  # Legal the president is
    UpperCamelCase__         =  [
        1_1859,
        0,
        1611,
        8,
        5,
        150,
        2_6449,
        2,
        19,
        348,
        469,
        3,
        2595,
        48,
        2_0740,
        24_6533,
        24_6533,
        19,
        30,
        5,
    ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

    UpperCamelCase__         =  model.generate(__SCREAMING_SNAKE_CASE,    do_sample=__SCREAMING_SNAKE_CASE    )
    self.assertListEqual(output_ids[0].numpy().tolist(),    __SCREAMING_SNAKE_CASE    )






"""simple docstring"""


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
a__    :   Any    						= logging.get_logger(__name__)



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							List[nn.Module] 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							list 							=   field(default_factory=__lowerCAmelCase							)

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= len(list(m.modules()   )   ) == 1 or isinstance(__SCREAMING_SNAKE_CASE      ,    nn.Convad   ) or isinstance(__SCREAMING_SNAKE_CASE      ,    nn.BatchNormad   )
        if has_not_submodules:
         if not isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          self.traced.append(__SCREAMING_SNAKE_CASE   )

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook   )   )
        self.module(__SCREAMING_SNAKE_CASE   )
        [x.remove() for x in self.handles]
        return self

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return list(filter(lambda __SCREAMING_SNAKE_CASE   : len(list(x.state_dict().keys()   )   ) > 0      ,    self.traced   )   )



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							nn.Module
       snake_case__ :							int 							=   0
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= Tracker(self.dest   )(__SCREAMING_SNAKE_CASE   ).parametrized
        snake_case__										= Tracker(self.src   )(__SCREAMING_SNAKE_CASE   ).parametrized

        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.src_skip      ,    __SCREAMING_SNAKE_CASE   )   )
        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.dest_skip      ,    __SCREAMING_SNAKE_CASE   )   )

        if len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
         raise Exception(
             F"Numbers of operations are different. Source module has {len(__SCREAMING_SNAKE_CASE   )} operations while"
             F" destination module has {len(__SCREAMING_SNAKE_CASE   )}."   )

        for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
         dest_m.load_state_dict(src_m.state_dict()   )
         if self.verbose == 1:
          print(F"Transfered from={src_m} to={dest_m}"   )





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     nn.Module:

 '''simple docstring'''






 snake_case__										= from_model.state_dict()
 snake_case__										= our_model.state_dict()
 snake_case__										= our_model.config
 snake_case__										= []
 for stage_idx in range(len(config.hidden_sizes		)		):
  for block_id in range(config.depths[stage_idx]		):
   snake_case__										= f"block{stage_idx + 1}.{block_id}.layer_scale_1"
   snake_case__										= f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

   all_keys.append((from_key, to_key)		)
   snake_case__										= f"block{stage_idx + 1}.{block_id}.layer_scale_2"
   snake_case__										= f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

   all_keys.append((from_key, to_key)		)

 for from_key, to_key in all_keys:
  snake_case__										= from_state_dict.pop(_UpperCAmelCase		)

 our_model.load_state_dict(_UpperCAmelCase		)
 return our_model





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase = True		,   )			->     Union[str, Any]:

 '''simple docstring'''






 print(f"Downloading weights for {name}..."		)
 snake_case__										= cached_download(_UpperCAmelCase		)
 print(f"Converting {name}..."		)
 snake_case__										= torch.load(_UpperCAmelCase		)["state_dict"]
 from_model.load_state_dict(_UpperCAmelCase		)
 from_model.eval()
 with torch.no_grad():
  snake_case__										= VanForImageClassification(_UpperCAmelCase		).eval()
  snake_case__										= ModuleTransfer(src=_UpperCAmelCase		,   dest=_UpperCAmelCase		)
  snake_case__										= torch.randn((1, 3, 2_2_4, 2_2_4)		)
  module_transfer(_UpperCAmelCase		)
  snake_case__										= copy_parameters(_UpperCAmelCase		,   _UpperCAmelCase		)

 if not torch.allclose(from_model(_UpperCAmelCase		)		,   our_model(_UpperCAmelCase		).logits		):
  raise ValueError("The model logits don't match the original one."		)

 snake_case__										= name
 print(_UpperCAmelCase		)

 if push_to_hub:
  our_model.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,   commit_message="Add model"		,   use_temp_dir=_UpperCAmelCase		,   )

  # we can use the convnext one
  snake_case__										= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,   commit_message="Add image processor"		,   use_temp_dir=_UpperCAmelCase		,   )

  print(f"Pushed {checkpoint_name}"		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase = None		,   _UpperCAmelCase = True		)			->     Union[str, Any]:

 '''simple docstring'''






 snake_case__										= "imagenet-1k-id2label.json"
 snake_case__										= 1_0_0_0

 snake_case__										= "huggingface/label-files"
 snake_case__										= num_labels
 snake_case__										= json.load(open(hf_hub_download(_UpperCAmelCase		,   _UpperCAmelCase		,   repo_type="dataset"		)		,   "r"		)		)
 snake_case__										= {int(_UpperCAmelCase		): v for k, v in idalabel.items()}

 snake_case__										= idalabel
 snake_case__										= {v: k for k, v in idalabel.items()}

 snake_case__										= partial(_UpperCAmelCase		,   num_labels=_UpperCAmelCase		,   idalabel=_UpperCAmelCase		,   labelaid=_UpperCAmelCase		)

 snake_case__										= {
     "van-tiny": ImageNetPreTrainedConfig(
         hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]		,   depths=[3, 3, 5, 2]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-small": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[2, 2, 4, 2]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-base": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[3, 3, 1_2, 3]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-large": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[3, 5, 2_7, 3]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
 }

 snake_case__										= {
     "van-tiny": van_tiny,
     "van-small": van_small,
     "van-base": van_base,
     "van-large": van_large,
 }

 snake_case__										= {
     "van-tiny": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
     ),
     "van-small": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
     ),
     "van-base": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
     ),
     "van-large": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
     ),
 }

 if model_name:
  convert_weight_and_push(
      _UpperCAmelCase		,   names_to_config[model_name]		,   checkpoint=names_to_original_checkpoints[model_name]		,   from_model=names_to_original_models[model_name]()		,   save_directory=_UpperCAmelCase		,   push_to_hub=_UpperCAmelCase		,   )
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(
       _UpperCAmelCase		,   _UpperCAmelCase		,   checkpoint=names_to_original_checkpoints[model_name]		,   from_model=names_to_original_models[model_name]()		,   save_directory=_UpperCAmelCase		,   push_to_hub=_UpperCAmelCase		,   )


if __name__ == "__main__":
   a__    :   Any    						= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model-name""",
       default=None,
       type=str,
       help=(
           """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
           """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=Path,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--van_dir""",
       required=True,
       type=Path,
       help=(
           """A path to VAN's original implementation directory. You can download from here:"""
           """ https://github.com/Visual-Attention-Network/VAN-Classification"""
       ),
   )
   parser.add_argument(
       """--push_to_hub""",
       default=True,
       type=bool,
       required=False,
       help="""If True, push model and image processor to the hub.""",
   )

   a__    :   Any    						= parser.parse_args()
   a__    :   Path    						= args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   a__    :   int    						= args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

