





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


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

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
      import torch

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					int		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Union[str, Any]=14		,lowerCamelCase__		:					Optional[Any]=7		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					Optional[int]=True		,lowerCamelCase__		:					Optional[Any]=False		,lowerCamelCase__		:					str=True		,lowerCamelCase__		:					str=99		,lowerCamelCase__		:					Dict=32		,lowerCamelCase__		:					Tuple=4		,lowerCamelCase__		:					int=4		,lowerCamelCase__		:					Optional[int]=4		,lowerCamelCase__		:					List[str]=37		,lowerCamelCase__		:					Dict="gelu"		,lowerCamelCase__		:					str=0.1		,lowerCamelCase__		:					Optional[Any]=0.1		,lowerCamelCase__		:					Any=512		,lowerCamelCase__		:					List[Any]=0.02		,)  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		seq_length
           SCREAMING_SNAKE_CASE	=		is_training
           SCREAMING_SNAKE_CASE	=		use_input_mask
           SCREAMING_SNAKE_CASE	=		use_token_type_ids
           SCREAMING_SNAKE_CASE	=		use_labels
           SCREAMING_SNAKE_CASE	=		vocab_size
           SCREAMING_SNAKE_CASE	=		hidden_size
           SCREAMING_SNAKE_CASE	=		rotary_dim
           SCREAMING_SNAKE_CASE	=		num_hidden_layers
           SCREAMING_SNAKE_CASE	=		num_attention_heads
           SCREAMING_SNAKE_CASE	=		intermediate_size
           SCREAMING_SNAKE_CASE	=		hidden_act
           SCREAMING_SNAKE_CASE	=		hidden_dropout_prob
           SCREAMING_SNAKE_CASE	=		attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE	=		max_position_embeddings
           SCREAMING_SNAKE_CASE	=		initializer_range
           SCREAMING_SNAKE_CASE	=		None
           SCREAMING_SNAKE_CASE	=		vocab_size - 1
           SCREAMING_SNAKE_CASE	=		vocab_size - 1
           SCREAMING_SNAKE_CASE	=		vocab_size - 1






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size )

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

           SCREAMING_SNAKE_CASE	=		GPTJConfig(
               vocab_size=self.vocab_size		,n_embd=self.hidden_size		,n_layer=self.num_hidden_layers		,n_head=self.num_attention_heads		,n_positions=self.max_position_embeddings		,use_cache=lowerCAmelCase_		,bos_token_id=self.bos_token_id		,eos_token_id=self.eos_token_id		,pad_token_id=self.pad_token_id		,rotary_dim=self.rotary_dim		,)

           return (config, input_ids, input_mask)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{"""input_ids""": input_ids, """attention_mask""": attention_mask}
           return config, inputs_dict






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					List[str] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		20
           SCREAMING_SNAKE_CASE	=		model_class_name(lowerCAmelCase_ )

           SCREAMING_SNAKE_CASE	=		model.init_cache(input_ids.shape[0]		,lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		jnp.ones((input_ids.shape[0], max_decoder_length)		,dtype="""i4""" )

           SCREAMING_SNAKE_CASE	=		jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,(input_ids.shape[0], input_ids.shape[-1] - 1) )
           SCREAMING_SNAKE_CASE	=		model(
               input_ids[:, :-1]		,attention_mask=lowerCAmelCase_		,past_key_values=lowerCAmelCase_		,position_ids=lowerCAmelCase_		,)

           SCREAMING_SNAKE_CASE	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,dtype="""i4""" )
           SCREAMING_SNAKE_CASE	=		model(
               input_ids[:, -1:]		,attention_mask=lowerCAmelCase_		,past_key_values=outputs_cache.past_key_values		,position_ids=lowerCAmelCase_		,)

           SCREAMING_SNAKE_CASE	=		model(lowerCAmelCase_ )

           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Union[str, Any] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		20
           SCREAMING_SNAKE_CASE	=		model_class_name(lowerCAmelCase_ )

           SCREAMING_SNAKE_CASE	=		jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]		,axis=-1		,)

           SCREAMING_SNAKE_CASE	=		model.init_cache(input_ids.shape[0]		,lowerCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,(input_ids.shape[0], input_ids.shape[-1] - 1) )

           SCREAMING_SNAKE_CASE	=		model(
               input_ids[:, :-1]		,attention_mask=lowerCAmelCase_		,past_key_values=lowerCAmelCase_		,position_ids=lowerCAmelCase_		,)
           SCREAMING_SNAKE_CASE	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,dtype="""i4""" )
           SCREAMING_SNAKE_CASE	=		model(
               input_ids[:, -1:]		,past_key_values=outputs_cache.past_key_values		,attention_mask=lowerCAmelCase_		,position_ids=lowerCAmelCase_		,)

           SCREAMING_SNAKE_CASE	=		model(lowerCAmelCase_		,attention_mask=lowerCAmelCase_ )

           SCREAMING_SNAKE_CASE	=		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_flax
class 	UpperCamelCase__				(   __UpperCAmelCase							,   __UpperCAmelCase							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			List[str]				=    (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     __snake_case					:			Union[str, Any]				=    (FlaxGPTJForCausalLM,) if is_flax_available() else ()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		FlaxGPTJModelTester(self )






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




           '''simple docstring'''


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_ )






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




           '''simple docstring'''


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_ )






     @tooslow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		GPTaTokenizer.from_pretrained("""gpt2"""		,pad_token="""<|endoftext|>"""		,padding_side="""left""" )
           SCREAMING_SNAKE_CASE	=		tokenizer(["""Hello this is a long string""", """Hey"""]		,return_tensors="""np"""		,padding=lowerCAmelCase_		,truncation=lowerCAmelCase_ )

           SCREAMING_SNAKE_CASE	=		FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		model.config.eos_token_id

           SCREAMING_SNAKE_CASE	=		jax.jit(model.generate )

           SCREAMING_SNAKE_CASE	=		jit_generate(
               inputs["""input_ids"""]		,attention_mask=inputs["""attention_mask"""]		,pad_token_id=tokenizer.pad_token_id ).sequences

           SCREAMING_SNAKE_CASE	=		tokenizer.batch_decode(lowerCAmelCase_		,skip_special_tokens=lowerCAmelCase_ )

           SCREAMING_SNAKE_CASE	=		[
               """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
               """Hey, I'm a little late to the party. I'm going to""",
           ]

           self.assertListEqual(lowerCAmelCase_		,lowerCAmelCase_ )






     @is_pt_flax_cross_test
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		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
                       SCREAMING_SNAKE_CASE	=		self._prepare_for_class(lowerCAmelCase_		,lowerCAmelCase_ )
                       SCREAMING_SNAKE_CASE	=		{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                       # load corresponding PyTorch class
                       SCREAMING_SNAKE_CASE	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       SCREAMING_SNAKE_CASE	=		getattr(lowerCAmelCase_		,lowerCAmelCase_ )

                       SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		pt_inputs["""input_ids"""].shape
                       SCREAMING_SNAKE_CASE	=		np.random.randint(0		,seq_length - 1		,size=(batch_size,) )
                       for batch_idx, start_index in enumerate(lowerCAmelCase_ ):
                             SCREAMING_SNAKE_CASE	=		0
                             SCREAMING_SNAKE_CASE	=		1
                             SCREAMING_SNAKE_CASE	=		0
                             SCREAMING_SNAKE_CASE	=		1
                       SCREAMING_SNAKE_CASE	=		pt_model_class(lowerCAmelCase_ ).eval()
                       SCREAMING_SNAKE_CASE	=		model_class(lowerCAmelCase_		,dtype=jnp.floataa )

                       SCREAMING_SNAKE_CASE	=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,lowerCAmelCase_ )
                       SCREAMING_SNAKE_CASE	=		fx_state

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE	=		pt_model(**lowerCAmelCase_ ).to_tuple()

                       SCREAMING_SNAKE_CASE	=		fx_model(**lowerCAmelCase_ ).to_tuple()
                       self.assertEqual(len(lowerCAmelCase_ )		,len(lowerCAmelCase_ )		,"""Output lengths differ between Flax and PyTorch""" )
                       for fx_output, pt_output in zip(lowerCAmelCase_		,lowerCAmelCase_ ):
                             self.assert_almost_equals(fx_output[:, -1]		,pt_output[:, -1].numpy()		,4e-2 )

                       with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(lowerCAmelCase_ )
                             SCREAMING_SNAKE_CASE	=		model_class.from_pretrained(lowerCAmelCase_		,from_pt=lowerCAmelCase_ )

                       SCREAMING_SNAKE_CASE	=		fx_model_loaded(**lowerCAmelCase_ ).to_tuple()
                       self.assertEqual(
                           len(lowerCAmelCase_ )		,len(lowerCAmelCase_ )		,"""Output lengths differ between Flax and PyTorch""" )
                       for fx_output_loaded, pt_output in zip(lowerCAmelCase_		,lowerCAmelCase_ ):
                             self.assert_almost_equals(fx_output_loaded[:, -1]		,pt_output[:, -1].numpy()		,4e-2 )






     @is_pt_flax_cross_test
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		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
                       SCREAMING_SNAKE_CASE	=		self._prepare_for_class(lowerCAmelCase_		,lowerCAmelCase_ )
                       SCREAMING_SNAKE_CASE	=		{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                       # load corresponding PyTorch class
                       SCREAMING_SNAKE_CASE	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       SCREAMING_SNAKE_CASE	=		getattr(lowerCAmelCase_		,lowerCAmelCase_ )

                       SCREAMING_SNAKE_CASE	=		pt_model_class(lowerCAmelCase_ ).eval()
                       SCREAMING_SNAKE_CASE	=		model_class(lowerCAmelCase_		,dtype=jnp.floataa )

                       SCREAMING_SNAKE_CASE	=		load_flax_weights_in_pytorch_model(lowerCAmelCase_		,fx_model.params )
                       SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		pt_inputs["""input_ids"""].shape
                       SCREAMING_SNAKE_CASE	=		np.random.randint(0		,seq_length - 1		,size=(batch_size,) )
                       for batch_idx, start_index in enumerate(lowerCAmelCase_ ):
                             SCREAMING_SNAKE_CASE	=		0
                             SCREAMING_SNAKE_CASE	=		1
                             SCREAMING_SNAKE_CASE	=		0
                             SCREAMING_SNAKE_CASE	=		1

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

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE	=		pt_model(**lowerCAmelCase_ ).to_tuple()

                       SCREAMING_SNAKE_CASE	=		fx_model(**lowerCAmelCase_ ).to_tuple()
                       self.assertEqual(len(lowerCAmelCase_ )		,len(lowerCAmelCase_ )		,"""Output lengths differ between Flax and PyTorch""" )
                       for fx_output, pt_output in zip(lowerCAmelCase_		,lowerCAmelCase_ ):
                             self.assert_almost_equals(fx_output[:, -1]		,pt_output[:, -1].numpy()		,4e-2 )

                       with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(lowerCAmelCase_ )
                             SCREAMING_SNAKE_CASE	=		pt_model_class.from_pretrained(lowerCAmelCase_		,from_flax=lowerCAmelCase_ )

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE	=		pt_model_loaded(**lowerCAmelCase_ ).to_tuple()

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






     @tooslow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	int:




           '''simple docstring'''


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE	=		model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
                 SCREAMING_SNAKE_CASE	=		model(np.ones((1, 1) ) )
                 self.assertIsNotNone(lowerCAmelCase_ )









import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[2, 2, 6, 2] if """tiny""" in model_name else [2, 2, 18, 2]
      SCREAMING_SNAKE_CASE	=		True if """large""" in model_name or """huge""" in model_name else False
      SCREAMING_SNAKE_CASE	=		True if """large""" in model_name or """huge""" in model_name else False
      SCREAMING_SNAKE_CASE	=		True if """large""" in model_name or """huge""" in model_name else False

      if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
            if "fl3" in model_name:
                  SCREAMING_SNAKE_CASE	=		[3, 3, 3, 3]
                  SCREAMING_SNAKE_CASE	=		[5, 5, 5, 5]
            elif "fl4" in model_name:
                  SCREAMING_SNAKE_CASE	=		[4, 4, 4, 4]
                  SCREAMING_SNAKE_CASE	=		[3, 3, 3, 3]

      if "tiny" in model_name or "small" in model_name or "base" in model_name:
            SCREAMING_SNAKE_CASE	=		[3, 3, 3, 3]
            if "lrf" in model_name:
                  SCREAMING_SNAKE_CASE	=		[3, 3, 3, 3]
            else:
                  SCREAMING_SNAKE_CASE	=		[2, 2, 2, 2]

      if "tiny" in model_name:
            SCREAMING_SNAKE_CASE	=		96
      elif "small" in model_name:
            SCREAMING_SNAKE_CASE	=		96
      elif "base" in model_name:
            SCREAMING_SNAKE_CASE	=		1_28
      elif "large" in model_name:
            SCREAMING_SNAKE_CASE	=		1_92
      elif "xlarge" in model_name:
            SCREAMING_SNAKE_CASE	=		2_56
      elif "huge" in model_name:
            SCREAMING_SNAKE_CASE	=		3_52

      # set label information
      SCREAMING_SNAKE_CASE	=		"""huggingface/label-files"""
      if "large" in model_name or "huge" in model_name:
            SCREAMING_SNAKE_CASE	=		"""imagenet-22k-id2label.json"""
      else:
            SCREAMING_SNAKE_CASE	=		"""imagenet-1k-id2label.json"""

      SCREAMING_SNAKE_CASE	=		json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""			)				,		"""r"""			)			)
      SCREAMING_SNAKE_CASE	=		{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE	=		{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE	=		FocalNetConfig(
          embed_dim=_SCREAMING_SNAKE_CASE				,		depths=_SCREAMING_SNAKE_CASE				,		focal_levels=_SCREAMING_SNAKE_CASE				,		focal_windows=_SCREAMING_SNAKE_CASE				,		use_conv_embed=_SCREAMING_SNAKE_CASE				,		idalabel=_SCREAMING_SNAKE_CASE				,		labelaid=_SCREAMING_SNAKE_CASE				,		use_post_layernorm=_SCREAMING_SNAKE_CASE				,		use_layerscale=_SCREAMING_SNAKE_CASE				,		)

      return config



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Optional[int]:


      '''simple docstring'''


      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""patch_embed.proj"""				,		"""embeddings.patch_embeddings.projection"""			)
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""patch_embed.norm"""				,		"""embeddings.norm"""			)
      if "layers" in name:
            SCREAMING_SNAKE_CASE	=		"""encoder.""" + name
      if "encoder.layers" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""encoder.layers"""				,		"""encoder.stages"""			)
      if "downsample.proj" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""downsample.proj"""				,		"""downsample.projection"""			)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""blocks"""				,		"""layers"""			)
      if "modulation.f.weight" in name or "modulation.f.bias" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""modulation.f"""				,		"""modulation.projection_in"""			)
      if "modulation.h.weight" in name or "modulation.h.bias" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""modulation.h"""				,		"""modulation.projection_context"""			)
      if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""modulation.proj"""				,		"""modulation.projection_out"""			)

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE	=		"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE	=		"""layernorm.bias"""

      if "head" in name:
            SCREAMING_SNAKE_CASE	=		name.replace("""head"""				,		"""classifier"""			)
      else:
            SCREAMING_SNAKE_CASE	=		"""focalnet.""" + name

      return name



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=False			)				->     Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		{
          """focalnet-tiny""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth""",
          """focalnet-tiny-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth""",
          """focalnet-small""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth""",
          """focalnet-small-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth""",
          """focalnet-base""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth""",
          """focalnet-base-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth""",
          """focalnet-large-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth""",
          """focalnet-large-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth""",
          """focalnet-xlarge-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth""",
          """focalnet-xlarge-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth""",
      }
      # fmt: on

      SCREAMING_SNAKE_CASE	=		model_name_to_url[model_name]
      print("""Checkpoint URL: """				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE				,		map_location="""cpu"""			)["""model"""]

      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE	=		state_dict.pop(_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		val

      SCREAMING_SNAKE_CASE	=		get_focalnet_config(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		FocalNetForImageClassification(_SCREAMING_SNAKE_CASE			)
      model.eval()

      # load state dict
      model.load_state_dict(_SCREAMING_SNAKE_CASE			)

      # verify conversion
      SCREAMING_SNAKE_CASE	=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""

      SCREAMING_SNAKE_CASE	=		BitImageProcessor(
          do_resize=_SCREAMING_SNAKE_CASE				,		size={"""shortest_edge""": 2_56}				,		resample=PILImageResampling.BILINEAR				,		do_center_crop=_SCREAMING_SNAKE_CASE				,		crop_size=2_24				,		do_normalize=_SCREAMING_SNAKE_CASE				,		image_mean=_SCREAMING_SNAKE_CASE				,		image_std=_SCREAMING_SNAKE_CASE				,		)
      SCREAMING_SNAKE_CASE	=		Image.open(requests.get(_SCREAMING_SNAKE_CASE				,		stream=_SCREAMING_SNAKE_CASE			).raw			)
      SCREAMING_SNAKE_CASE	=		processor(images=_SCREAMING_SNAKE_CASE				,		return_tensors="""pt"""			)

      SCREAMING_SNAKE_CASE	=		transforms.Compose(
          [
              transforms.Resize(2_56			),
              transforms.CenterCrop(2_24			),
              transforms.ToTensor(),
              transforms.Normalize(mean=[0.485, 0.456, 0.406]				,		std=[0.229, 0.224, 0.225]			),
          ]			)

      SCREAMING_SNAKE_CASE	=		image_transforms(_SCREAMING_SNAKE_CASE			).unsqueeze(0			)

      # verify pixel_values
      assert torch.allclose(inputs.pixel_values				,		_SCREAMING_SNAKE_CASE				,		atol=1E-4			)

      SCREAMING_SNAKE_CASE	=		model(**_SCREAMING_SNAKE_CASE			)

      SCREAMING_SNAKE_CASE	=		outputs.logits.argmax(-1			).item()
      print("""Predicted class:"""				,		model.config.idalabel[predicted_class_idx]			)

      print("""First values of logits:"""				,		outputs.logits[0, :3]			)

      if model_name == "focalnet-tiny":
            SCREAMING_SNAKE_CASE	=		torch.tensor([0.2_166, -0.4_368, 0.2_191]			)
      elif model_name == "focalnet-tiny-lrf":
            SCREAMING_SNAKE_CASE	=		torch.tensor([1.1_669, 0.0_125, -0.1_695]			)
      elif model_name == "focalnet-small":
            SCREAMING_SNAKE_CASE	=		torch.tensor([0.4_917, -0.0_430, 0.1_341]			)
      elif model_name == "focalnet-small-lrf":
            SCREAMING_SNAKE_CASE	=		torch.tensor([-0.2_588, -0.5_342, -0.2_331]			)
      elif model_name == "focalnet-base":
            SCREAMING_SNAKE_CASE	=		torch.tensor([-0.1_655, -0.4_090, -0.1_730]			)
      elif model_name == "focalnet-base-lrf":
            SCREAMING_SNAKE_CASE	=		torch.tensor([0.5_306, -0.0_483, -0.3_928]			)
      assert torch.allclose(outputs.logits[0, :3]				,		_SCREAMING_SNAKE_CASE				,		atol=1E-4			)
      print("""Looks ok!"""			)

      if pytorch_dump_folder_path is not None:
            print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""			)
            model.save_pretrained(_SCREAMING_SNAKE_CASE			)
            processor.save_pretrained(_SCREAMING_SNAKE_CASE			)

      if push_to_hub:
            print(F"""Pushing model and processor of {model_name} to the hub..."""			)
            model.push_to_hub(F"""{model_name}"""			)
            processor.push_to_hub(F"""{model_name}"""			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""focalnet-tiny""",
          type=str,
          help="""Name of the FocalNet model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""",
          action="""store_true""",
          help="""Whether to push the model and processor to the hub.""",
      )

      SCREAMING_SNAKE_CASE_		     = parser.parse_args()
      convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


