






"""simple docstring"""



import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
    from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import PoolFormerImageProcessor

class 	a					(       a_     ):







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(_lowerCamelCase							,		'hidden_sizes'	)	)
      self.parent.assertTrue(hasattr(_lowerCamelCase							,		'num_encoder_blocks'	)	)


class 	a					:







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=1_3							,		_lowerCamelCase=6_4							,		_lowerCamelCase=3							,		_lowerCamelCase=4							,		_lowerCamelCase=[2, 2, 2, 2]							,		_lowerCamelCase=[8, 4, 2, 1]							,		_lowerCamelCase=[1_6, 3_2, 6_4, 1_2_8]							,		_lowerCamelCase=[1, 4, 8, 1_6]							,		_lowerCamelCase=False							,		_lowerCamelCase=True							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=3							,		_lowerCamelCase=None							,		):
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	image_size
      lowercase							   =	num_channels
      lowercase							   =	num_encoder_blocks
      lowercase							   =	sr_ratios
      lowercase							   =	depths
      lowercase							   =	hidden_sizes
      lowercase							   =	downsampling_rates
      lowercase							   =	is_training
      lowercase							   =	use_labels
      lowercase							   =	hidden_act
      lowercase							   =	hidden_dropout_prob
      lowercase							   =	initializer_range
      lowercase							   =	num_labels
      lowercase							   =	scope







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

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

      lowercase							   =	PoolFormerConfig(
          image_size=self.image_size							,		num_channels=self.num_channels							,		num_encoder_blocks=self.num_encoder_blocks							,		depths=self.depths							,		hidden_sizes=self.hidden_sizes							,		hidden_act=self.hidden_act							,		hidden_dropout_prob=self.hidden_dropout_prob							,		initializer_range=self.initializer_range							,		)

      return config, pixel_values, labels







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	PoolFormerModel(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(_lowerCamelCase	)
      lowercase							   =	lowercase							   =	self.image_size // 3_2.0
      self.parent.assertEqual(
          result.last_hidden_state.shape							,		(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.prepare_config_and_inputs()
      lowercase   ,					lowercase   ,					lowercase							   =	config_and_inputs
      lowercase							   =	{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class 	a					(       a_,    a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					Optional[int]     					=(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
    UpperCAmelCase_			:					Optional[int]     					=(
        {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase_			:					str     					=False
    UpperCAmelCase_			:					int     					=False
    UpperCAmelCase_			:					Optional[int]     					=False
    UpperCAmelCase_			:					str     					=False
    UpperCAmelCase_			:					List[str]     					=False







    def       UpperCamelCase_				(      self	):
      lowercase							   =	PoolFormerModelTester(self	)
      lowercase							   =	PoolFormerConfigTester(self							,		config_class=_lowerCamelCase	)







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







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase	)







    @unittest.skip('PoolFormer does not use inputs_embeds'	)
    def       UpperCamelCase_				(      self	):
      pass







    @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'	)
    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      def check_hidden_states_output(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	model_class(_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()

        with torch.no_grad():
          lowercase							   =	model(**self._prepare_for_class(_lowerCamelCase							,		_lowerCamelCase	)	)

        lowercase							   =	outputs.hidden_states

        lowercase							   =	self.model_tester.num_encoder_blocks
        self.assertEqual(len(_lowerCamelCase	)							,		_lowerCamelCase	)

        # verify the first hidden states (first block)
        self.assertListEqual(
            list(hidden_states[0].shape[-3:]	)							,		[
                self.model_tester.hidden_sizes[0],
                self.model_tester.image_size // 4,
                self.model_tester.image_size // 4,
            ]							,		)

      lowercase   ,					lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase							   =	True
        check_hidden_states_output(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase							   =	True

        check_hidden_states_output(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      if not self.model_tester.is_training:
        return

      lowercase   ,					lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()
      lowercase							   =	True

      for model_class in self.all_model_classes:
        if model_class in get_values(_lowerCamelCase	):
          continue
        lowercase							   =	model_class(_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.train()
        lowercase							   =	self._prepare_for_class(_lowerCamelCase							,		_lowerCamelCase							,		return_labels=_lowerCamelCase	)
        lowercase							   =	model(**_lowerCamelCase	).loss
        loss.backward()







    def       UpperCamelCase_				(      self	):
      lowercase   ,					lowercase							   =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase							   =	model_class(_lowerCamelCase	)
        lowercase							   =	inspect.signature(model.forward	)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase							   =	[*signature.parameters.keys()]

        lowercase							   =	['pixel_values']
        self.assertListEqual(arg_names[:1]							,		_lowerCamelCase	)







    @slow
    def       UpperCamelCase_				(      self	):
      for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase							   =	PoolFormerModel.from_pretrained(_lowerCamelCase	)
        self.assertIsNotNone(_lowerCamelCase	)


def 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





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



@require_torch
class 	a					(       unittest.TestCase     ):







    @slow
    def       UpperCamelCase_				(      self	):
      lowercase							   =	PoolFormerImageProcessor()
      lowercase							   =	PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'	).to(_lowerCamelCase	)

      lowercase							   =	image_processor(images=prepare_img()							,		return_tensors='pt'	).to(_lowerCamelCase	)

      # forward pass
      with torch.no_grad():
        lowercase							   =	model(**_lowerCamelCase	)

      # verify the logits
      lowercase							   =	torch.Size((1, 1_0_0_0)	)
      self.assertEqual(outputs.logits.shape							,		_lowerCamelCase	)

      lowercase							   =	torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]	).to(_lowerCamelCase	)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]							,		_lowerCamelCase							,		atol=1e-4	)	)










"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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


_UpperCamelCase		:  Any						=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCamelCase		:  Optional[int]						=		{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

_UpperCamelCase		:  Union[str, Any]						=		{
    'RUCAIBox/mvp': 1_0_2_4,
}
@lru_cache()
def 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





  lowercase							   =	(
      list(range(ord('!'   )		, ord('~'   ) + 1   )   ) + list(range(ord('¡'   )		, ord('¬'   ) + 1   )   ) + list(range(ord('®'   )		, ord('ÿ'   ) + 1   )   )
  )
  lowercase							   =	bs[:]
  lowercase							   =	0
  for b in range(2**8   ):
    if b not in bs:
      bs.append(__snake_case   )
      cs.append(2**8 + n   )
      n += 1
  lowercase							   =	[chr(__snake_case   ) for n in cs]
  return dict(zip(__snake_case		, __snake_case   )   )
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int   ):





  '''simple docstring'''





  lowercase							   =	set()
  lowercase							   =	word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)   )
    lowercase							   =	char
  return pairs



class 	a					(       a_     ):
    UpperCAmelCase_			:					Tuple     					=VOCAB_FILES_NAMES
    UpperCAmelCase_			:					int     					=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_			:					Optional[int]     					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_			:					Any     					=["input_ids", "attention_mask"]







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase="replace"							,		_lowerCamelCase="<s>"							,		_lowerCamelCase="</s>"							,		_lowerCamelCase="</s>"							,		_lowerCamelCase="<s>"							,		_lowerCamelCase="<unk>"							,		_lowerCamelCase="<pad>"							,		_lowerCamelCase="<mask>"							,		_lowerCamelCase=False							,		**_lowerCamelCase							,		):
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else bos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else eos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else sep_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else cls_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else unk_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else mask_token

      super().__init__(
          errors=_lowerCamelCase							,		bos_token=_lowerCamelCase							,		eos_token=_lowerCamelCase							,		unk_token=_lowerCamelCase							,		sep_token=_lowerCamelCase							,		cls_token=_lowerCamelCase							,		pad_token=_lowerCamelCase							,		mask_token=_lowerCamelCase							,		add_prefix_space=_lowerCamelCase							,		**_lowerCamelCase							,		)
      with open(_lowerCamelCase							,		encoding='utf-8'	) as vocab_handle:
        lowercase							   =	json.load(_lowerCamelCase	)
      lowercase							   =	{v: k for k, v in self.encoder.items()}
      lowercase							   =	errors  # how to handle errors in decoding
      lowercase							   =	bytes_to_unicode()
      lowercase							   =	{v: k for k, v in self.byte_encoder.items()}
      with open(_lowerCamelCase							,		encoding='utf-8'	) as merges_handle:
        lowercase							   =	merges_handle.read().split('\n'	)[1:-1]
      lowercase							   =	[tuple(merge.split()	) for merge in bpe_merges]
      lowercase							   =	dict(zip(_lowerCamelCase							,		range(len(_lowerCamelCase	)	)	)	)
      lowercase							   =	{}
      lowercase							   =	add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      lowercase							   =	re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)







    @property
    def       UpperCamelCase_				(      self	):
      return len(self.encoder	)







    def       UpperCamelCase_				(      self	):
      return dict(self.encoder							,		**self.added_tokens_encoder	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      if token in self.cache:
        return self.cache[token]
      lowercase							   =	tuple(_lowerCamelCase	)
      lowercase							   =	get_pairs(_lowerCamelCase	)

      if not pairs:
        return token

      while True:
        lowercase							   =	min(_lowerCamelCase							,		key=lambda _lowerCamelCase	: self.bpe_ranks.get(_lowerCamelCase							,		float('inf'	)	)	)
        if bigram not in self.bpe_ranks:
          break
        lowercase   ,					lowercase							   =	bigram
        lowercase							   =	[]
        lowercase							   =	0
        while i < len(_lowerCamelCase	):
          try:
            lowercase							   =	word.index(_lowerCamelCase							,		_lowerCamelCase	)
          except ValueError:
            new_word.extend(word[i:]	)
            break
          else:
            new_word.extend(word[i:j]	)
            lowercase							   =	j

          if word[i] == first and i < len(_lowerCamelCase	) - 1 and word[i + 1] == second:
            new_word.append(first + second	)
            i += 2
          else:
            new_word.append(word[i]	)
            i += 1
        lowercase							   =	tuple(_lowerCamelCase	)
        lowercase							   =	new_word
        if len(_lowerCamelCase	) == 1:
          break
        else:
          lowercase							   =	get_pairs(_lowerCamelCase	)
      lowercase							   =	' '.join(_lowerCamelCase	)
      lowercase							   =	word
      return word







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	[]
      for token in re.findall(self.pat							,		_lowerCamelCase	):
        lowercase							   =	''.join(
            self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase	).split(' '	)	)
      return bpe_tokens







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.encoder.get(_lowerCamelCase							,		self.encoder.get(self.unk_token	)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.decoder.get(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	''.join(_lowerCamelCase	)
      lowercase							   =	bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'							,		errors=self.errors	)
      return text







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      if not os.path.isdir(_lowerCamelCase	):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
        return
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

      with open(_lowerCamelCase							,		'w'							,		encoding='utf-8'	) as f:
        f.write(json.dumps(self.encoder							,		indent=2							,		sort_keys=_lowerCamelCase							,		ensure_ascii=_lowerCamelCase	) + '\n'	)

      lowercase							   =	0
      with open(_lowerCamelCase							,		'w'							,		encoding='utf-8'	) as writer:
        writer.write('#version: 0.2\n'	)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,		key=lambda _lowerCamelCase	: kv[1]	):
          if index != token_index:
            logger.warning(
                F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                ' Please check that the tokenizer is not corrupted!'	)
            lowercase							   =	token_index
          writer.write(' '.join(_lowerCamelCase	) + '\n'	)
          index += 1

      return vocab_file, merge_file







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowercase							   =	[self.cls_token_id]
      lowercase							   =	[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = False	):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowerCamelCase							,		token_ids_a=_lowerCamelCase							,		already_has_special_tokens=_lowerCamelCase	)

      if token_ids_a is None:
        return [1] + ([0] * len(_lowerCamelCase	)) + [1]
      return [1] + ([0] * len(_lowerCamelCase	)) + [1, 1] + ([0] * len(_lowerCamelCase	)) + [1]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      lowercase							   =	[self.sep_token_id]
      lowercase							   =	[self.cls_token_id]

      if token_ids_a is None:
        return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		**_lowerCamelCase	):
      lowercase							   =	kwargs.pop('add_prefix_space'							,		self.add_prefix_space	)
      if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase	) > 0 and not text[0].isspace()):
        lowercase							   =	' ' + text
      return (text, kwargs)


