





'''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()
lowercase_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			original_name.split("""."""    )[0]
    _SCREAMING_SNAKE_CASE									=			key.split("""."""    )
    _SCREAMING_SNAKE_CASE									=			int(key_list[key_list.index(__lowerCamelCase    ) - 2]    )
    _SCREAMING_SNAKE_CASE									=			int(key_list[key_list.index(__lowerCamelCase    ) - 1]    )
    _SCREAMING_SNAKE_CASE									=			orig_block_num - offset

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





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





def lowerCamelCase						(     )						->int:
    _SCREAMING_SNAKE_CASE									=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(__lowerCamelCase					,       stream=__lowerCamelCase    ).raw    )

    return image





@torch.no_grad()
def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    int    )						->Optional[Any]:
    _SCREAMING_SNAKE_CASE									=			PoolFormerConfig()

    # set attributes based on model_name
    _SCREAMING_SNAKE_CASE									=			"""huggingface/label-files"""
    _SCREAMING_SNAKE_CASE									=			model_name[-3:]
    _SCREAMING_SNAKE_CASE									=			1000
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			(1, 1000)

    # set config attributes
    _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )					,       """r"""    )    )
    _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}
    _SCREAMING_SNAKE_CASE									=			idalabel
    _SCREAMING_SNAKE_CASE									=			{v: k for k, v in idalabel.items()}
    if size == "s12":
        _SCREAMING_SNAKE_CASE									=			[2, 2, 6, 2]
        _SCREAMING_SNAKE_CASE									=			[64, 128, 320, 512]
        _SCREAMING_SNAKE_CASE									=			4.0
        _SCREAMING_SNAKE_CASE									=			0.9
    elif size == "s24":
        _SCREAMING_SNAKE_CASE									=			[4, 4, 12, 4]
        _SCREAMING_SNAKE_CASE									=			[64, 128, 320, 512]
        _SCREAMING_SNAKE_CASE									=			4.0
        _SCREAMING_SNAKE_CASE									=			0.9
    elif size == "s36":
        _SCREAMING_SNAKE_CASE									=			[6, 6, 18, 6]
        _SCREAMING_SNAKE_CASE									=			[64, 128, 320, 512]
        _SCREAMING_SNAKE_CASE									=			4.0
        _SCREAMING_SNAKE_CASE									=			1e-6
        _SCREAMING_SNAKE_CASE									=			0.9
    elif size == "m36":
        _SCREAMING_SNAKE_CASE									=			[6, 6, 18, 6]
        _SCREAMING_SNAKE_CASE									=			[96, 192, 384, 768]
        _SCREAMING_SNAKE_CASE									=			4.0
        _SCREAMING_SNAKE_CASE									=			1e-6
        _SCREAMING_SNAKE_CASE									=			0.95
    elif size == "m48":
        _SCREAMING_SNAKE_CASE									=			[8, 8, 24, 8]
        _SCREAMING_SNAKE_CASE									=			[96, 192, 384, 768]
        _SCREAMING_SNAKE_CASE									=			4.0
        _SCREAMING_SNAKE_CASE									=			1e-6
        _SCREAMING_SNAKE_CASE									=			0.95
    else:
        raise ValueError(F'Size {size} not supported'    )

    # load image processor
    _SCREAMING_SNAKE_CASE									=			PoolFormerImageProcessor(crop_pct=__lowerCamelCase    )

    # Prepare image
    _SCREAMING_SNAKE_CASE									=			prepare_img()
    _SCREAMING_SNAKE_CASE									=			image_processor(images=__lowerCamelCase					,       return_tensors="""pt"""    ).pixel_values

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

    # load original state dict
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       map_location=torch.device("""cpu"""    )    )

    # rename keys
    _SCREAMING_SNAKE_CASE									=			rename_keys(__lowerCamelCase    )

    # create HuggingFace model and load state dict
    _SCREAMING_SNAKE_CASE									=			PoolFormerForImageClassification(__lowerCamelCase    )
    model.load_state_dict(__lowerCamelCase    )
    model.eval()

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

    # forward pass
    _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([-0.3045, -0.6758, -0.4869]    )
    elif size == "s24":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([0.4402, -0.1374, -0.8045]    )
    elif size == "s36":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([-0.6080, -0.5133, -0.5898]    )
    elif size == "m36":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([0.3952, 0.2263, -1.2668]    )
    elif size == "m48":
        _SCREAMING_SNAKE_CASE									=			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]					,       __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__":
      lowercase_					= 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."""
      )
      lowercase_					= parser.parse_args()
      convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertConfig,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertWithLMHeadModel,
  )

class __snake_case		:
    def __init__(  self		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      parent
           lowercase							:     Tuple       	=      13
           lowercase							:     str       	=      7
           lowercase							:     Dict       	=      True
           lowercase							:     Dict       	=      True
           lowercase							:     str       	=      True
           lowercase							:     List[str]       	=      True
           lowercase							:     int       	=      True
           lowercase							:     Union[str, Any]       	=      False
           lowercase							:     Dict       	=      False
           lowercase							:     List[Any]       	=      False
           lowercase							:     List[Any]       	=      2
           lowercase							:     Optional[Any]       	=      99
           lowercase							:     int       	=      0
           lowercase							:     Tuple       	=      32
           lowercase							:     int       	=      2
           lowercase							:     Tuple       	=      4
           lowercase							:     List[Any]       	=      0.1
           lowercase							:     Tuple       	=      0.1
           lowercase							:     List[Any]       	=      512
           lowercase							:     int       	=      16
           lowercase							:     Dict       	=      2
           lowercase							:     int       	=      0.02
           lowercase							:     Union[str, Any]       	=      3
           lowercase							:     Any       	=      4
           lowercase							:     List[Any]       	=      """last"""
           lowercase							:     Tuple       	=      True
           lowercase							:     List[Any]       	=      None
           lowercase							:     Any       	=      0
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size      )
           lowercase							:     List[str]       	=      random_attention_mask([self.batch_size, self.seq_length]		,dtype=tf.floataa      )

           lowercase							:     Tuple       	=      None
           if self.use_input_lengths:
                  lowercase							:     List[str]       	=      (
                      ids_tensor([self.batch_size]		,vocab_size=2      ) + self.seq_length - 2
                  )  # small variation of seq_length

           lowercase							:     Tuple       	=      None
           if self.use_token_type_ids:
                  lowercase							:     Optional[int]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.n_langs      )

           lowercase							:     List[str]       	=      None
           lowercase							:     List[str]       	=      None
           lowercase							:     Optional[Any]       	=      None
           if self.use_labels:
                  lowercase							:     List[str]       	=      ids_tensor([self.batch_size]		,self.type_sequence_label_size      )
                  lowercase							:     List[str]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.num_labels      )
                  lowercase							:     str       	=      ids_tensor([self.batch_size]		,2		,dtype=tf.floataa      )
                  lowercase							:     Optional[Any]       	=      ids_tensor([self.batch_size]		,self.num_choices      )

           lowercase							:     str       	=      FlaubertConfig(
               vocab_size=self.vocab_size		,n_special=self.n_special		,emb_dim=self.hidden_size		,n_layers=self.num_hidden_layers		,n_heads=self.num_attention_heads		,dropout=self.hidden_dropout_prob		,attention_dropout=self.attention_probs_dropout_prob		,gelu_activation=self.gelu_activation		,sinusoidal_embeddings=self.sinusoidal_embeddings		,asm=self.asm		,causal=self.causal		,n_langs=self.n_langs		,max_position_embeddings=self.max_position_embeddings		,initializer_range=self.initializer_range		,summary_type=self.summary_type		,use_proj=self.use_proj		,bos_token_id=self.bos_token_id		,)

           return (
               config,
               input_ids,
               token_type_ids,
               input_lengths,
               sequence_labels,
               token_labels,
               is_impossible_labels,
               choice_labels,
               input_mask,
           )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      TFFlaubertModel(config=snake_case      )
           lowercase							:     str       	=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowercase							:     Optional[Any]       	=      model(snake_case      )

           lowercase							:     List[Any]       	=      [input_ids, input_mask]
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      TFFlaubertWithLMHeadModel(snake_case      )

           lowercase							:     Optional[Any]       	=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowercase							:     int       	=      model(snake_case      )

           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      TFFlaubertForQuestionAnsweringSimple(snake_case      )

           lowercase							:     Union[str, Any]       	=      {"""input_ids""": input_ids, """lengths""": input_lengths}

           lowercase							:     Tuple       	=      model(snake_case      )

           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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      TFFlaubertForSequenceClassification(snake_case      )

           lowercase							:     str       	=      {"""input_ids""": input_ids, """lengths""": input_lengths}

           lowercase							:     str       	=      model(snake_case      )

           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.type_sequence_label_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      self.num_labels
           lowercase							:     List[str]       	=      TFFlaubertForTokenClassification(config=snake_case      )
           lowercase							:     Dict       	=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      self.num_choices
           lowercase							:     Dict       	=      TFFlaubertForMultipleChoice(config=snake_case      )
           lowercase							:     Any       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Optional[Any]       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Dict       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Union[str, Any]       	=      {
               """input_ids""": multiple_choice_inputs_ids,
               """attention_mask""": multiple_choice_input_mask,
               """token_type_ids""": multiple_choice_token_type_ids,
           }
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_choices)      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.prepare_config_and_inputs()
           (
               (
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			
           )							:     int       	=      config_and_inputs
           lowercase							:     List[str]       	=      {
               """input_ids""": input_ids,
               """token_type_ids""": token_type_ids,
               """langs""": token_type_ids,
               """lengths""": input_lengths,
           }
           return config, inputs_dict






@require_tf
class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Dict=				(
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _a		:						Optional[Any]=				(
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    _a		:						Any=				(
        {
            "feature-extraction": TFFlaubertModel,
            "fill-mask": TFFlaubertWithLMHeadModel,
            "question-answering": TFFlaubertForQuestionAnsweringSimple,
            "text-classification": TFFlaubertForSequenceClassification,
            "token-classification": TFFlaubertForTokenClassification,
            "zero-shot": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    _a		:						Tuple=				False
    _a		:						int=				False
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if (
               pipeline_test_casse_name == "QAPipelineTests"
               and tokenizer_name is not None
               and not tokenizer_name.endswith("""Fast"""      )
           ):
                  # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                  # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                  # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                  return True

           return False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      TFFlaubertModelTester(self      )
           lowercase							:     List[Any]       	=      ConfigTester(self		,config_class=snake_case		,emb_dim=37      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_token_classification(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_multiple_choice(*snake_case      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:     Dict       	=      TFFlaubertModel.from_pretrained(snake_case      )
                  self.assertIsNotNone(snake_case      )






@require_tf
@require_sentencepiece
@require_tokenizers
class __snake_case		(      unittest.TestCase     ):
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""      )

           lowercase							:     int       	=      tf.convert_to_tensor(
               [[0, 158, 735, 2592, 1424, 6727, 82, 1]]		,dtype=tf.intaa		,)  # "J'aime flaubert !"

           lowercase							:     Dict       	=      model(snake_case      )[0]
           lowercase							:     Union[str, Any]       	=      tf.TensorShape((1, 8, 512)      )
           self.assertEqual(output.shape		,snake_case      )
           # compare the actual values for a slice.
           lowercase							:     Tuple       	=      tf.convert_to_tensor(
               [
                   [
                       [-1.8_768_773, -1.566_555, 0.27_072_418],
                       [-1.6_920_038, -0.5_873_505, 1.9_329_599],
                       [-2.9_563_985, -1.6_993_835, 1.7_972_052],
                   ]
               ]		,dtype=tf.floataa		,)

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,expected_slice.numpy()		,atol=1e-4      )      )
