

"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			__A						       =      "platform"

			import jax
			import jax.numpy as jnp

			from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
			    FlaxBlenderbotSmallForConditionalGeneration,
			    FlaxBlenderbotSmallModel,
			    shift_tokens_right,
			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			)     ->     str:





		if attention_mask is None:
				SCREAMING_SNAKE_CASE:	int   				=      np.where(input_ids != config.pad_token_id ,			1 ,			0			)
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      np.where(decoder_input_ids != config.pad_token_id ,			1 ,			0			)
		if head_mask is None:
				SCREAMING_SNAKE_CASE:	int   				=      np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE:	Dict   				=      np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE:	str   				=      np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}





class    snake_case     :

				def __init__(     self							:    Union[str, Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[int]=1_3					,  UpperCamelCase__							:    Any=7					,  UpperCamelCase__							:    List[Any]=True					,  UpperCamelCase__							:    Dict=False					,  UpperCamelCase__							:    List[Any]=9_9					,  UpperCamelCase__							:    Union[str, Any]=1_6					,  UpperCamelCase__							:    Dict=2					,  UpperCamelCase__							:    Tuple=4					,  UpperCamelCase__							:    Union[str, Any]=4					,  UpperCamelCase__							:    List[str]="gelu"					,  UpperCamelCase__							:    Optional[int]=0.1					,  UpperCamelCase__							:    Dict=0.1					,  UpperCamelCase__							:    str=3_2					,  UpperCamelCase__							:    Any=2					,  UpperCamelCase__							:    Optional[int]=1					,  UpperCamelCase__							:    List[Any]=0					,  UpperCamelCase__							:    List[Any]=0.02					,  )->       Dict:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	str   				=      parent
						SCREAMING_SNAKE_CASE:	str   				=      batch_size
						SCREAMING_SNAKE_CASE:	Dict   				=      seq_length
						SCREAMING_SNAKE_CASE:	List[Any]   				=      is_training
						SCREAMING_SNAKE_CASE:	Tuple   				=      use_labels
						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      vocab_size
						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      hidden_size
						SCREAMING_SNAKE_CASE:	str   				=      num_hidden_layers
						SCREAMING_SNAKE_CASE:	List[str]   				=      num_attention_heads
						SCREAMING_SNAKE_CASE:	List[Any]   				=      intermediate_size
						SCREAMING_SNAKE_CASE:	Tuple   				=      hidden_act
						SCREAMING_SNAKE_CASE:	Tuple   				=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE:	Any   				=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      max_position_embeddings
						SCREAMING_SNAKE_CASE:	Any   				=      eos_token_id
						SCREAMING_SNAKE_CASE:	List[Any]   				=      pad_token_id
						SCREAMING_SNAKE_CASE:	str   				=      bos_token_id
						SCREAMING_SNAKE_CASE:	int   				=      initializer_range

				def 							lowercase_					(     self							:    str)->       Optional[Any]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Tuple   				=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,  self.vocab_size)					,  3					,  self.vocab_size)
						SCREAMING_SNAKE_CASE:	int   				=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,  dtype=np.intaa))					,  -1)

						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      shift_tokens_right(__lowerCAmelCase					,  1					,  2)

						SCREAMING_SNAKE_CASE:	Dict   				=      BlenderbotSmallConfig(
						    vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  encoder_layers=self.num_hidden_layers					,  decoder_layers=self.num_hidden_layers					,  encoder_attention_heads=self.num_attention_heads					,  decoder_attention_heads=self.num_attention_heads					,  encoder_ffn_dim=self.intermediate_size					,  decoder_ffn_dim=self.intermediate_size					,  dropout=self.hidden_dropout_prob					,  attention_dropout=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.bos_token_id					,  pad_token_id=self.pad_token_id					,  initializer_range=self.initializer_range					,  use_cache=__lowerCAmelCase					,  )
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      prepare_blenderbot_inputs_dict(__lowerCAmelCase					,  __lowerCAmelCase					,  __lowerCAmelCase)
						return config, inputs_dict

				def 							lowercase_					(     self							:    str)->       str:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	str   				=      self.prepare_config_and_inputs()
						return config, inputs_dict

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    int)->       List[Any]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	List[Any]   				=      2_0
						SCREAMING_SNAKE_CASE:	Optional[int]   				=      model_class_name(__lowerCAmelCase)

						SCREAMING_SNAKE_CASE:	Any   				=      model.encode(inputs_dict["input_ids"])

						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	Optional[int]   				=      (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						SCREAMING_SNAKE_CASE:	Optional[int]   				=      model.init_cache(decoder_input_ids.shape[0]					,  __lowerCAmelCase					,  __lowerCAmelCase)
						SCREAMING_SNAKE_CASE:	Optional[int]   				=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,  dtype="i4")

						SCREAMING_SNAKE_CASE:	List[Any]   				=      jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      model.decode(
						    decoder_input_ids[:, :-1]					,  __lowerCAmelCase					,  decoder_attention_mask=__lowerCAmelCase					,  past_key_values=__lowerCAmelCase					,  decoder_position_ids=__lowerCAmelCase					,  )

						SCREAMING_SNAKE_CASE:	Any   				=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype="i4")
						SCREAMING_SNAKE_CASE:	Any   				=      model.decode(
						    decoder_input_ids[:, -1:]					,  __lowerCAmelCase					,  decoder_attention_mask=__lowerCAmelCase					,  past_key_values=outputs_cache.past_key_values					,  decoder_position_ids=__lowerCAmelCase					,  )

						SCREAMING_SNAKE_CASE:	List[Any]   				=      model.decode(__lowerCAmelCase					,  __lowerCAmelCase)

						SCREAMING_SNAKE_CASE:	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 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Tuple)->       Tuple:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Optional[int]   				=      2_0
						SCREAMING_SNAKE_CASE:	Any   				=      model_class_name(__lowerCAmelCase)

						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      model.encode(inputs_dict["input_ids"])

						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
						    ]					,  axis=-1					,  )

						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      model.init_cache(decoder_input_ids.shape[0]					,  __lowerCAmelCase					,  __lowerCAmelCase)
						SCREAMING_SNAKE_CASE:	Tuple   				=      jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )

						SCREAMING_SNAKE_CASE:	str   				=      model.decode(
						    decoder_input_ids[:, :-1]					,  __lowerCAmelCase					,  decoder_attention_mask=__lowerCAmelCase					,  past_key_values=__lowerCAmelCase					,  decoder_position_ids=__lowerCAmelCase					,  )
						SCREAMING_SNAKE_CASE:	int   				=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype="i4")
						SCREAMING_SNAKE_CASE:	Tuple   				=      model.decode(
						    decoder_input_ids[:, -1:]					,  __lowerCAmelCase					,  past_key_values=outputs_cache.past_key_values					,  decoder_attention_mask=__lowerCAmelCase					,  decoder_position_ids=__lowerCAmelCase					,  )

						SCREAMING_SNAKE_CASE:	int   				=      model.decode(__lowerCAmelCase					,  __lowerCAmelCase					,  decoder_attention_mask=__lowerCAmelCase)

						SCREAMING_SNAKE_CASE:	Union[str, 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_flax
class    snake_case     (    unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				int       				=  99

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



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	int   				=      np.array(
						    [
						        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
						        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
						        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
						        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
						        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
						        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
						        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
						        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
						        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
						        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
						        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
						        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
						        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
						    ]					,  dtype=np.intaa					,  )

						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      input_ids.shape[0]
						SCREAMING_SNAKE_CASE:	List[str]   				=      BlenderbotSmallConfig(
						    vocab_size=self.vocab_size					,  d_model=2_4					,  encoder_layers=2					,  decoder_layers=2					,  encoder_attention_heads=2					,  decoder_attention_heads=2					,  encoder_ffn_dim=3_2					,  decoder_ffn_dim=3_2					,  max_position_embeddings=4_8					,  eos_token_id=2					,  pad_token_id=1					,  bos_token_id=0					,  )
						return config, input_ids, batch_size

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



						'''simple docstring'''


						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      self._get_config_and_data()
						SCREAMING_SNAKE_CASE:	int   				=      FlaxBlenderbotSmallForConditionalGeneration(__lowerCAmelCase)
						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      lm_model(input_ids=__lowerCAmelCase)
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["logits"].shape					,  __lowerCAmelCase)

				def 							lowercase_					(     self							:    str)->       Optional[Any]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Any   				=      BlenderbotSmallConfig(
						    vocab_size=self.vocab_size					,  d_model=1_4					,  encoder_layers=2					,  decoder_layers=2					,  encoder_attention_heads=2					,  decoder_attention_heads=2					,  encoder_ffn_dim=8					,  decoder_ffn_dim=8					,  max_position_embeddings=4_8					,  )
						SCREAMING_SNAKE_CASE:	Any   				=      FlaxBlenderbotSmallForConditionalGeneration(__lowerCAmelCase)
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]					,  dtype=np.intaa)
						SCREAMING_SNAKE_CASE:	str   				=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,  dtype=np.intaa)
						SCREAMING_SNAKE_CASE:	List[Any]   				=      lm_model(input_ids=__lowerCAmelCase					,  decoder_input_ids=__lowerCAmelCase)
						SCREAMING_SNAKE_CASE:	str   				=      (*summary.shape, config.vocab_size)
						self.assertEqual(outputs["logits"].shape					,  __lowerCAmelCase)




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



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	str   				=      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,  dtype=np.intaa)
						SCREAMING_SNAKE_CASE:	List[Any]   				=      shift_tokens_right(__lowerCAmelCase					,  1					,  2)
						SCREAMING_SNAKE_CASE:	Dict   				=      np.equal(__lowerCAmelCase					,  1).astype(np.floataa).sum()
						SCREAMING_SNAKE_CASE:	List[str]   				=      np.equal(__lowerCAmelCase					,  1).astype(np.floataa).sum()
						self.assertEqual(shifted.shape					,  input_ids.shape)
						self.assertEqual(__lowerCAmelCase					,  n_pad_before - 1)
						self.assertTrue(np.equal(shifted[:, 0]					,  2).all())





@require_flax
class    snake_case     (    _a,							unittest.TestCase,							_a  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  True
				SCREAMING_SNAKE_CASE_       :				Dict       				=  (
				    (
				        FlaxBlenderbotSmallModel,
				        FlaxBlenderbotSmallForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

				def 							lowercase_					(     self							:    Tuple)->       int:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Any   				=      FlaxBlenderbotSmallModelTester(self)

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	Any   				=      self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(__lowerCAmelCase					,  __lowerCAmelCase					,  __lowerCAmelCase)

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



						'''simple docstring'''


						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	Any   				=      self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCAmelCase					,  __lowerCAmelCase					,  __lowerCAmelCase)

				def 							lowercase_					(     self							:    List[str])->       List[Any]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	List[Any]   				=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__):
										SCREAMING_SNAKE_CASE:	str   				=      self._prepare_for_class(__lowerCAmelCase					,  __lowerCAmelCase)
										SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      model_class(__lowerCAmelCase)

										@jax.jit
										def encode_jitted(UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Any=None					,  **UpperCamelCase__							:    List[str]):
												return model.encode(input_ids=__lowerCAmelCase					,  attention_mask=__lowerCAmelCase)

										with self.subTest("JIT Enabled"):
												SCREAMING_SNAKE_CASE:	Optional[Any]   				=      encode_jitted(**__lowerCAmelCase).to_tuple()

										with self.subTest("JIT Disabled"):
												with jax.disable_jit():
														SCREAMING_SNAKE_CASE:	int   				=      encode_jitted(**__lowerCAmelCase).to_tuple()

										self.assertEqual(len(__lowerCAmelCase)					,  len(__lowerCAmelCase))
										for jitted_output, output in zip(__lowerCAmelCase					,  __lowerCAmelCase):
												self.assertEqual(jitted_output.shape					,  output.shape)

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



						'''simple docstring'''


						SCREAMING_SNAKE_CASE				,			SCREAMING_SNAKE_CASE:	Optional[int]   				=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__):
										SCREAMING_SNAKE_CASE:	Optional[int]   				=      model_class(__lowerCAmelCase)
										SCREAMING_SNAKE_CASE:	Any   				=      model.encode(inputs_dict["input_ids"]					,  inputs_dict["attention_mask"])

										SCREAMING_SNAKE_CASE:	int   				=      {
										    "decoder_input_ids": inputs_dict["decoder_input_ids"],
										    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
										    "encoder_outputs": encoder_outputs,
										}

										@jax.jit
										def decode_jitted(UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Any):
												return model.decode(
												    decoder_input_ids=__lowerCAmelCase					,  decoder_attention_mask=__lowerCAmelCase					,  encoder_outputs=__lowerCAmelCase					,  )

										with self.subTest("JIT Enabled"):
												SCREAMING_SNAKE_CASE:	List[str]   				=      decode_jitted(**__lowerCAmelCase).to_tuple()

										with self.subTest("JIT Disabled"):
												with jax.disable_jit():
														SCREAMING_SNAKE_CASE:	str   				=      decode_jitted(**__lowerCAmelCase).to_tuple()

										self.assertEqual(len(__lowerCAmelCase)					,  len(__lowerCAmelCase))
										for jitted_output, output in zip(__lowerCAmelCase					,  __lowerCAmelCase):
												self.assertEqual(jitted_output.shape					,  output.shape)




				@slow
				def 							lowercase_					(     self							:    Tuple)->       Tuple:



						'''simple docstring'''


						for model_class_name in self.all_model_classes:
								SCREAMING_SNAKE_CASE:	Optional[Any]   				=      model_class_name.from_pretrained("facebook/blenderbot_small-90M")
								# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
								SCREAMING_SNAKE_CASE:	Optional[int]   				=      np.ones((1, 1)) * model.config.eos_token_id
								SCREAMING_SNAKE_CASE:	Optional[Any]   				=      model(__lowerCAmelCase)
								self.assertIsNotNone(__lowerCAmelCase)





"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		__lowerCAmelCase:	Dict   				=      original_name.split("."			)[0]
		__lowerCAmelCase:	Any   				=      key.split("."			)
		__lowerCAmelCase:	Union[str, Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 2]			)
		__lowerCAmelCase:	List[Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 1]			)
		__lowerCAmelCase:	List[str]   				=      orig_block_num - offset

		__lowerCAmelCase:	Tuple   				=      key.replace(F"{orig_block_num}.{layer_num}.{original_name}" ,			F"block.{new_block_num}.{layer_num}.{new_name}"			)
		return key






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		__lowerCAmelCase:	List[Any]   				=      OrderedDict()
		__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      0, 0
		for key, value in state_dict.items():
				if key.startswith("network"			):
						__lowerCAmelCase:	Dict   				=      key.replace("network" ,			"poolformer.encoder"			)
				if "proj" in key:
						# Works for the first embedding as well as the internal embedding layers
						if key.endswith("bias"			) and "patch_embed" not in key:
								patch_emb_offset += 1
						__lowerCAmelCase:	int   				=      key[: key.find("proj"			)]
						__lowerCAmelCase:	Dict   				=      key.replace(__SCREAMING_SNAKE_CASE ,			F"patch_embeddings.{total_embed_found}."			)
						__lowerCAmelCase:	Optional[int]   				=      key.replace("proj" ,			"projection"			)
						if key.endswith("bias"			):
								total_embed_found += 1
				if "patch_embeddings" in key:
						__lowerCAmelCase:	int   				=      "poolformer.encoder." + key
				if "mlp.fc1" in key:
						__lowerCAmelCase:	Optional[Any]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc1" ,			"output.conv1"			)
				if "mlp.fc2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc2" ,			"output.conv2"			)
				if "norm1" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm1" ,			"before_norm"			)
				if "norm2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm2" ,			"after_norm"			)
				if "layer_scale_1" in key:
						__lowerCAmelCase:	Optional[int]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_1" ,			"layer_scale_1"			)
				if "layer_scale_2" in key:
						__lowerCAmelCase:	Any   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_2" ,			"layer_scale_2"			)
				if "head" in key:
						__lowerCAmelCase:	int   				=      key.replace("head" ,			"classifier"			)
				__lowerCAmelCase:	Tuple   				=      value
		return new_state_dict






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Union[str, Any]   				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase:	int   				=      Image.open(requests.get(__SCREAMING_SNAKE_CASE ,			stream=__SCREAMING_SNAKE_CASE			).raw			)

		return image






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		__lowerCAmelCase:	Any   				=      PoolFormerConfig()

		# set attributes based on model_name
		__lowerCAmelCase:	Any   				=      "huggingface/label-files"
		__lowerCAmelCase:	int   				=      model_name[-3:]
		__lowerCAmelCase:	List[Any]   				=      1_0_0_0
		__lowerCAmelCase:	Tuple   				=      "imagenet-1k-id2label.json"
		__lowerCAmelCase:	str   				=      (1, 1_0_0_0)

		# set config attributes
		__lowerCAmelCase:	Dict   				=      json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			repo_type="dataset"			) ,			"r"			)			)
		__lowerCAmelCase:	List[str]   				=      {int(__SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
		__lowerCAmelCase:	Any   				=      idalabel
		__lowerCAmelCase:	Any   				=      {v: k for k, v in idalabel.items()}
		if size == "s12":
				__lowerCAmelCase:	Dict   				=      [2, 2, 6, 2]
				__lowerCAmelCase:	str   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Optional[Any]   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      0.9
		elif size == "s24":
				__lowerCAmelCase:	Tuple   				=      [4, 4, 1_2, 4]
				__lowerCAmelCase:	List[str]   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Tuple   				=      4.0
				__lowerCAmelCase:	Optional[int]   				=      0.9
		elif size == "s36":
				__lowerCAmelCase:	int   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	int   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	List[str]   				=      4.0
				__lowerCAmelCase:	Dict   				=      1E-6
				__lowerCAmelCase:	List[Any]   				=      0.9
		elif size == "m36":
				__lowerCAmelCase:	Dict   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	Dict   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      1E-6
				__lowerCAmelCase:	Union[str, Any]   				=      0.95
		elif size == "m48":
				__lowerCAmelCase:	str   				=      [8, 8, 2_4, 8]
				__lowerCAmelCase:	Optional[int]   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	int   				=      1E-6
				__lowerCAmelCase:	str   				=      0.95
		else:
				raise ValueError(F"Size {size} not supported"			)

		# load image processor
		__lowerCAmelCase:	Union[str, Any]   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)

		# Prepare image
		__lowerCAmelCase:	int   				=      prepare_img()
		__lowerCAmelCase:	Tuple   				=      image_processor(images=__SCREAMING_SNAKE_CASE ,			return_tensors="pt"			).pixel_values

		logger.info(F"Converting model {model_name}..."			)

		# load original state dict
		__lowerCAmelCase:	Optional[int]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)

		# rename keys
		__lowerCAmelCase:	Any   				=      rename_keys(__SCREAMING_SNAKE_CASE			)

		# create HuggingFace model and load state dict
		__lowerCAmelCase:	str   				=      PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE			)
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)
		model.eval()

		# Define image processor
		__lowerCAmelCase:	Any   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      image_processor(images=prepare_img() ,			return_tensors="pt"			).pixel_values

		# forward pass
		__lowerCAmelCase:	int   				=      model(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.3045, -0.6758, -0.4869]			)
		elif size == "s24":
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([0.4402, -0.1374, -0.8045]			)
		elif size == "s36":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.6080, -0.5133, -0.5898]			)
		elif size == "m36":
				__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([0.3952, 0.2263, -1.2668]			)
		elif size == "m48":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([0.1167, -0.0656, -0.3423]			)
		else:
				raise ValueError(F"Size {size} not supported"			)

		# verify logits
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-2			)

		# finally, save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."			)
		Path(__SCREAMING_SNAKE_CASE			).mkdir(exist_ok=__SCREAMING_SNAKE_CASE			)
		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(
			    "--model_name",
			    default="poolformer_s12",
			    type=str,
			    help="Name of the model you'd like to convert.",
			)
			parser.add_argument(
			    "--checkpoint_path", default=None, type=str, help="Path 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_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


