"""simple docstring"""






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
           TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           LayoutLMvaConfig,
           TFLayoutLMvaForQuestionAnswering,
           TFLayoutLMvaForSequenceClassification,
           TFLayoutLMvaForTokenClassification,
           TFLayoutLMvaModel,
       )

if is_vision_available():
       from PIL import Image

       from transformers import LayoutLMvaImageProcessor





class 				_A		:

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=2	, __lowerCAmelCase=3	, __lowerCAmelCase=4	, __lowerCAmelCase=2	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=36	, __lowerCAmelCase=2	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=6	, __lowerCAmelCase=6	, __lowerCAmelCase=3	, __lowerCAmelCase=4	, __lowerCAmelCase=None	, __lowerCAmelCase=1000	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			num_channels
            lowercase														=			image_size
            lowercase														=			patch_size
            lowercase														=			is_training
            lowercase														=			use_input_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			coordinate_size
            lowercase														=			shape_size
            lowercase														=			num_labels
            lowercase														=			num_choices
            lowercase														=			scope
            lowercase														=			range_bbox

            # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
            lowercase														=			text_seq_length
            lowercase														=			(image_size // patch_size) ** 2 + 1
            lowercase														=			self.text_seq_length + self.image_seq_length

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.text_seq_length]	, self.vocab_size		)

            lowercase														=			ids_tensor([self.batch_size, self.text_seq_length, 4]	, self.range_bbox		)
            lowercase														=			bbox.numpy()
            # Ensure that bbox is legal
            for i in range(bbox.shape[0]		):
                  for j in range(bbox.shape[1]		):
                        if bbox[i, j, 3] < bbox[i, j, 1]:
                              lowercase														=			bbox[i, j, 3]
                              lowercase														=			bbox[i, j, 1]
                              lowercase														=			tmp_coordinate
                        if bbox[i, j, 2] < bbox[i, j, 0]:
                              lowercase														=			bbox[i, j, 2]
                              lowercase														=			bbox[i, j, 0]
                              lowercase														=			tmp_coordinate
            lowercase														=			tf.constant(__lowerCAmelCase		)

            lowercase														=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

            lowercase														=			None
            if self.use_input_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.text_seq_length]		)

            lowercase														=			None
            if self.use_token_type_ids:
                  lowercase														=			ids_tensor([self.batch_size, self.text_seq_length]	, self.type_vocab_size		)

            lowercase														=			None
            lowercase														=			None
            if self.use_labels:
                  lowercase														=			ids_tensor([self.batch_size]	, self.type_sequence_label_size		)
                  lowercase														=			ids_tensor([self.batch_size, self.text_seq_length]	, self.num_labels		)

            lowercase														=			LayoutLMvaConfig(
                vocab_size=self.vocab_size	, hidden_size=self.hidden_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	, coordinate_size=self.coordinate_size	, shape_size=self.shape_size	, input_size=self.image_size	, patch_size=self.patch_size	, )

            return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			TFLayoutLMvaModel(config=__lowerCAmelCase		)

            # text + image
            lowercase														=			model(__lowerCAmelCase	, pixel_values=__lowerCAmelCase	, training=__lowerCAmelCase		)
            lowercase														=			model(
                __lowerCAmelCase	, bbox=__lowerCAmelCase	, pixel_values=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, training=__lowerCAmelCase	, )
            lowercase														=			model(__lowerCAmelCase	, bbox=__lowerCAmelCase	, pixel_values=__lowerCAmelCase	, training=__lowerCAmelCase		)

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

            # text only
            lowercase														=			model(__lowerCAmelCase	, training=__lowerCAmelCase		)
            self.parent.assertEqual(
                result.last_hidden_state.shape	, (self.batch_size, self.text_seq_length, self.hidden_size)		)

            # image only
            lowercase														=			model({"""pixel_values""": pixel_values}	, training=__lowerCAmelCase		)
            self.parent.assertEqual(
                result.last_hidden_state.shape	, (self.batch_size, self.image_seq_length, self.hidden_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			TFLayoutLMvaForSequenceClassification(config=__lowerCAmelCase		)
            lowercase														=			model(
                __lowerCAmelCase	, bbox=__lowerCAmelCase	, pixel_values=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase	, training=__lowerCAmelCase	, )
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			TFLayoutLMvaForTokenClassification(config=__lowerCAmelCase		)
            lowercase														=			model(
                __lowerCAmelCase	, bbox=__lowerCAmelCase	, pixel_values=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase	, training=__lowerCAmelCase	, )
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.text_seq_length, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			2
            lowercase														=			TFLayoutLMvaForQuestionAnswering(config=__lowerCAmelCase		)
            lowercase														=			model(
                __lowerCAmelCase	, bbox=__lowerCAmelCase	, pixel_values=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, start_positions=__lowerCAmelCase	, end_positions=__lowerCAmelCase	, training=__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        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            ((lowercase)							,							(lowercase)							,							(lowercase)							,							(lowercase)							,							(lowercase)							,							(lowercase)							,							(lowercase)							,							(lowercase))														=			config_and_inputs
            lowercase														=			{
                """input_ids""": input_ids,
                """bbox""": bbox,
                """pixel_values""": pixel_values,
                """token_type_ids""": token_type_ids,
                """attention_mask""": input_mask,
            }
            return config, inputs_dict






@require_tf
class 				_A		( lowerCAmelCase						,					lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Tuple													=			(
          (
              TFLayoutLMvaModel,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      snake_case__    :     Tuple													=			(
          {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
          if is_tf_available()
          else {}
      )

      snake_case__    :     int													=			False
      snake_case__    :     Any													=			False
      snake_case__    :     Optional[int]													=			False

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            return True

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=False		):







            """simple docstring"""



            lowercase														=			copy.deepcopy(__lowerCAmelCase		)

            if model_class in get_values(__lowerCAmelCase		):
                  lowercase														=			{
                      k: tf.tile(tf.expand_dims(__lowerCAmelCase	, 1		)	, (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
                      if isinstance(__lowerCAmelCase	, tf.Tensor		) and v.ndim > 0
                      else v
                      for k, v in inputs_dict.items()
                  }

            if return_labels:
                  if model_class in get_values(__lowerCAmelCase		):
                        lowercase														=			tf.ones(self.model_tester.batch_size	, dtype=tf.intaa		)
                  elif model_class in get_values(__lowerCAmelCase		):
                        lowercase														=			tf.zeros(self.model_tester.batch_size	, dtype=tf.intaa		)
                        lowercase														=			tf.zeros(self.model_tester.batch_size	, dtype=tf.intaa		)
                  elif model_class in get_values(__lowerCAmelCase		):
                        lowercase														=			tf.zeros(self.model_tester.batch_size	, dtype=tf.intaa		)
                  elif model_class in get_values(__lowerCAmelCase		):
                        lowercase														=			tf.zeros(
                            (self.model_tester.batch_size, self.model_tester.text_seq_length)	, dtype=tf.intaa		)

            return inputs_dict

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			TFLayoutLMvaModelTester(self		)
            lowercase														=			ConfigTester(self	, config_class=__lowerCAmelCase	, hidden_size=37		)

      def        A__ (		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                  lowercase														=			model_class(__lowerCAmelCase		)
                  if getattr(__lowerCAmelCase	, """hf_compute_loss"""	, __lowerCAmelCase		):
                        # The number of elements in the loss should be the same as the number of elements in the label
                        lowercase														=			self._prepare_for_class(inputs_dict.copy()	, __lowerCAmelCase	, return_labels=__lowerCAmelCase		)
                        lowercase														=			prepared_for_class[
                            sorted(prepared_for_class.keys() - inputs_dict.keys()	, reverse=__lowerCAmelCase		)[0]
                        ]
                        lowercase														=			added_label.shape.as_list()[:1]

                        # Test that model correctly compute the loss with kwargs
                        lowercase														=			self._prepare_for_class(inputs_dict.copy()	, __lowerCAmelCase	, return_labels=__lowerCAmelCase		)
                        lowercase														=			prepared_for_class.pop("""input_ids"""		)

                        lowercase														=			model(__lowerCAmelCase	, **__lowerCAmelCase		)[0]
                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

                        # Test that model correctly compute the loss when we mask some positions
                        lowercase														=			self._prepare_for_class(inputs_dict.copy()	, __lowerCAmelCase	, return_labels=__lowerCAmelCase		)
                        lowercase														=			prepared_for_class.pop("""input_ids"""		)
                        if "labels" in prepared_for_class:
                              lowercase														=			prepared_for_class["""labels"""].numpy()
                              if len(labels.shape		) > 1 and labels.shape[1] != 1:
                                    lowercase														=			-100
                                    lowercase														=			tf.convert_to_tensor(__lowerCAmelCase		)
                                    lowercase														=			model(__lowerCAmelCase	, **__lowerCAmelCase		)[0]
                                    self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
                                    self.assertTrue(not np.any(np.isnan(loss.numpy()		)		)		)

                # Test that model correctly compute the loss with a dict
                        lowercase														=			self._prepare_for_class(inputs_dict.copy()	, __lowerCAmelCase	, return_labels=__lowerCAmelCase		)
                        lowercase														=			model(__lowerCAmelCase		)[0]
                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

                        # Test that model correctly compute the loss with a tuple
                        lowercase														=			self._prepare_for_class(inputs_dict.copy()	, __lowerCAmelCase	, return_labels=__lowerCAmelCase		)

                        # Get keys that were added with the _prepare_for_class function
                        lowercase														=			prepared_for_class.keys() - inputs_dict.keys()
                        lowercase														=			inspect.signature(model.call		).parameters
                        lowercase														=			list(signature.keys()		)

                        # Create a dictionary holding the location of the tensors in the tuple
                        lowercase														=			{0: """input_ids"""}
                        for label_key in label_keys:
                              lowercase														=			signature_names.index(__lowerCAmelCase		)
                              lowercase														=			label_key
                        lowercase														=			sorted(tuple_index_mapping.items()		)
                        # Initialize a list with their default values, update the values and convert to a tuple
                        lowercase														=			[]

                        for name in signature_names:
                              if name != "kwargs":
                                    list_input.append(signature[name].default		)

                        for index, value in sorted_tuple_index_mapping:
                              lowercase														=			prepared_for_class[value]

                        lowercase														=			tuple(__lowerCAmelCase		)

                        # Send to model
                        lowercase														=			model(tuple_input[:-1]		)[0]

                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

      def        A__ (		self		):







            """simple docstring"""



            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=			self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  lowercase														=			type
                  self.model_tester.create_and_check_model(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase														=			TFLayoutLMvaModel.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)






def     UpperCAmelCase__ (       )				->					List[str]:
      '''simple docstring'''


      lowercase														=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
      return image






@require_tf
class 				_A		( unittest.TestCase			):

      @cached_property
      def        A__ (		self		):







            """simple docstring"""



            return LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase		) if is_vision_available() else None







      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""		)

            lowercase														=			self.default_image_processor
            lowercase														=			prepare_img()
            lowercase														=			image_processor(images=__lowerCAmelCase	, return_tensors="""tf"""		).pixel_values

            lowercase														=			tf.constant([[1, 2]]		)
            lowercase														=			tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]		)	, axis=0		)

            # forward pass
            lowercase														=			model(input_ids=__lowerCAmelCase	, bbox=__lowerCAmelCase	, pixel_values=__lowerCAmelCase	, training=__lowerCAmelCase		)

            # verify the logits
            lowercase														=			(1, 199, 768)
            self.assertEqual(outputs.last_hidden_state.shape	, __lowerCAmelCase		)

            lowercase														=			tf.constant(
                [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]		)

            self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]	, __lowerCAmelCase	, atol=1E-4		)		)




"""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()
__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Optional[int]	)				->					Any:
      '''simple docstring'''


      lowercase														=			original_name.split("""."""	)[0]
      lowercase														=			key.split("""."""	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 2]	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 1]	)
      lowercase														=			orig_block_num - offset

      lowercase														=			key.replace(f'{orig_block_num}.{layer_num}.{original_name}'      ,	f'block.{new_block_num}.{layer_num}.{new_name}'	)
      return key


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			OrderedDict()
      lowercase							,							lowercase														=			0, 0
      for key, value in state_dict.items():
            if key.startswith("""network"""	):
                  lowercase														=			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
                  lowercase														=			key[: key.find("""proj"""	)]
                  lowercase														=			key.replace(lowerCAmelCase__      ,	f'patch_embeddings.{total_embed_found}.'	)
                  lowercase														=			key.replace("""proj"""      ,	"""projection"""	)
                  if key.endswith("""bias"""	):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  lowercase														=			"""poolformer.encoder.""" + key
            if "mlp.fc1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc1"""      ,	"""output.conv1"""	)
            if "mlp.fc2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc2"""      ,	"""output.conv2"""	)
            if "norm1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm1"""      ,	"""before_norm"""	)
            if "norm2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm2"""      ,	"""after_norm"""	)
            if "layer_scale_1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_1"""      ,	"""layer_scale_1"""	)
            if "layer_scale_2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_2"""      ,	"""layer_scale_2"""	)
            if "head" in key:
                  lowercase														=			key.replace("""head"""      ,	"""classifier"""	)
            lowercase														=			value
      return new_state_dict


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      lowercase														=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase														=			Image.open(requests.get(lowerCAmelCase__      ,	stream=lowerCAmelCase__	).raw	)

      return image


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			PoolFormerConfig()

      # set attributes based on model_name
      lowercase														=			"""huggingface/label-files"""
      lowercase														=			model_name[-3:]
      lowercase														=			1_0_0_0
      lowercase														=			"""imagenet-1k-id2label.json"""
      lowercase														=			(1, 1_0_0_0)

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

      # load image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)

      # Prepare image
      lowercase														=			prepare_img()
      lowercase														=			image_processor(images=lowerCAmelCase__      ,	return_tensors="""pt"""	).pixel_values

      logger.info(f'Converting model {model_name}...'	)

      # load original state dict
      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location=torch.device("""cpu"""	)	)

      # rename keys
      lowercase														=			rename_keys(lowerCAmelCase__	)

      # create HuggingFace model and load state dict
      lowercase														=			PoolFormerForImageClassification(lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)
      model.eval()

      # Define image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)
      lowercase														=			image_processor(images=prepare_img()      ,	return_tensors="""pt"""	).pixel_values

      # forward pass
      lowercase														=			model(lowerCAmelCase__	)
      lowercase														=			outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            lowercase														=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
      elif size == "s24":
            lowercase														=			torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
      elif size == "s36":
            lowercase														=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
      elif size == "m36":
            lowercase														=			torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
      elif size == "m48":
            lowercase														=			torch.tensor([0.1_167, -0.0_656, -0.3_423]	)
      else:
            raise ValueError(f'Size {size} not supported'	)

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

      # finally, save model and image processor
      logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'	)
      Path(lowerCAmelCase__	).mkdir(exist_ok=lowerCAmelCase__	)
      model.save_pretrained(lowerCAmelCase__	)
      print(f'Saving image processor to {pytorch_dump_folder_path}'	)
      image_processor.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: List[Any]        =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."""
       )
       __lowerCAmelCase					: int        =parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



