'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDeiTForImageClassification,
        TFDeiTForImageClassificationWithTeacher,
        TFDeiTForMaskedImageModeling,
        TFDeiTModel,
    )
    from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor




class        __A      :



    def __init__(self    :    Any   ,			__a    :    str   ,			__a    :    List[str]=13   ,			__a    :    Optional[int]=30   ,			__a    :    Tuple=2   ,			__a    :    str=3   ,			__a    :    Tuple=True   ,			__a    :    List[Any]=True   ,			__a    :    Optional[int]=32   ,			__a    :    Optional[int]=2   ,			__a    :    int=4   ,			__a    :    Optional[Any]=37   ,			__a    :    Optional[Any]="gelu"   ,			__a    :    Optional[Any]=0.1   ,			__a    :    int=0.1   ,			__a    :    int=10   ,			__a    :    Optional[int]=0.02   ,			__a    :    Dict=3   ,			__a    :    Optional[int]=None   ,			__a    :    List[str]=2   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   patch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   scope
         UpperCAmelCase_           =   encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         UpperCAmelCase_           =   (image_size // patch_size) ** 2
         UpperCAmelCase_           =   num_patches + 2



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

         UpperCAmelCase_           =   self.get_config()

         return config, pixel_values, labels



    def 					_lowercase						(self    :    List[str] ):
         return DeiTConfig(
             image_size=self.image_size   ,			patch_size=self.patch_size   ,			num_channels=self.num_channels   ,			hidden_size=self.hidden_size   ,			num_hidden_layers=self.num_hidden_layers   ,			num_attention_heads=self.num_attention_heads   ,			intermediate_size=self.intermediate_size   ,			hidden_act=self.hidden_act   ,			hidden_dropout_prob=self.hidden_dropout_prob   ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,			is_decoder=__a   ,			initializer_range=self.initializer_range   ,			encoder_stride=self.encoder_stride   ,			)



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    str   ,			__a    :    Any   ,			__a    :    Dict ):
         UpperCAmelCase_           =   TFDeiTModel(config=__a )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, self.seq_length, self.hidden_size) )



    def 					_lowercase						(self    :    str   ,			__a    :    Optional[int]   ,			__a    :    List[str]   ,			__a    :    Tuple ):
         UpperCAmelCase_           =   TFDeiTForMaskedImageModeling(config=__a )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(
             result.reconstruction.shape   ,			(self.batch_size, self.num_channels, self.image_size, self.image_size) )

         # test greyscale images
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   TFDeiTForMaskedImageModeling(__a )

         UpperCAmelCase_           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(result.reconstruction.shape   ,			(self.batch_size, 1, self.image_size, self.image_size) )



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    List[str]   ,			__a    :    int   ,			__a    :    int ):
         UpperCAmelCase_           =   self.type_sequence_label_size
         UpperCAmelCase_           =   TFDeiTForImageClassification(__a )
         UpperCAmelCase_           =   model(__a   ,			labels=__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   TFDeiTForImageClassification(__a )

         UpperCAmelCase_           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         UpperCAmelCase_           =   model(__a   ,			labels=__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.type_sequence_label_size) )






    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   config_and_inputs
         UpperCAmelCase_           =   {"pixel_values": pixel_values}
         return config, inputs_dict




@require_tf
class        __A      ( UpperCamelCase__		,	UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Tuple          =  (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    a__       :						Union[str, Any]          =  (
        {
            """feature-extraction""": TFDeiTModel,
            """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    a__       :						Tuple          =  False
    a__       :						List[Any]          =  False
    a__       :						Any          =  False
    a__       :						Dict          =  False



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   TFDeiTModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			has_text_modality=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    Union[str, Any] ):
         self.config_tester.run_common_tests()



    @unittest.skip(reason="DeiT does not use inputs_embeds" )
    def 					_lowercase						(self    :    List[str] ):
         pass



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(__a )
              self.assertIsInstance(model.get_input_embeddings()   ,			(tf.keras.layers.Layer) )
              UpperCAmelCase_           =   model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__a   ,			tf.keras.layers.Dense ) )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_           =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]   ,			__a )



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a )



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Tuple   ,			__a    :    Tuple   ,			__a    :    Union[str, Any]=False ):
         UpperCAmelCase_           =   super()._prepare_for_class(__a   ,			__a   ,			return_labels=__a )

         if return_labels:
              if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
                   del inputs_dict["labels"]

         return inputs_dict






    @slow
    def 					_lowercase						(self    :    int ):
         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_           =   TFDeiTModel.from_pretrained(__a )
              self.assertIsNotNone(__a )





def 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
     return image






@require_tf
@require_vision
class        __A      ( unittest.TestCase	):



    @cached_property
    def 					_lowercase						(self    :    Union[str, Any] ):
         return (
             DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
             if is_vision_available()
             else None
         )






    @slow
    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" )

         UpperCAmelCase_           =   self.default_image_processor
         UpperCAmelCase_           =   prepare_img()
         UpperCAmelCase_           =   image_processor(images=__a   ,			return_tensors="tf" )

         # forward pass
         UpperCAmelCase_           =   model(**__a )

         # verify the logits
         UpperCAmelCase_           =   tf.TensorShape((1, 1000) )
         self.assertEqual(outputs.logits.shape   ,			__a )

         UpperCAmelCase_           =   tf.constant([-1.02_66, 0.19_12, -1.28_61] )

         self.assertTrue(np.allclose(outputs.logits[0, :3]   ,			__a   ,			atol=1E-4 ) )












"""simple docstring"""




import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_snake_case          =   {
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

_snake_case          =   {
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	list(state_dict.keys()				)
  for name in state_dict_keys:
    _a  :       str             =	state_dict.pop(UpperCamelCase__				)
    # emb -> embedding
    if name.startswith("""emb."""				):
      _a  :       Dict             =	name.replace("""emb."""     ,   """embeddings."""				)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith("""blocks.0.ln0"""				):
      _a  :       Dict             =	name.replace("""blocks.0.ln0"""     ,   """blocks.0.pre_ln"""				)
    # att -> attention
    _a  :       Any             =	re.sub(R"""blocks\.(\d+)\.att"""     ,   R"""blocks.\1.attention"""     ,   UpperCamelCase__				)
    # ffn -> feed_forward
    _a  :       int             =	re.sub(R"""blocks\.(\d+)\.ffn"""     ,   R"""blocks.\1.feed_forward"""     ,   UpperCamelCase__				)
    # time_mix_k -> time_mix_key and reshape
    if name.endswith(""".time_mix_k"""				):
      _a  :       List[str]             =	name.replace(""".time_mix_k"""     ,   """.time_mix_key"""				)
    # time_mix_v -> time_mix_value and reshape
    if name.endswith(""".time_mix_v"""				):
      _a  :       Tuple             =	name.replace(""".time_mix_v"""     ,   """.time_mix_value"""				)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith(""".time_mix_r"""				):
      _a  :       Dict             =	name.replace(""".time_mix_r"""     ,   """.time_mix_receptance"""				)

    if name != "head.weight":
      _a  :       Optional[int]             =	"""rwkv.""" + name

    _a  :       Any             =	weight
  return state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=False     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
    _a  :       Tuple             =	5_0_2_7_7
    _a  :       str             =	AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
  else:
    _a  :       int             =	PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__				)
    _a  :       int             =	len(UpperCamelCase__				)
  tokenizer.save_pretrained(UpperCamelCase__				)

  # 2. Build the config
  _a  :       Optional[Any]             =	list(NUM_HIDDEN_LAYERS_MAPPING.keys()				)
  if size is None:
    # Try to infer size from the checkpoint name
    for candidate in possible_sizes:
      if candidate in checkpoint_file:
        _a  :       Tuple             =	candidate
        break
    if size is None:
      raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""				)
  if size not in possible_sizes:
    raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}."""				)

  _a  :       List[Any]             =	RwkvConfig(
      vocab_size=UpperCamelCase__     ,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,   hidden_size=HIDEN_SIZE_MAPPING[size]     ,   )
  config.save_pretrained(UpperCamelCase__				)

  # 3. Download model file then convert state_dict
  _a  :       str             =	hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       int             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       List[str]             =	convert_state_dict(UpperCamelCase__				)

  # 4. Split in shards and save
  _a      ,  _a  :       List[str]             =	shard_checkpoint(UpperCamelCase__				)
  for shard_file, shard in shards.items():
    torch.save(UpperCamelCase__     ,   os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)

  if index is not None:
    _a  :       Optional[Any]             =	os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)
    # Save the index as well
    with open(UpperCamelCase__     ,   """w"""     ,   encoding="""utf-8"""				) as f:
      _a  :       Dict             =	json.dumps(UpperCamelCase__     ,   indent=2     ,   sort_keys=UpperCamelCase__				) + """\n"""
      f.write(UpperCamelCase__				)

    # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
    print(
        """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""				)
    _a  :       List[Any]             =	list(shards.keys()				)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      _a  :       Any             =	torch.load(os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}     ,   os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)

  del state_dict
  gc.collect()

  if push_to_hub:
    if model_name is None:
      raise ValueError("""Please provide a `model_name` to push the model to the Hub."""				)
    _a  :       Dict             =	AutoModelForCausalLM.from_pretrained(UpperCamelCase__				)
    model.push_to_hub(UpperCamelCase__     ,   max_shard_size="""2GB"""				)
    tokenizer.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
 _snake_case          =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
 )
 parser.add_argument(
     '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
 )
 parser.add_argument(
     '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
 )
 parser.add_argument(
     '--tokenizer_file',
     default=None,
     type=str,
     help='Path to the tokenizer file to use (if not provided, only the model is converted).',
 )
 parser.add_argument(
     '--size',
     default=None,
     type=str,
     help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
     help='Push to the Hub the converted model.',
 )
 parser.add_argument(
     '--model_name',
     default=None,
     type=str,
     help='Name of the pushed model on the Hub, including the username / organization.',
 )

 _snake_case          =   parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )

