code: 
  
  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)
  
  
style_context: 
  
  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"
  
  