


import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

_A		       =						{
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

_A		       =						{
    'b0': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 224,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 240,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 1408,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 260,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 1536,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 300,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 1792,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 380,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2048,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 456,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 2304,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 528,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 2560,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 600,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=EfficientNetConfig()
     __UpperCamelCase    							=CONFIG_MAP[model_name]['hidden_dim']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['width_coef']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['depth_coef']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['image_size']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['dropout_rate']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['dw_padding']

     __UpperCamelCase    							='huggingface/label-files'
     __UpperCamelCase    							='imagenet-1k-id2label.json'
     __UpperCamelCase    							=10_00
     __UpperCamelCase    							=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     repo_type='dataset'				)			,     'r'				)				)
     __UpperCamelCase    							={int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}

     __UpperCamelCase    							=idalabel
     __UpperCamelCase    							={v: k for k, v in idalabel.items()}
     return config

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]				):
     __UpperCamelCase    							=CONFIG_MAP[model_name]['image_size']
     __UpperCamelCase    							=EfficientNetImageProcessor(
         size={'height': size, 'width': size}			,     image_mean=[0.485, 0.456, 0.406]			,     image_std=[0.47853944, 0.4732864, 0.47434163]			,     do_center_crop=SCREAMING_SNAKE_CASE__			,     )
     return preprocessor

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str				):
     __UpperCamelCase    							=[v.split('_'				)[0].split('block'				)[1] for v in original_param_names if v.startswith('block'				)]
     __UpperCamelCase    							=sorted(set(SCREAMING_SNAKE_CASE__				)				)
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							={b: str(SCREAMING_SNAKE_CASE__				) for b, i in zip(SCREAMING_SNAKE_CASE__			,     range(SCREAMING_SNAKE_CASE__				)				)}

     __UpperCamelCase    							=[]
     rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')				)
     rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')				)
     rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')				)
     rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')				)
     rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')				)

     for b in block_names:
          __UpperCamelCase    							=block_name_mapping[b]
          rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight')				)
          rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight')				)
          rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias')				)
          rename_keys.append(
              (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')				)
          rename_keys.append(
              (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')				)
          rename_keys.append(
              (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')				)
          rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')				)
          rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')				)
          rename_keys.append(
              (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')				)
          rename_keys.append(
              (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')				)

          rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')				)
          rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')				)
          rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')				)
          rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')				)
          rename_keys.append(
              (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight')				)
          rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight')				)
          rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias')				)
          rename_keys.append(
              (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean')				)
          rename_keys.append(
              (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var')				)

     rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')				)
     rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')				)
     rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')				)
     rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')				)
     rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')				)

     __UpperCamelCase    							={}
     for item in rename_keys:
          if item[0] in original_param_names:
               __UpperCamelCase    							='efficientnet.' + item[1]

     __UpperCamelCase    							='classifier.weight'
     __UpperCamelCase    							='classifier.bias'
     return key_mapping

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Tuple				):
     for key, value in tf_params.items():
          if "normalization" in key:
               continue

          __UpperCamelCase    							=key_mapping[key]
          if "_conv" in key and "kernel" in key:
               __UpperCamelCase    							=torch.from_numpy(SCREAMING_SNAKE_CASE__				).permute(3			,     2			,     0			,     1				)
          elif "depthwise_kernel" in key:
               __UpperCamelCase    							=torch.from_numpy(SCREAMING_SNAKE_CASE__				).permute(2			,     3			,     0			,     1				)
          elif "kernel" in key:
               __UpperCamelCase    							=torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__				)				)
          else:
               __UpperCamelCase    							=torch.from_numpy(SCREAMING_SNAKE_CASE__				)

          # Replace HF parameters with original TF model parameters
          assert hf_params[hf_key].shape == new_hf_value.shape
          hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE__				)

@torch.no_grad()
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]				):
     __UpperCamelCase    							=model_classes[model_name](
         include_top=SCREAMING_SNAKE_CASE__			,     weights='imagenet'			,     input_tensor=SCREAMING_SNAKE_CASE__			,     input_shape=SCREAMING_SNAKE_CASE__			,     pooling=SCREAMING_SNAKE_CASE__			,     classes=10_00			,     classifier_activation='softmax'			,     )

     __UpperCamelCase    							=original_model.trainable_variables
     __UpperCamelCase    							=original_model.non_trainable_variables
     __UpperCamelCase    							={param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
          __UpperCamelCase    							=param.numpy()
     __UpperCamelCase    							=list(tf_params.keys()				)

     # Load HuggingFace model
     __UpperCamelCase    							=get_efficientnet_config(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=EfficientNetForImageClassification(SCREAMING_SNAKE_CASE__				).eval()
     __UpperCamelCase    							=hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print('Converting parameters...'				)
     __UpperCamelCase    							=rename_keys(SCREAMING_SNAKE_CASE__				)
     replace_params(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     # Initialize preprocessor and preprocess input image
     __UpperCamelCase    							=convert_image_processor(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=preprocessor(images=prepare_img()			,     return_tensors='pt'				)

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          __UpperCamelCase    							=hf_model(**SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=outputs.logits.detach().numpy()

     # Original model inference
     __UpperCamelCase    							=False
     __UpperCamelCase    							=CONFIG_MAP[model_name]['image_size']
     __UpperCamelCase    							=prepare_img().resize((image_size, image_size)			,     resample=PIL.Image.NEAREST				)
     __UpperCamelCase    							=image.img_to_array(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=np.expand_dims(SCREAMING_SNAKE_CASE__			,     axis=0				)
     __UpperCamelCase    							=original_model.predict(SCREAMING_SNAKE_CASE__				)

     # Check whether original and HF model outputs match  -> np.allclose
     assert np.allclose(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     atol=1E-3				), "The predicted logits are not the same."
     print('Model outputs match!'				)

     if save_model:
          # Create folder to save model
          if not os.path.isdir(SCREAMING_SNAKE_CASE__				):
               os.mkdir(SCREAMING_SNAKE_CASE__				)
          # Save converted model and image processor
          hf_model.save_pretrained(SCREAMING_SNAKE_CASE__				)
          preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__				)

     if push_to_hub:
          # Push model and image processor to hub
          print(F'Pushing converted {model_name} to the hub...'				)
          __UpperCamelCase    							=F'efficientnet-{model_name}'
          preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__				)
          hf_model.push_to_hub(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
   _A		       =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='b0',
       type=str,
       help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default='hf_model',
       type=str,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument('--save_model', action='store_true', help='Save model to local')
   parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

   _A		       =						parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)









import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  GPTaTokenizer
    UpperCAmelCase__						:		Any      =  GPTaTokenizerFast
    UpperCAmelCase__						:		Tuple      =  True
    UpperCAmelCase__						:		int      =  {"add_prefix_space": True}
    UpperCAmelCase__						:		Any      =  False






    def _a					(			self    )  ->       Optional[int]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase    							=[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
             '<|endoftext|>',
         ]
         __UpperCamelCase    							=dict(zip(A_					,   range(len(A_    )    )    )    )
         __UpperCamelCase    							=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __UpperCamelCase    							={'unk_token': '<unk>'}

         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write(json.dumps(A_    ) + '\n'    )
         with open(self.merges_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write('\n'.join(A_    )    )






    def _a					(			self					,   **A_    )  ->       str:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   **A_    )  ->       Optional[Any]:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							='lower newer'
         return input_text, output_text






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=GPTaTokenizer(self.vocab_file					,   self.merges_file					,   **self.special_tokens_map    )
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							=['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         self.assertListEqual(A_					,   A_    )

         __UpperCamelCase    							=tokens + [tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self    )  ->       int:
         if not self.test_rust_tokenizer:
              return

         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )

         __UpperCamelCase    							='lower newer'

         # Testing tokenization
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.tokenize(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids without special tokens
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_					,   add_special_tokens=A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids with special tokens
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing the unknown token
         __UpperCamelCase    							=tokens + [rust_tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       Optional[int]:
         # It's very difficult to mix/test pretokenization with byte-level
         # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
         pass






    def _a					(			self					,   A_=15    )  ->       List[str]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
                   __UpperCamelCase    							=self.rust_tokenizer_class.from_pretrained(A_					,   **A_    )

                   # Simple input
                   __UpperCamelCase    							='This is a simple input'
                   __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']
                   __UpperCamelCase    							=('This is a simple input', 'This is a pair')
                   __UpperCamelCase    							=[
                       ('This is a simple input 1', 'This is a simple input 2'),
                       ('This is a simple pair 1', 'This is a simple pair 2'),
                   ]

                   # Simple input tests
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   pad_token='<pad>'    )

         # Simple input
         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input looooooooong', 'This is a simple input']
         __UpperCamelCase    							=('This is a simple input', 'This is a pair')
         __UpperCamelCase    							=[
             ('This is a simple input loooooong', 'This is a simple input'),
             ('This is a simple pair loooooong', 'This is a simple pair'),
         ]

         __UpperCamelCase    							=tokenizer.pad_token_id

         __UpperCamelCase    							=tokenizer(A_					,   padding='max_length'					,   max_length=30					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(*A_					,   padding='max_length'					,   max_length=60					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )

         # s
         # test single string max_length padding
         self.assertEqual(out_s['input_ids'].shape[-1]					,   30    )
         self.assertTrue(pad_token_id in out_s['input_ids']    )
         self.assertTrue(0 in out_s['attention_mask']    )

         # s2
         # test automatic padding
         self.assertEqual(out_sa['input_ids'].shape[-1]					,   33    )
         # long slice doesn't have padding
         self.assertFalse(pad_token_id in out_sa['input_ids'][0]    )
         self.assertFalse(0 in out_sa['attention_mask'][0]    )
         # short slice does have padding
         self.assertTrue(pad_token_id in out_sa['input_ids'][1]    )
         self.assertTrue(0 in out_sa['attention_mask'][1]    )

         # p
         # test single pair max_length padding
         self.assertEqual(out_p['input_ids'].shape[-1]					,   60    )
         self.assertTrue(pad_token_id in out_p['input_ids']    )
         self.assertTrue(0 in out_p['attention_mask']    )

         # p2
         # test automatic padding pair
         self.assertEqual(out_pa['input_ids'].shape[-1]					,   52    )
         # long slice pair doesn't have padding
         self.assertFalse(pad_token_id in out_pa['input_ids'][0]    )
         self.assertFalse(0 in out_pa['attention_mask'][0]    )
         # short slice pair does have padding
         self.assertTrue(pad_token_id in out_pa['input_ids'][1]    )
         self.assertTrue(0 in out_pa['attention_mask'][1]    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							='$$$'
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   bos_token=A_					,   add_bos_token=A_    )

         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']

         __UpperCamelCase    							=tokenizer.bos_token_id

         __UpperCamelCase    							=tokenizer(A_    )
         __UpperCamelCase    							=tokenizer(A_    )

         self.assertEqual(out_s.input_ids[0]					,   A_    )
         self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

         __UpperCamelCase    							=tokenizer.decode(out_s.input_ids    )
         __UpperCamelCase    							=tokenizer.batch_decode(out_sa.input_ids    )

         self.assertEqual(decode_s.split()[0]					,   A_    )
         self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )






    def _a					(			self    )  ->       Optional[int]:
         pass






    def _a					(			self    )  ->       Any:
         # TODO: change to self.get_tokenizers() when the fast version is implemented
         __UpperCamelCase    							=[self.get_tokenizer(do_lower_case=A_					,   add_bos_token=A_    )]
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   __UpperCamelCase    							='Encode this.'
                   __UpperCamelCase    							='This one too please.'
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   encoded_sequence += tokenizer.encode(A_					,   add_special_tokens=A_    )
                   __UpperCamelCase    							=tokenizer.encode_plus(
                       A_					,   A_					,   add_special_tokens=A_					,   return_special_tokens_mask=A_					,   )
                   __UpperCamelCase    							=encoded_sequence_dict['input_ids']
                   __UpperCamelCase    							=encoded_sequence_dict['special_tokens_mask']
                   self.assertEqual(len(A_    )					,   len(A_    )    )

                   __UpperCamelCase    							=[
                       (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_    )
                   ]
                   __UpperCamelCase    							=[x for x in filtered_sequence if x is not None]
                   self.assertEqual(A_					,   A_    )






@require_tokenizers
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Optional[Any]:
         # More context:
         # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
         # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
         # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('test_opt'    )

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./test_opt'    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   use_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # Same as above
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    @unittest.skip('This test is failing because of a bug in the fast tokenizer'    )
    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )

         __UpperCamelCase    							='bos'
         __UpperCamelCase    							=tokenizer.get_vocab()['bos']

         __UpperCamelCase    							='A photo of a cat'
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # We changed the bos token
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('./tok'    )
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./tok'    )
         self.assertTrue(tokenizer.is_fast    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )





