

import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
           RwkvForCausalLM,
           RwkvModel,
       )
       from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
       snake_case_    						=				False


class    SCREAMING_SNAKE_CASE__      :





    def __init__(   self		,     a		,     a=14		,     a=7		,     a=True		,     a=False		,     a=True		,     a=True		,     a=True		,     a=99		,     a=32		,     a=5		,     a=37		,     a="gelu"		,     a=0.1		,     a=0.1		,     a=512		,     a=16		,     a=2		,     a=3		,     a=4		,     a=None		,     ):
         lowercase__					:     Tuple   = parent
         lowercase__					:     Optional[int]   = batch_size
         lowercase__					:     Tuple   = seq_length
         lowercase__					:     Tuple   = is_training
         lowercase__					:     str   = use_token_type_ids
         lowercase__					:     Union[str, Any]   = use_input_mask
         lowercase__					:     List[str]   = use_labels
         lowercase__					:     List[Any]   = use_mc_token_ids
         lowercase__					:     int   = vocab_size
         lowercase__					:     str   = hidden_size
         lowercase__					:     List[Any]   = num_hidden_layers
         lowercase__					:     Optional[Any]   = intermediate_size
         lowercase__					:     Tuple   = hidden_act
         lowercase__					:     Optional[Any]   = hidden_dropout_prob
         lowercase__					:     List[str]   = attention_probs_dropout_prob
         lowercase__					:     str   = max_position_embeddings
         lowercase__					:     Tuple   = type_vocab_size
         lowercase__					:     Optional[Any]   = type_sequence_label_size
         lowercase__					:     int   = num_labels
         lowercase__					:     List[str]   = num_choices
         lowercase__					:     int   = scope
         lowercase__					:     Tuple   = vocab_size - 1
         lowercase__					:     int   = vocab_size - 1
         lowercase__					:     Any   = vocab_size - 1





    def    snake_case_       (   self):
         return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b')





    def    snake_case_       (   self		,     a=False		,     a=False		,     a=False):
         lowercase__					:     str   = ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size)

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

         lowercase__					:     str   = None
         if self.use_token_type_ids:
              lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size)

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

         lowercase__					:     Optional[int]   = None
         lowercase__					:     Tuple   = None
         lowercase__					:     Tuple   = None
         if self.use_labels:
              lowercase__					:     Dict   = ids_tensor([self.batch_size]		,     self.type_sequence_label_size)
              lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels)
              lowercase__					:     str   = ids_tensor([self.batch_size]		,     self.num_choices)

         lowercase__					:     List[str]   = self.get_config(
             gradient_checkpointing=a		,     scale_attn_by_inverse_layer_idx=a		,     reorder_and_upcast_attn=a		,     )

         return (
             config,
             input_ids,
             input_mask,
             None,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )





    def    snake_case_       (   self		,     a=False		,     a=False		,     a=False):
         return RwkvConfig(
             vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     intermediate_size=self.intermediate_size		,     activation_function=self.hidden_act		,     resid_pdrop=self.hidden_dropout_prob		,     attn_pdrop=self.attention_probs_dropout_prob		,     n_positions=self.max_position_embeddings		,     type_vocab_size=self.type_vocab_size		,     use_cache=a		,     bos_token_id=self.bos_token_id		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.pad_token_id		,     gradient_checkpointing=a		,     scale_attn_by_inverse_layer_idx=a		,     reorder_and_upcast_attn=a		,     )





    def    snake_case_       (   self):
         lowercase__					:     Optional[int]   = self.get_config()
         lowercase__					:     Union[str, Any]   = 300
         return config





    def    snake_case_       (   self):
         (
             (
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				
         )					:     int   = self.prepare_config_and_inputs()

         lowercase__					:     List[Any]   = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
         lowercase__					:     Any   = ids_tensor([self.batch_size, self.seq_length]		,     vocab_size=2)

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





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
         lowercase__					:     Any   = True
         lowercase__					:     Dict   = RwkvModel(config=a)
         model.to(a)
         model.eval()

         lowercase__					:     Dict   = model(a)

         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(len(result.hidden_states)		,     config.num_hidden_layers + 1)





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
         lowercase__					:     Any   = RwkvForCausalLM(a)
         model.to(a)
         model.eval()

         lowercase__					:     Union[str, Any]   = model(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    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
         lowercase__					:     Optional[int]   = RwkvModel(config=a)
         model.to(a)
         model.eval()

         lowercase__					:     List[str]   = model(a)
         lowercase__					:     int   = outputs.last_hidden_state

         lowercase__					:     Optional[int]   = model(input_ids[:, :2])
         lowercase__					:     Optional[int]   = outputs.last_hidden_state

         # Using the state computed on the first inputs, we will get the same output
         lowercase__					:     int   = model(input_ids[:, 2:]		,     state=outputs.state)
         lowercase__					:     List[Any]   = outputs.last_hidden_state

         self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]		,     dim=1)		,     a		,     atol=1e-5))





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a		,     a=False):
         lowercase__					:     Optional[int]   = RwkvForCausalLM(a)
         model.to(a)
         if gradient_checkpointing:
              model.gradient_checkpointing_enable()

         lowercase__					:     Tuple   = model(a		,     labels=a)
         self.parent.assertEqual(result.loss.shape		,     ())
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size))
         result.loss.backward()





    def    snake_case_       (   self):
         lowercase__					:     List[Any]   = self.prepare_config_and_inputs()

         (
             (
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				(
             lowercase__
         )   ,				
         )					:     Optional[int]   = config_and_inputs

         lowercase__					:     int   = {'input_ids': input_ids}

         return config, inputs_dict



@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	, reason="""See https://github.com/huggingface/transformers/pull/24204"""  )
@require_torch
class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case	, __snake_case	, unittest.TestCase  ):
    __lowerCamelCase		: List[Any]														=  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
    __lowerCamelCase		: Tuple														=  (
        {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
    )
    # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
    __lowerCamelCase		: Optional[Any]														=  False
    __lowerCamelCase		: Any														=  False
    __lowerCamelCase		: List[str]														=  False
    __lowerCamelCase		: int														=  False
    __lowerCamelCase		: List[str]														=  False  # Rwkv does not support head masking





    def    snake_case_       (   self):
         lowercase__					:     Dict   = RwkvModelTester(self)
         lowercase__					:     List[Any]   = ConfigTester(
             self		,     config_class=a		,     n_embd=37		,     common_properties=['hidden_size', 'num_hidden_layers'])





    def    snake_case_       (   self		,     a		,     a		,     a=None):
         if isinstance(a		,     torch.Tensor):
              lowercase__   ,				lowercase__					:     Optional[Any]   = member.max().item(), member.min().item()
         elif isinstance(a		,     a) or isinstance(a		,     a):
              lowercase__   ,				lowercase__					:     Optional[int]   = max(a), min(a)

         if not isinstance(a		,     a):
              raise TypeError('container should be a list or tuple')
         elif len(a) != 2:
              raise ValueError('container should have 2 elements')

         lowercase__   ,				lowercase__					:     Union[str, Any]   = container

         lowercase__					:     str   = (min_value >= expected_min) and (max_value <= expected_max)

         if not is_inside_interval:
              lowercase__					:     Tuple   = '%s not found in %s' % (safe_repr(a), safe_repr(a))
              self.fail(self._formatMessage(a		,     a))





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





    def    snake_case_       (   self):
         lowercase__					:     str   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_rwkv_model(*a)





    def    snake_case_       (   self):
         lowercase__					:     Dict   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_causl_lm(*a)





    def    snake_case_       (   self):
         lowercase__					:     int   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_state_equivalency(*a)





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     str   = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__					:     Union[str, Any]   = model_class(config=a)
              for name, param in model.named_parameters():
                   if "time_decay" in name:
                        if param.requires_grad:
                             self.assertTrue(param.data.max().item() == 3.0)
                             self.assertTrue(param.data.min().item() == -5.0)
                   elif "time_first" in name:
                        if param.requires_grad:
                             # check if it's a ones like
                             self.assertTrue(torch.allclose(param.data		,     torch.ones_like(param.data)		,     atol=1e-5		,     rtol=1e-5))
                   elif any(x in name for x in ['time_mix_key', 'time_mix_receptance']):
                        if param.requires_grad:
                             self.assertInterval(
                                 param.data		,     [0.0, 1.0]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )
                   elif "time_mix_value" in name:
                        if param.requires_grad:
                             self.assertInterval(
                                 param.data		,     [0.0, 1.3]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     Optional[Any]   = self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__					:     Optional[Any]   = True

         lowercase__					:     List[Any]   = getattr(self.model_tester		,     'seq_length'		,     a)

         for model_class in self.all_model_classes:
              lowercase__					:     Tuple   = True
              lowercase__					:     Optional[Any]   = False
              lowercase__					:     Optional[Any]   = True
              lowercase__					:     Any   = model_class(a)
              model.to(a)
              model.eval()

              lowercase__					:     Optional[Any]   = self._prepare_for_class(a		,     a)
              lowercase__					:     Dict   = inputs['input_ids'].shape[0]
              with torch.no_grad():
                   lowercase__					:     Optional[int]   = model(**a)
              lowercase__					:     List[str]   = outputs.attentions
              self.assertEqual(len(a)		,     self.model_tester.num_hidden_layers)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              lowercase__					:     Optional[Any]   = True
              lowercase__					:     Any   = model_class(a)
              model.to(a)
              model.eval()

              lowercase__					:     int   = self._prepare_for_class(a		,     a)
              lowercase__					:     Dict   = inputs['input_ids'].shape[0]
              with torch.no_grad():
                   lowercase__					:     List[Any]   = model(**a)
              lowercase__					:     List[Any]   = outputs.attentions
              self.assertEqual(len(a)		,     self.model_tester.num_hidden_layers)

              self.assertListEqual(
                  list(attentions[0].shape[-3:])		,     [batch_size, seq_len, config.hidden_size]		,     )
              lowercase__					:     int   = len(a)

              # Check attention is always last and order is fine
              lowercase__					:     Dict   = True
              lowercase__					:     Dict   = True
              lowercase__					:     Dict   = model_class(a)
              model.to(a)
              model.eval()

              lowercase__					:     List[str]   = self._prepare_for_class(a		,     a)
              lowercase__					:     Any   = inputs['input_ids'].shape[0]
              with torch.no_grad():
                   lowercase__					:     int   = model(**a)

              lowercase__					:     Any   = 1
              self.assertEqual(out_len + added_hidden_states		,     len(a))

              lowercase__					:     int   = outputs.attentions

              self.assertEqual(len(a)		,     self.model_tester.num_hidden_layers)
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:])		,     [batch_size, seq_len, config.hidden_size]		,     )





    @slow
    def    snake_case_       (   self):
         for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__					:     Union[str, Any]   = RwkvModel.from_pretrained(a)
              self.assertIsNotNone(a)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	, reason="""See https://github.com/huggingface/transformers/pull/24204"""  )
@slow
class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):





    def    snake_case_       (   self):
         lowercase__					:     Any   = 'RWKV/rwkv-4-169m-pile'
         lowercase__					:     int   = AutoTokenizer.from_pretrained(self.model_id)





    def    snake_case_       (   self):
         lowercase__					:     Optional[int]   = 'Hello my name is Jasmine and I am a newbie to the'
         lowercase__					:     Optional[Any]   = RwkvForCausalLM.from_pretrained(self.model_id).to(a)

         lowercase__					:     List[Any]   = self.tokenizer('Hello my name is'		,     return_tensors='pt').input_ids.to(a)
         lowercase__					:     Dict   = model.generate(a		,     max_new_tokens=10)
         lowercase__					:     int   = self.tokenizer.decode(output[0].tolist())

         self.assertEqual(a		,     a)





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = 'Hello my name is Jasmine and I am a newbie to the'

         lowercase__					:     str   = self.tokenizer('Hello my name is'		,     return_tensors='pt').input_ids.to(a)
         lowercase__					:     Optional[int]   = RwkvForCausalLM.from_pretrained(self.model_id		,     torch_dtype=torch.bfloataa).to(a)

         lowercase__					:     Optional[int]   = model.generate(a		,     max_new_tokens=10)
         lowercase__					:     Union[str, Any]   = self.tokenizer.decode(output[0].tolist())

         self.assertEqual(a		,     a)




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					int=False			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]="data2vec."    ):



     '''simple docstring'''





     lowercase__					:     str   = 'backbone.' if is_semantic else ''

     lowercase__					:     Dict   = []
     for i in range(config.num_hidden_layers    ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append(
              (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")    )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
             (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
             (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
         ]    )

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ('mask_token', f"""{hf_prefix}embeddings.mask_token"""),
                  (
                      'rel_pos_bias.relative_position_bias_table',
                      f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                  ),
                  (
                      'rel_pos_bias.relative_position_index',
                      f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                  ),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]    )
     elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                  ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                  ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                  ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
              ]    )
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('fc_norm.weight', f"""{hf_prefix}pooler.layernorm.weight"""),
                  ('fc_norm.bias', f"""{hf_prefix}pooler.layernorm.bias"""),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]    )

     return rename_keys

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					Dict=False			,  SCREAMING_SNAKE_CASE_	:					List[str]="data2vec_vision."    ):



     '''simple docstring'''





     for i in range(config.num_hidden_layers    ):
          lowercase__					:     Optional[int]   = 'backbone.' if is_semantic else ''
          # queries, keys and values
          lowercase__					:     List[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""    )
          lowercase__					:     Optional[int]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""    )
          lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""    )

          lowercase__					:     Union[str, Any]   = in_proj_weight[
              : config.hidden_size, :
          ]
          lowercase__					:     List[str]   = q_bias
          lowercase__					:     Tuple   = in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase__					:     Union[str, Any]   = in_proj_weight[
              -config.hidden_size :, :
          ]
          lowercase__					:     Union[str, Any]   = v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""    )
          lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""    )

          lowercase__					:     List[str]   = gamma_a
          lowercase__					:     Dict   = gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               lowercase__					:     Tuple   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""    )
               lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""    )

               lowercase__					:     int   = table
               lowercase__					:     List[str]   = index

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     List[str]   = argparse.ArgumentParser(
         'Convert Data2VecVision to HF for image classification and pretraining'			,  add_help=SCREAMING_SNAKE_CASE_    )
     parser.add_argument('--hf_checkpoint_name'			,  type=SCREAMING_SNAKE_CASE_    )
     parser.add_argument('--input_size'			,  default=224			,  type=SCREAMING_SNAKE_CASE_			,  help='images input size'    )
     parser.add_argument('--beit_checkpoint'			,  default=''			,  help='beit checkpoint'    )

     return parser.parse_args()

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





     def load_state_dict(SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""			,  SCREAMING_SNAKE_CASE_	:					List[Any]="relative_position_index"    ):
          lowercase__					:     str   = []
          lowercase__					:     str   = []
          lowercase__					:     Dict   = []
          # copy state_dict so _load_from_state_dict can modify it
          lowercase__					:     List[Any]   = getattr(SCREAMING_SNAKE_CASE_			,  '_metadata'			,  SCREAMING_SNAKE_CASE_    )
          lowercase__					:     Optional[int]   = state_dict.copy()
          if metadata is not None:
               lowercase__					:     Dict   = metadata

          def load(SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""    ):
               lowercase__					:     Optional[Any]   = {} if metadata is None else metadata.get(prefix[:-1]			,  {}    )
               module._load_from_state_dict(
                   SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
               for name, child in module._modules.items():
                    if child is not None:
                         load(SCREAMING_SNAKE_CASE_			,  prefix + name + '.'    )

          load(SCREAMING_SNAKE_CASE_			,  prefix=SCREAMING_SNAKE_CASE_    )

          lowercase__					:     List[Any]   = []
          lowercase__					:     Dict   = []
          for key in missing_keys:
               lowercase__					:     Any   = True
               for ignore_key in ignore_missing.split('|'    ):
                    if ignore_key in key:
                         lowercase__					:     Union[str, Any]   = False
                         break
               if keep_flag:
                    warn_missing_keys.append(SCREAMING_SNAKE_CASE_    )
               else:
                    ignore_missing_keys.append(SCREAMING_SNAKE_CASE_    )

          lowercase__					:     Tuple   = warn_missing_keys

          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print(
                   'Weights of {} not initialized from pretrained model: {}'.format(
                       model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print(
                   'Ignored weights of {} not initialized from pretrained model: {}'.format(
                       model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print('\n'.join(SCREAMING_SNAKE_CASE_    )    )

     lowercase__					:     int   = {
         'pretrained': False,
         'use_shared_rel_pos_bias': True,
         'use_abs_pos_emb': False,
         'init_values': 0.1,
     }

     if is_finetuned:
          model_kwargs.update(
              {
                  'num_classes': 1_000,
                  'use_mean_pooling': True,
                  'init_scale': 0.001,
                  'use_rel_pos_bias': True,
              }    )

     lowercase__					:     Dict   = create_model(
         'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'			,  **SCREAMING_SNAKE_CASE_			,  )
     lowercase__					:     int   = model.patch_embed.patch_size
     lowercase__					:     int   = (args.input_size // patch_size[0], args.input_size // patch_size[1])
     lowercase__					:     str   = torch.load(args.beit_checkpoint			,  map_location='cpu'    )

     print(f"""Load ckpt from {args.beit_checkpoint}"""    )
     lowercase__					:     int   = None
     for model_key in ("model", "module"):
          if model_key in checkpoint:
               lowercase__					:     Tuple   = checkpoint[model_key]
               print(f"""Load state_dict by model_key = {model_key}"""    )
               break

     lowercase__					:     Optional[Any]   = list(checkpoint_model.keys()    )
     for key in all_keys:
          if "relative_position_index" in key:
               checkpoint_model.pop(SCREAMING_SNAKE_CASE_    )

          if "relative_position_bias_table" in key:
               lowercase__					:     Any   = checkpoint_model[key]
               lowercase__   ,				lowercase__					:     List[Any]   = rel_pos_bias.size()
               lowercase__   ,				lowercase__					:     Optional[int]   = model.state_dict()[key].size()
               lowercase__					:     Dict   = model.patch_embed.patch_shape
               if dst_patch_shape[0] != dst_patch_shape[1]:
                    raise NotImplementedError()

     load_state_dict(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  prefix=''    )

     return model

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     List[str]   = get_args()

     lowercase__					:     Union[str, Any]   = 'ft1k' in args.hf_checkpoint_name
     lowercase__					:     Dict   = 'large' in args.hf_checkpoint_name

     if is_finetuned:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
          # into this folder.
          import modeling_finetune  # noqa: F401
     else:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
          # into this folder
          # IMPORTANT: Note that for now we've only converted the down-stream
          # model and not the full pretrained model. This means for the integration
          # test you need to add a `return x` after the following line:
          # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
          # to make the integration test pass.
          import modeling_cyclical  # noqa: F401

     # 1. Create model config
     lowercase__					:     Optional[Any]   = DataaVecVisionConfig()
     if is_finetuned:
          lowercase__					:     int   = True
          lowercase__					:     Any   = False
          lowercase__					:     Union[str, Any]   = True
          lowercase__					:     Union[str, Any]   = 1_000

          lowercase__					:     Any   = 'huggingface/label-files'
          lowercase__					:     Any   = 'imagenet-1k-id2label.json'
          lowercase__					:     Union[str, Any]   = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  repo_type='dataset'    )			,  'r'    )    )
          lowercase__					:     Dict   = {int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
          lowercase__					:     Optional[int]   = idalabel
          lowercase__					:     Optional[int]   = {v: k for k, v in idalabel.items()}
     else:
          lowercase__					:     Dict   = False
          lowercase__					:     int   = True
          lowercase__					:     List[str]   = False

     if is_large:
          lowercase__					:     List[str]   = 1_024
          lowercase__					:     Union[str, Any]   = 4_096
          lowercase__					:     Union[str, Any]   = 24
          lowercase__					:     Optional[int]   = 16

     # 2. Load Beit model
     lowercase__					:     List[Any]   = load_beit_model(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
     orig_model.eval()

     # 3. Forward Beit model
     lowercase__					:     Optional[Any]   = BeitImageProcessor(size=config.image_size			,  do_center_crop=SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Tuple   = Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'    )
     lowercase__					:     Tuple   = image_processor(images=SCREAMING_SNAKE_CASE_			,  return_tensors='pt'    )
     lowercase__					:     List[Any]   = encoding['pixel_values']

     lowercase__					:     List[Any]   = (pixel_values,) if is_finetuned else (pixel_values, None)
     with torch.no_grad():
          lowercase__					:     List[str]   = orig_model(*SCREAMING_SNAKE_CASE_    )

     # 4. Load HF Data2VecVision model
     if is_finetuned:
          lowercase__					:     Optional[int]   = DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_    )
          hf_model.eval()
          lowercase__					:     Union[str, Any]   = False
          lowercase__					:     Dict   = 'data2vec_vision.'
     else:
          lowercase__					:     Tuple   = DataaVecVisionModel(SCREAMING_SNAKE_CASE_    )
          hf_model.eval()
          lowercase__					:     int   = True
          lowercase__					:     Tuple   = ''

     lowercase__					:     Tuple   = create_rename_keys(SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
     lowercase__					:     str   = orig_model.state_dict()
     for src, dest in rename_keys:
          lowercase__					:     int   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
          lowercase__					:     Dict   = val

     read_in_q_k_v(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
     lowercase__   ,				lowercase__					:     List[Any]   = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_			,  strict=SCREAMING_SNAKE_CASE_    )
     print('HF missing'			,  SCREAMING_SNAKE_CASE_    )
     print('HF unexpected_keys'			,  SCREAMING_SNAKE_CASE_    )

     # 5. Forward HF Data2VecVision model
     with torch.no_grad():
          lowercase__					:     Optional[int]   = hf_model(SCREAMING_SNAKE_CASE_    )

     lowercase__					:     Optional[int]   = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

     # 6. Compare
     lowercase__					:     Dict   = torch.max(torch.abs(hf_output - orig_model_output    )    ).item()

     print(f"""max_absolute_diff = {max_absolute_diff}"""    )
     lowercase__					:     Dict   = torch.allclose(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  atol=1E-3    )
     print('Do both models output the same tensors?'			,  '🔥' if success else '💩'    )
     if not success:
          raise Exception('Something went wRoNg'    )

     # 7. Save
     print(f"""Saving to {args.hf_checkpoint_name}"""    )
     hf_model.save_pretrained(args.hf_checkpoint_name    )
     image_processor.save_pretrained(args.hf_checkpoint_name    )


if __name__ == "__main__":
       main()
       # Run the following to convert checkpoints
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

