



from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
  from transformers.models.albert.modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertModel,
  )







class 						snake_case  :



   '''simple docstring'''






   def __init__(   self							:     List[str]			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     Union[str, Any]=13			,						lowerCAmelCase							:     Tuple=7			,						lowerCAmelCase							:     List[Any]=True			,						lowerCAmelCase							:     str=True			,						lowerCAmelCase							:     str=True			,						lowerCAmelCase							:     Union[str, Any]=True			,						lowerCAmelCase							:     Union[str, Any]=99			,						lowerCAmelCase							:     Dict=16			,						lowerCAmelCase							:     List[Any]=32			,						lowerCAmelCase							:     Union[str, Any]=2			,						lowerCAmelCase							:     Any=4			,						lowerCAmelCase							:     str=37			,						lowerCAmelCase							:     Optional[int]="gelu"			,						lowerCAmelCase							:     List[str]=0.1			,						lowerCAmelCase							:     Union[str, Any]=0.1			,						lowerCAmelCase							:     int=512			,						lowerCAmelCase							:     str=16			,						lowerCAmelCase							:     Dict=2			,						lowerCAmelCase							:     Union[str, Any]=0.02			,						lowerCAmelCase							:     Dict=3			,						lowerCAmelCase							:     Any=4			,						lowerCAmelCase							:     Union[str, Any]=None			,						)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					int					=   parent
    _snake_case      :					Optional[int]					=   13
    _snake_case      :					Any					=   7
    _snake_case      :					int					=   True
    _snake_case      :					Dict					=   True
    _snake_case      :					Any					=   True
    _snake_case      :					Dict					=   True
    _snake_case      :					Union[str, Any]					=   99
    _snake_case      :					List[Any]					=   16
    _snake_case      :					Union[str, Any]					=   32
    _snake_case      :					int					=   2
    _snake_case      :					str					=   4
    _snake_case      :					Tuple					=   37
    _snake_case      :					Dict					=   """gelu"""
    _snake_case      :					Optional[int]					=   0.1
    _snake_case      :					Optional[Any]					=   0.1
    _snake_case      :					Tuple					=   512
    _snake_case      :					int					=   16
    _snake_case      :					Any					=   2
    _snake_case      :					List[Any]					=   0.02
    _snake_case      :					Dict					=   3
    _snake_case      :					Tuple					=   4
    _snake_case      :					int					=   None

   def        UpperCamelCase_					(   self							:     Tuple)			->			Tuple:

    """simple docstring"""







    _snake_case      :					str					=   ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

    _snake_case      :					Dict					=   None
    if self.use_input_mask:
     _snake_case      :					List[Any]					=   random_attention_mask([self.batch_size, self.seq_length])

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

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

    _snake_case      :					int					=   AlbertConfig(
        vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						embedding_size=self.embedding_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			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						initializer_range=self.initializer_range			,						)

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def        UpperCamelCase_					(   self							:     Any			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     Optional[Any]			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     Tuple)			->			Dict:

    """simple docstring"""







    _snake_case      :					Optional[int]					=   TFAlbertModel(config=lowerCAmelCase)
    # inputs = {'input_ids': input_ids,
    #           'attention_mask': input_mask,
    #           'token_type_ids': token_type_ids}
    # sequence_output, pooled_output = model(**inputs)
    _snake_case      :					Optional[Any]					=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
    _snake_case      :					List[str]					=   model(lowerCAmelCase)

    _snake_case      :					Tuple					=   [input_ids, input_mask]
    _snake_case      :					Optional[int]					=   model(lowerCAmelCase)

    _snake_case      :					List[Any]					=   model(lowerCAmelCase)

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

   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     Tuple			,						lowerCAmelCase							:     Optional[Any]			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     List[str])			->			List[Any]:

    """simple docstring"""







    _snake_case      :					str					=   self.num_labels
    _snake_case      :					Dict					=   TFAlbertForPreTraining(config=lowerCAmelCase)
    _snake_case      :					str					=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
    _snake_case      :					int					=   model(lowerCAmelCase)
    self.parent.assertEqual(result.prediction_logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))
    self.parent.assertEqual(result.sop_logits.shape			,						(self.batch_size, self.num_labels))

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     Optional[int]			,						lowerCAmelCase							:     List[Any]			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     List[Any]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     List[str])			->			List[Any]:

    """simple docstring"""







    _snake_case      :					Optional[int]					=   TFAlbertForMaskedLM(config=lowerCAmelCase)
    _snake_case      :					Dict					=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
    _snake_case      :					List[Any]					=   model(lowerCAmelCase)
    self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))

   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     Optional[int]			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     Tuple			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     Optional[int])			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					int					=   self.num_labels
    _snake_case      :					Optional[int]					=   TFAlbertForSequenceClassification(config=lowerCAmelCase)
    _snake_case      :					Union[str, Any]					=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
    _snake_case      :					Optional[Any]					=   model(lowerCAmelCase)
    self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     List[Any]			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     Optional[int]			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     Dict			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   TFAlbertForQuestionAnswering(config=lowerCAmelCase)
    _snake_case      :					Union[str, Any]					=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
    _snake_case      :					str					=   model(lowerCAmelCase)
    self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length))
    self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length))

   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     Optional[int]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     Dict			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     Tuple)			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					str					=   self.num_choices
    _snake_case      :					List[str]					=   TFAlbertForMultipleChoice(config=lowerCAmelCase)
    _snake_case      :					Any					=   tf.tile(tf.expand_dims(lowerCAmelCase			,						1)			,						(1, self.num_choices, 1))
    _snake_case      :					Tuple					=   tf.tile(tf.expand_dims(lowerCAmelCase			,						1)			,						(1, self.num_choices, 1))
    _snake_case      :					str					=   tf.tile(tf.expand_dims(lowerCAmelCase			,						1)			,						(1, self.num_choices, 1))
    _snake_case      :					Any					=   {
        """input_ids""": multiple_choice_inputs_ids,
        """attention_mask""": multiple_choice_input_mask,
        """token_type_ids""": multiple_choice_token_type_ids,
    }
    _snake_case      :					List[str]					=   model(lowerCAmelCase)
    self.parent.assertListEqual(list(result["""logits"""].shape)			,						[self.batch_size, self.num_choices])

   def        UpperCamelCase_					(   self							:     int			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     Optional[Any]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     Any)			->			int:

    """simple docstring"""







    _snake_case      :					Tuple					=   self.num_labels
    _snake_case      :					Union[str, Any]					=   TFAlbertForTokenClassification(config=lowerCAmelCase)
    _snake_case      :					str					=   {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    _snake_case      :					Dict					=   model(lowerCAmelCase)
    self.parent.assertListEqual(list(result["""logits"""].shape)			,						[self.batch_size, self.seq_length, self.num_labels])


   def        UpperCamelCase_					(   self							:     Optional[int])			->			List[str]:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   self.prepare_config_and_inputs()
    (
        (
        _snake_case
    )		,      (
        _snake_case
    )		,      (
        _snake_case
    )		,      (
        _snake_case
    )		,      (
        _snake_case
    )		,      (
        _snake_case
    )		,      (
        _snake_case
    )		,      
    )      :					Tuple					=   config_and_inputs
    _snake_case      :					List[str]					=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict







@require_tf
class 						snake_case  (		SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,unittest.TestCase					):



   '''simple docstring'''






   snake_case_   :	List[Any]       =						(
       (
           TFAlbertModel,
           TFAlbertForPreTraining,
           TFAlbertForMaskedLM,
           TFAlbertForSequenceClassification,
           TFAlbertForQuestionAnswering,
           TFAlbertForTokenClassification,
           TFAlbertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   snake_case_   :	str       =						(
       {
           """feature-extraction""": TFAlbertModel,
           """fill-mask""": TFAlbertForMaskedLM,
           """question-answering""": TFAlbertForQuestionAnswering,
           """text-classification""": TFAlbertForSequenceClassification,
           """token-classification""": TFAlbertForTokenClassification,
           """zero-shot""": TFAlbertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   snake_case_   :	Dict       =						False
   snake_case_   :	Tuple       =						False

   def        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     Optional[Any]			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     int=False)			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					Dict					=   super()._prepare_for_class(lowerCAmelCase			,						lowerCAmelCase			,						return_labels=lowerCAmelCase)

    if return_labels:
     if model_class in get_values(lowerCAmelCase):
      _snake_case      :					Any					=   tf.zeros(self.model_tester.batch_size			,						dtype=tf.intaa)

    return inputs_dict

   def        UpperCamelCase_					(   self							:     Any)			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					Dict					=   TFAlbertModelTester(self)
    _snake_case      :					Optional[int]					=   ConfigTester(self			,						config_class=lowerCAmelCase			,						hidden_size=37)

   def        UpperCamelCase_					(   self							:     Any)			->			str:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def        UpperCamelCase_					(   self							:     int)			->			int:

    """simple docstring"""







    _snake_case      :					List[Any]					=   self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_albert_model(*lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     Any)			->			List[Any]:

    """simple docstring"""







    _snake_case      :					List[Any]					=   self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_albert_for_pretraining(*lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     List[str])			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_albert_for_masked_lm(*lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     int)			->			Dict:

    """simple docstring"""







    _snake_case      :					Tuple					=   self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_albert_for_multiple_choice(*lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     int)			->			List[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_albert_for_sequence_classification(*lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					List[Any]					=   self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_albert_for_question_answering(*lowerCAmelCase)


   @slow
   def        UpperCamelCase_					(   self							:     List[str])			->			Optional[Any]:

    """simple docstring"""







    for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case      :					Optional[int]					=   TFAlbertModel.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)







@require_tf
class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @slow
   def        UpperCamelCase_					(   self							:     Tuple)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   TFAlbertForPreTraining.from_pretrained("""albert-base-v2""")
    _snake_case      :					Union[str, Any]					=   tf.constant([[0, 1, 2, 3, 4, 5]])
    _snake_case      :					List[str]					=   model(lowerCAmelCase)[0]

    _snake_case      :					Optional[int]					=   [1, 6, 3_0000]
    self.assertEqual(output.shape			,						lowerCAmelCase)

    _snake_case      :					List[Any]					=   tf.constant(
        [
            [
                [4.595_668, 0.74_462_754, -1.818_147],
                [4.5_954_347, 0.7_454_184, -1.8_188_258],
                [4.5_954_905, 0.7_448_235, -1.8_182_316],
            ]
        ])
    tf.debugging.assert_near(output[:, :3, :3]			,						lowerCAmelCase			,						atol=1E-4)





import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Optional[int]			,    SCREAMING_SNAKE_CASE__    :		Dict=False			,    SCREAMING_SNAKE_CASE__    :		Optional[Any]=False			)					->       Any:
 _snake_case      :					int					=   """backbone.""" if is_semantic else """"""

 _snake_case      :					Optional[Any]					=   []
 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'''beit.encoder.layer.{i}.layernorm_before.weight''')			)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')			)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')			)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')			)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')			)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')			)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')			)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')			)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')			)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')			)

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

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ("""mask_token""", """beit.embeddings.mask_token"""),
          (
              """rel_pos_bias.relative_position_bias_table""",
              """beit.encoder.relative_position_bias.relative_position_bias_table""",
          ),
          (
              """rel_pos_bias.relative_position_index""",
              """beit.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""", """beit.pooler.layernorm.weight"""),
          ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]			)

 return rename_keys



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Tuple			,    SCREAMING_SNAKE_CASE__    :		Union[str, Any]			,    SCREAMING_SNAKE_CASE__    :		List[Any]=False			,    SCREAMING_SNAKE_CASE__    :		List[str]=False			)					->       List[Any]:
 for i in range(config.num_hidden_layers			):
  _snake_case      :					int					=   """backbone.""" if is_semantic else """"""
  # queries, keys and values
  _snake_case      :					List[Any]					=   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''			)
  _snake_case      :					List[str]					=   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''			)
  _snake_case      :					Any					=   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''			)

  _snake_case      :					Optional[Any]					=   in_proj_weight[
      : config.hidden_size, :
  ]
  _snake_case      :					Union[str, Any]					=   q_bias
  _snake_case      :					Dict					=   in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _snake_case      :					str					=   in_proj_weight[
      -config.hidden_size :, :
  ]
  _snake_case      :					List[Any]					=   v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  _snake_case      :					Tuple					=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''			)
  _snake_case      :					List[str]					=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''			)

  _snake_case      :					Dict					=   gamma_a
  _snake_case      :					Tuple					=   gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   _snake_case      :					Tuple					=   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''			)
   _snake_case      :					Any					=   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''			)

   _snake_case      :					Optional[int]					=   table
   _snake_case      :					Dict					=   index



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		List[str]			,    SCREAMING_SNAKE_CASE__    :		List[str]			,    SCREAMING_SNAKE_CASE__    :		Optional[Any]			)					->       Optional[int]:
 _snake_case      :					str					=   dct.pop(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Tuple					=   val



def  lowercase    (				)					->       Any:
 _snake_case      :					List[Any]					=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case      :					List[str]					=   Image.open(requests.get(SCREAMING_SNAKE_CASE__			,    stream=SCREAMING_SNAKE_CASE__			).raw			)
 return im



@torch.no_grad()
def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Union[str, Any]			,    SCREAMING_SNAKE_CASE__    :		str			)					->       Dict:
 _snake_case      :					Any					=   BeitConfig()
 _snake_case      :					Union[str, Any]					=   False
 _snake_case      :					str					=   False
 _snake_case      :					Tuple					=   """huggingface/label-files"""
 # set config parameters based on URL
 if checkpoint_url[-9:-4] == "pt22k":
  # masked image modeling
  _snake_case      :					Optional[int]					=   True
  _snake_case      :					Union[str, Any]					=   True
  _snake_case      :					Dict					=   True
 elif checkpoint_url[-9:-4] == "ft22k":
  # intermediate fine-tuning on ImageNet-22k
  _snake_case      :					List[str]					=   True
  _snake_case      :					List[Any]					=   21_841
  _snake_case      :					Optional[Any]					=   """imagenet-22k-id2label.json"""
  _snake_case      :					Optional[int]					=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    repo_type="""dataset"""			)			,    """r"""			)			)
  _snake_case      :					str					=   {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
  # this dataset contains 21843 labels but the model only has 21841
  # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  del idalabel[9_205]
  del idalabel[15_027]
  _snake_case      :					str					=   idalabel
  _snake_case      :					int					=   {v: k for k, v in idalabel.items()}
 elif checkpoint_url[-8:-4] == "to1k":
  # fine-tuning on ImageNet-1k
  _snake_case      :					Any					=   True
  _snake_case      :					List[Any]					=   1_000
  _snake_case      :					int					=   """imagenet-1k-id2label.json"""
  _snake_case      :					Dict					=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    repo_type="""dataset"""			)			,    """r"""			)			)
  _snake_case      :					List[str]					=   {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
  _snake_case      :					Any					=   idalabel
  _snake_case      :					Dict					=   {v: k for k, v in idalabel.items()}
  if "384" in checkpoint_url:
   _snake_case      :					Any					=   384
  if "512" in checkpoint_url:
   _snake_case      :					List[str]					=   512
 elif "ade20k" in checkpoint_url:
  # fine-tuning
  _snake_case      :					Optional[Any]					=   True
  _snake_case      :					List[str]					=   150
  _snake_case      :					Any					=   """ade20k-id2label.json"""
  _snake_case      :					int					=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    repo_type="""dataset"""			)			,    """r"""			)			)
  _snake_case      :					Optional[Any]					=   {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
  _snake_case      :					Any					=   idalabel
  _snake_case      :					Optional[Any]					=   {v: k for k, v in idalabel.items()}
  _snake_case      :					int					=   640
  _snake_case      :					List[Any]					=   True
 else:
  raise ValueError("""Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"""			)

 # size of the architecture
 if "base" in checkpoint_url:
  pass
 elif "large" in checkpoint_url:
  _snake_case      :					List[Any]					=   1_024
  _snake_case      :					int					=   4_096
  _snake_case      :					Union[str, Any]					=   24
  _snake_case      :					Optional[int]					=   16
  if "ade20k" in checkpoint_url:
   _snake_case      :					Any					=   640
   _snake_case      :					Tuple					=   [7, 11, 15, 23]
 else:
  raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""			)

 # load state_dict of original model, remove and rename some keys
 _snake_case      :					Any					=   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__			,    map_location="""cpu"""			,    check_hash=SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Optional[int]					=   state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

 _snake_case      :					Union[str, Any]					=   create_rename_keys(SCREAMING_SNAKE_CASE__			,    has_lm_head=SCREAMING_SNAKE_CASE__			,    is_semantic=SCREAMING_SNAKE_CASE__			)
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
 read_in_q_k_v(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    has_lm_head=SCREAMING_SNAKE_CASE__			,    is_semantic=SCREAMING_SNAKE_CASE__			)
 if is_semantic:
  # add prefix to decoder keys
  for key, val in state_dict.copy().items():
   _snake_case      :					int					=   state_dict.pop(SCREAMING_SNAKE_CASE__			)
   if key.startswith("""backbone.fpn"""			):
    _snake_case      :					Union[str, Any]					=   key.replace("""backbone.fpn"""			,    """fpn"""			)
   _snake_case      :					int					=   val

    # load HuggingFace model
 if checkpoint_url[-9:-4] == "pt22k":
  _snake_case      :					List[str]					=   BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE__			)
 elif "ade20k" in checkpoint_url:
  _snake_case      :					Tuple					=   BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE__			)
 else:
  _snake_case      :					str					=   BeitForImageClassification(SCREAMING_SNAKE_CASE__			)
 model.eval()
 model.load_state_dict(SCREAMING_SNAKE_CASE__			)

 # Check outputs on an image
 if is_semantic:
  _snake_case      :					int					=   BeitImageProcessor(size=config.image_size			,    do_center_crop=SCREAMING_SNAKE_CASE__			)
  _snake_case      :					Any					=   load_dataset("""hf-internal-testing/fixtures_ade20k"""			,    split="""test"""			)
  _snake_case      :					Any					=   Image.open(ds[0]["""file"""]			)
 else:
  _snake_case      :					Union[str, Any]					=   BeitImageProcessor(
      size=config.image_size			,    resample=PILImageResampling.BILINEAR			,    do_center_crop=SCREAMING_SNAKE_CASE__			)
  _snake_case      :					List[str]					=   prepare_img()

 _snake_case      :					Tuple					=   image_processor(images=SCREAMING_SNAKE_CASE__			,    return_tensors="""pt"""			)
 _snake_case      :					int					=   encoding["""pixel_values"""]

 _snake_case      :					str					=   model(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Tuple					=   outputs.logits

 # verify logits
 _snake_case      :					int					=   torch.Size([1, 1_000]			)
 if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""			):
  _snake_case      :					int					=   torch.Size([1, 196, 8_192]			)
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""			):
  _snake_case      :					Any					=   torch.Size([1, 196, 8_192]			)
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""			):
  _snake_case      :					Optional[int]					=   torch.Size([1, 21_841]			)
  _snake_case      :					Union[str, Any]					=   torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]			)
  _snake_case      :					List[str]					=   2_397
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""			):
  _snake_case      :					Union[str, Any]					=   torch.Size([1, 21_841]			)
  _snake_case      :					List[Any]					=   torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			)
  _snake_case      :					Optional[int]					=   2_396
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""			):
  _snake_case      :					Optional[int]					=   torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]			)
  _snake_case      :					Any					=   285
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""			):
  _snake_case      :					Dict					=   torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			)
  _snake_case      :					int					=   281
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""			):
  _snake_case      :					Optional[Any]					=   torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]			)
  _snake_case      :					int					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""			):
  _snake_case      :					Union[str, Any]					=   torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]			)
  _snake_case      :					Optional[int]					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""			):
  _snake_case      :					Dict					=   torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]			)
  _snake_case      :					Union[str, Any]					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""			):
  _snake_case      :					List[Any]					=   torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]			)
  _snake_case      :					int					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""			):
  _snake_case      :					List[str]					=   torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]			)
  _snake_case      :					Tuple					=   761
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""			):
  _snake_case      :					Dict					=   (1, 150, 160, 160)
  _snake_case      :					Dict					=   torch.tensor(
      [
          [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
          [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
          [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
      ]			)
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""			):
  _snake_case      :					Tuple					=   (1, 150, 160, 160)
  _snake_case      :					str					=   torch.tensor(
      [
          [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
          [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
          [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
      ]			)
 else:
  raise ValueError("""Can't verify logits as model is not supported"""			)

 assert logits.shape == expected_shape, "Shape of logits not as expected"
 if not has_lm_head:
  if is_semantic:
   assert torch.allclose(
       logits[0, :3, :3, :3]			,    SCREAMING_SNAKE_CASE__			,    atol=1e-3			), "First elements of logits not as expected"
  else:
   print("""Predicted class idx:"""			,    logits.argmax(-1			).item()			)
   assert torch.allclose(
       logits[0, :3]			,    SCREAMING_SNAKE_CASE__			,    atol=1e-3			), "First elements of logits not as expected"
   assert logits.argmax(-1			).item() == expected_class_idx, "Predicted class index not as expected"

 Path(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
 print(F'''Saving model to {pytorch_dump_folder_path}'''			)
 model.save_pretrained(SCREAMING_SNAKE_CASE__			)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
  a__      =		argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth""",
      type=str,
      help="""URL to the original PyTorch checkpoint (.pth file).""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  a__      =		parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
