






"""simple docstring"""


import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
   import torch






@require_flax
class __a :



  def __init__(self	:		List[str]					,      lowercase_	:		Tuple					,      lowercase_	:		Tuple=14					,      lowercase_	:		Tuple=7					,      lowercase_	:		Union[str, Any]=True					,      lowercase_	:		str=True					,      lowercase_	:		Optional[Any]=True					,      lowercase_	:		Dict=99					,      lowercase_	:		List[Any]=32					,      lowercase_	:		List[str]=5					,      lowercase_	:		List[Any]=4					,      lowercase_	:		int=37					,      lowercase_	:		Dict="gelu"					,      lowercase_	:		Tuple=0.1					,      lowercase_	:		Union[str, Any]=0.1					,      lowercase_	:		Optional[Any]=512					,      lowercase_	:		Optional[Any]=0.02					,      lowercase_	:		Optional[int]=None					,      )		->				str:
   __A						:							str      =						parent
   __A						:							Union[str, Any]      =						batch_size
   __A						:							List[str]      =						seq_length
   __A						:							Any      =						is_training
   __A						:							List[Any]      =						use_input_mask
   __A						:							List[str]      =						use_labels
   __A						:							Any      =						vocab_size
   __A						:							List[Any]      =						d_model
   __A						:							Tuple      =						num_hidden_layers
   __A						:							Optional[int]      =						num_attention_heads
   __A						:							Union[str, Any]      =						ffn_dim
   __A						:							Tuple      =						activation_function
   __A						:							Union[str, Any]      =						activation_dropout
   __A						:							Optional[Any]      =						attention_dropout
   __A						:							Tuple      =						max_position_embeddings
   __A						:							Tuple      =						initializer_range
   __A						:							str      =						None
   __A						:							Optional[int]      =						0
   __A						:							Optional[Any]      =						2
   __A						:							Tuple      =						1



  def     A_					(self	:		Optional[int] )		->				Union[str, Any]:
   __A						:							str      =						np.clip(ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size )					,      3					,      self.vocab_size )

   __A						:							List[str]      =						None
   if self.use_input_mask:
    __A						:							str      =						random_attention_mask([self.batch_size, self.seq_length] )

   __A						:							Any      =						XGLMConfig(
       vocab_size=self.vocab_size					,      d_model=self.hidden_size					,      num_layers=self.num_hidden_layers					,      attention_heads=self.num_attention_heads					,      ffn_dim=self.ffn_dim					,      activation_function=self.activation_function					,      activation_dropout=self.activation_dropout					,      attention_dropout=self.attention_dropout					,      max_position_embeddings=self.max_position_embeddings					,      initializer_range=self.initializer_range					,      use_cache=SCREAMING_SNAKE_CASE_					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      )

   return (config, input_ids, input_mask)



  def     A_					(self	:		str )		->				Optional[int]:
   __A						:							Optional[int]      =						self.prepare_config_and_inputs()
   __A     ,       __A     ,       __A						:							str      =						config_and_inputs
   __A						:							Union[str, Any]      =						{'input_ids': input_ids, 'attention_mask': attention_mask}
   return config, inputs_dict



  def     A_					(self	:		Any )		->				Any:
   __A     ,       __A     ,       __A						:							Dict      =						self.prepare_config_and_inputs()

   __A						:							Optional[int]      =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
   __A						:							List[str]      =						ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2 )

   return (
       config,
       input_ids,
       attention_mask,
       encoder_hidden_states,
       encoder_attention_mask,
   )



  def     A_					(self	:		Any					,      lowercase_	:		int					,      lowercase_	:		Tuple					,      lowercase_	:		Tuple					,      lowercase_	:		Optional[int] )		->				Dict:
   __A						:							Tuple      =						20
   __A						:							List[str]      =						model_class_name(SCREAMING_SNAKE_CASE_ )

   __A						:							Tuple      =						model.init_cache(input_ids.shape[0]					,      SCREAMING_SNAKE_CASE_ )
   __A						:							Tuple      =						jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype='i4' )

   __A						:							Optional[int]      =						jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1 )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1) )
   __A						:							Optional[Any]      =						model(
       input_ids[:, :-1]					,      attention_mask=SCREAMING_SNAKE_CASE_					,      past_key_values=SCREAMING_SNAKE_CASE_					,      position_ids=SCREAMING_SNAKE_CASE_					,      )

   __A						:							Tuple      =						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype='i4' )
   __A						:							Any      =						model(
       input_ids[:, -1:]					,      attention_mask=SCREAMING_SNAKE_CASE_					,      past_key_values=outputs_cache.past_key_values					,      position_ids=SCREAMING_SNAKE_CASE_					,      )

   __A						:							List[str]      =						model(SCREAMING_SNAKE_CASE_ )

   __A						:							Optional[Any]      =						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
   self.parent.assertTrue(diff < 1E-3					,      msg=f"Max diff is {diff}" )



  def     A_					(self	:		Dict					,      lowercase_	:		Optional[int]					,      lowercase_	:		Optional[int]					,      lowercase_	:		Any					,      lowercase_	:		List[Any] )		->				int:
   __A						:							int      =						20
   __A						:							Tuple      =						model_class_name(SCREAMING_SNAKE_CASE_ )

   __A						:							List[str]      =						jnp.concatenate(
       [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]					,      axis=-1					,      )

   __A						:							str      =						model.init_cache(input_ids.shape[0]					,      SCREAMING_SNAKE_CASE_ )
   __A						:							Dict      =						jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1 )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1) )

   __A						:							str      =						model(
       input_ids[:, :-1]					,      attention_mask=SCREAMING_SNAKE_CASE_					,      past_key_values=SCREAMING_SNAKE_CASE_					,      position_ids=SCREAMING_SNAKE_CASE_					,      )
   __A						:							Union[str, Any]      =						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype='i4' )
   __A						:							Dict      =						model(
       input_ids[:, -1:]					,      past_key_values=outputs_cache.past_key_values					,      attention_mask=SCREAMING_SNAKE_CASE_					,      position_ids=SCREAMING_SNAKE_CASE_					,      )

   __A						:							Tuple      =						model(SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_ )
   __A						:							Any      =						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
   self.parent.assertTrue(diff < 1E-3					,      msg=f"Max diff is {diff}" )






@require_sentencepiece
@require_flax
class __a ( SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,unittest.TestCase					):
  __lowercase     :     List[str]       						=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  __lowercase     :     List[str]       						=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()



  def     A_					(self	:		List[Any] )		->				Any:
   __A						:							Union[str, Any]      =						FlaxXGLMModelTester(self )



  def     A_					(self	:		Optional[Any] )		->				Tuple:






   for model_class_name in self.all_model_classes:
    __A     ,       __A     ,       __A						:							Dict      =						self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )



  def     A_					(self	:		Dict )		->				List[str]:






   for model_class_name in self.all_model_classes:
    __A     ,       __A     ,       __A						:							Optional[int]      =						self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward_with_attn_mask(
        SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )



  @slow
  def     A_					(self	:		Optional[Any] )		->				List[str]:
   __A						:							str      =						XGLMTokenizer.from_pretrained('XGLM'					,      padding_side='left' )
   __A						:							Optional[Any]      =						tokenizer(['Hello this is a long string', 'Hey']					,      return_tensors='np'					,      padding=SCREAMING_SNAKE_CASE_					,      truncation=SCREAMING_SNAKE_CASE_ )

   __A						:							str      =						FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
   __A						:							Optional[Any]      =						1
   __A						:							Tuple      =						False

   __A						:							Optional[Any]      =						jax.jit(model.generate )

   __A						:							List[str]      =						jit_generate(inputs['input_ids']					,      attention_mask=inputs['attention_mask'] ).sequences

   __A						:							str      =						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					,      skip_special_tokens=SCREAMING_SNAKE_CASE_ )

   __A						:							Any      =						[
       'Hello this is a long string of questions, but I\'m not sure if I\'m',
       'Hey, I\'m a newbie to the forum and I\'',
   ]

   self.assertListEqual(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )



  @is_pt_flax_cross_test
  def     A_					(self	:		Optional[Any] )		->				Optional[Any]:
   __A     ,       __A						:							Dict      =						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__ ):
     # prepare inputs
     __A						:							int      =						self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )
     __A						:							Tuple      =						{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

     # load corresponding PyTorch class
     __A						:							int      =						model_class.__name__[4:]  # Skip the "Flax" at the beginning
     __A						:							Union[str, Any]      =						getattr(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )

     __A     ,       __A						:							str      =						pt_inputs['input_ids'].shape
     __A						:							str      =						np.random.randint(0					,      seq_length - 1					,      size=(batch_size,) )
     for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_ ):
      __A						:							List[str]      =						0
      __A						:							Dict      =						1
      __A						:							Optional[Any]      =						0
      __A						:							Tuple      =						1
     __A						:							Any      =						pt_model_class(SCREAMING_SNAKE_CASE_ ).eval()
     # Flax models don't use the `use_cache` option and cache is not returned as a default.
     # So we disable `use_cache` here for PyTorch model.
     __A						:							int      =						False
     __A						:							List[str]      =						model_class(SCREAMING_SNAKE_CASE_					,      dtype=jnp.floataa )
     __A						:							Union[str, Any]      =						convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      SCREAMING_SNAKE_CASE_ )
     __A						:							List[str]      =						fx_state

     with torch.no_grad():
      __A						:							str      =						pt_model(**SCREAMING_SNAKE_CASE_ ).to_tuple()

     __A						:							Optional[Any]      =						fx_model(**SCREAMING_SNAKE_CASE_ ).to_tuple()
     self.assertEqual(len(SCREAMING_SNAKE_CASE_ )					,      len(SCREAMING_SNAKE_CASE_ )					,      'Output lengths differ between Flax and PyTorch' )
     for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ ):
      self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4E-2 )

     with tempfile.TemporaryDirectory() as tmpdirname:
      pt_model.save_pretrained(SCREAMING_SNAKE_CASE_ )
      __A						:							Dict      =						model_class.from_pretrained(SCREAMING_SNAKE_CASE_					,      from_pt=SCREAMING_SNAKE_CASE_ )

     __A						:							Any      =						fx_model_loaded(**SCREAMING_SNAKE_CASE_ ).to_tuple()
     self.assertEqual(
         len(SCREAMING_SNAKE_CASE_ )					,      len(SCREAMING_SNAKE_CASE_ )					,      'Output lengths differ between Flax and PyTorch' )
     for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ ):
      self.assert_almost_equals(fx_output_loaded[:, -1]					,      pt_output[:, -1].numpy()					,      4E-2 )



  @is_pt_flax_cross_test
  def     A_					(self	:		str )		->				Tuple:
   __A     ,       __A						:							List[str]      =						self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__ ):
     # prepare inputs
     __A						:							str      =						self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )
     __A						:							Any      =						{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

     # load corresponding PyTorch class
     __A						:							Optional[int]      =						model_class.__name__[4:]  # Skip the "Flax" at the beginning
     __A						:							str      =						getattr(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )

     __A						:							Union[str, Any]      =						pt_model_class(SCREAMING_SNAKE_CASE_ ).eval()
     __A						:							int      =						False
     __A						:							List[str]      =						model_class(SCREAMING_SNAKE_CASE_					,      dtype=jnp.floataa )

     __A						:							Any      =						load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_					,      fx_model.params )
     __A     ,       __A						:							Any      =						pt_inputs['input_ids'].shape
     __A						:							Any      =						np.random.randint(0					,      seq_length - 1					,      size=(batch_size,) )
     for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_ ):
      __A						:							Any      =						0
      __A						:							List[str]      =						1
      __A						:							str      =						0
      __A						:							List[str]      =						1

     # make sure weights are tied in PyTorch
     pt_model.tie_weights()

     with torch.no_grad():
      __A						:							Any      =						pt_model(**SCREAMING_SNAKE_CASE_ ).to_tuple()

     __A						:							Any      =						fx_model(**SCREAMING_SNAKE_CASE_ ).to_tuple()
     self.assertEqual(len(SCREAMING_SNAKE_CASE_ )					,      len(SCREAMING_SNAKE_CASE_ )					,      'Output lengths differ between Flax and PyTorch' )
     for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ ):
      self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4E-2 )

     with tempfile.TemporaryDirectory() as tmpdirname:
      fx_model.save_pretrained(SCREAMING_SNAKE_CASE_ )
      __A						:							Union[str, Any]      =						pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_					,      from_flax=SCREAMING_SNAKE_CASE_ )

     with torch.no_grad():
      __A						:							Any      =						pt_model_loaded(**SCREAMING_SNAKE_CASE_ ).to_tuple()

     self.assertEqual(
         len(SCREAMING_SNAKE_CASE_ )					,      len(SCREAMING_SNAKE_CASE_ )					,      'Output lengths differ between Flax and PyTorch' )
     for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ ):
      self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4E-2 )



  @slow
  def     A_					(self	:		Any )		->				Optional[int]:






   for model_class_name in self.all_model_classes:
    __A						:							Optional[Any]      =						model_class_name.from_pretrained('facebook/xglm-564M' )
    __A						:							str      =						model(np.ones((1, 1) ) )
    self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )







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()
lowercase__				:				List[Any]						=      logging.get_logger(__name__)




def 						A_			(				snake_case				: int			,   snake_case				: Tuple=False			,   snake_case				: List[Any]=False			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      '''backbone.''' if is_semantic else ''''''

						__UpperCamelCase  	=      []
						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 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: List[Any]			,   snake_case				: Union[str, Any]=False			,   snake_case				: List[str]=False			)   ->     Union[str, Any]:



						'''simple docstring'''

						for i in range(config.num_hidden_layers			):
												__UpperCamelCase  	=      '''backbone.''' if is_semantic else ''''''
												# queries, keys and values
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"			)

												__UpperCamelCase  	=      in_proj_weight[
												    : config.hidden_size, :
												]
												__UpperCamelCase  	=      q_bias
												__UpperCamelCase  	=      in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__UpperCamelCase  	=      in_proj_weight[
												    -config.hidden_size :, :
												]
												__UpperCamelCase  	=      v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.gamma_1"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.gamma_2"			)

												__UpperCamelCase  	=      gamma_a
												__UpperCamelCase  	=      gamma_a

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

																		__UpperCamelCase  	=      table
																		__UpperCamelCase  	=      index




def 						A_			(				snake_case				: Dict			,   snake_case				: Optional[int]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      dct.pop(snake_case			)
						__UpperCamelCase  	=      val




def 						A_			(				)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCamelCase  	=      Image.open(requests.get(snake_case			,   stream=snake_case			).raw			)
						return im




@torch.no_grad()
def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: Any			)   ->     Tuple:



						'''simple docstring'''

						__UpperCamelCase  	=      BeitConfig()
						__UpperCamelCase  	=      False
						__UpperCamelCase  	=      False
						__UpperCamelCase  	=      '''huggingface/label-files'''
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      21841
												__UpperCamelCase  	=      '''imagenet-22k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(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[9205]
												del idalabel[15027]
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      1000
												__UpperCamelCase  	=      '''imagenet-1k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		__UpperCamelCase  	=      384
												if "512" in checkpoint_url:
																		__UpperCamelCase  	=      512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      150
												__UpperCamelCase  	=      '''ade20k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
												__UpperCamelCase  	=      640
												__UpperCamelCase  	=      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:
												__UpperCamelCase  	=      1024
												__UpperCamelCase  	=      4096
												__UpperCamelCase  	=      24
												__UpperCamelCase  	=      16
												if "ade20k" in checkpoint_url:
																		__UpperCamelCase  	=      640
																		__UpperCamelCase  	=      [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
						__UpperCamelCase  	=      torch.hub.load_state_dict_from_url(snake_case			,   map_location='''cpu'''			,   check_hash=snake_case			)
						__UpperCamelCase  	=      state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

						__UpperCamelCase  	=      create_rename_keys(snake_case			,   has_lm_head=snake_case			,   is_semantic=snake_case			)
						for src, dest in rename_keys:
												rename_key(snake_case			,   snake_case			,   snake_case			)
						read_in_q_k_v(snake_case			,   snake_case			,   has_lm_head=snake_case			,   is_semantic=snake_case			)
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		__UpperCamelCase  	=      state_dict.pop(snake_case			)
																		if key.startswith('''backbone.fpn'''			):
																								__UpperCamelCase  	=      key.replace('''backbone.fpn'''			,   '''fpn'''			)
																		__UpperCamelCase  	=      val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												__UpperCamelCase  	=      BeitForMaskedImageModeling(snake_case			)
						elif "ade20k" in checkpoint_url:
												__UpperCamelCase  	=      BeitForSemanticSegmentation(snake_case			)
						else:
												__UpperCamelCase  	=      BeitForImageClassification(snake_case			)
						model.eval()
						model.load_state_dict(snake_case			)

						# Check outputs on an image
						if is_semantic:
												__UpperCamelCase  	=      BeitImageProcessor(size=config.image_size			,   do_center_crop=snake_case			)
												__UpperCamelCase  	=      load_dataset('''hf-internal-testing/fixtures_ade20k'''			,   split='''test'''			)
												__UpperCamelCase  	=      Image.open(ds[0]['''file''']			)
						else:
												__UpperCamelCase  	=      BeitImageProcessor(
												    size=config.image_size			,   resample=PILImageResampling.BILINEAR			,   do_center_crop=snake_case			)
												__UpperCamelCase  	=      prepare_img()

						__UpperCamelCase  	=      image_processor(images=snake_case			,   return_tensors='''pt'''			)
						__UpperCamelCase  	=      encoding['''pixel_values''']

						__UpperCamelCase  	=      model(snake_case			)
						__UpperCamelCase  	=      outputs.logits

						# verify logits
						__UpperCamelCase  	=      torch.Size([1, 1000]			)
						if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 196, 8192]			)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 196, 8192]			)
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 21841]			)
												__UpperCamelCase  	=      torch.tensor([2.2288, 2.4671, 0.7395]			)
												__UpperCamelCase  	=      2397
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 21841]			)
												__UpperCamelCase  	=      torch.tensor([1.6881, -0.2787, 0.5901]			)
												__UpperCamelCase  	=      2396
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''			):
												__UpperCamelCase  	=      torch.tensor([0.1241, 0.0798, -0.6569]			)
												__UpperCamelCase  	=      285
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-1.2385, -1.0987, -1.0108]			)
												__UpperCamelCase  	=      281
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-1.5303, -0.9484, -0.3147]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''			):
												__UpperCamelCase  	=      torch.tensor([0.4610, -0.0928, 0.2086]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-0.4804, 0.6257, -0.1837]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([[-0.5122, 0.5117, -0.2113]]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-0.3062, 0.7261, 0.4852]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''			):
												__UpperCamelCase  	=      (1, 150, 160, 160)
												__UpperCamelCase  	=      torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]			)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''			):
												__UpperCamelCase  	=      (1, 150, 160, 160)
												__UpperCamelCase  	=      torch.tensor(
												    [
												        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
												        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
												        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
												    ]			)
						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]			,   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]			,   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(snake_case			).mkdir(exist_ok=snake_case			)
						print(f"Saving model to {pytorch_dump_folder_path}"			)
						model.save_pretrained(snake_case			)
						print(f"Saving image processor to {pytorch_dump_folder_path}"			)
						image_processor.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Any						=      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."
				)
				lowercase__				:				int						=      parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




