






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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase  :							Union[str, Any]			  =    logging.get_logger(__name__)


lowercase  :							Union[str, Any]			  =    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase  :							Optional[Any]			  =    {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowercase  :							Optional[Any]			  =    {
    '''allenai/led-base-16384''': 1_63_84,
}

@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def      lowerCAmelCase__	(    ):
  snake_case_				:							int						 =       (
      list(range(ord("!"    )  ,      ord("~"    ) + 1    )    ) + list(range(ord("¡"    )  ,      ord("¬"    ) + 1    )    ) + list(range(ord("®"    )  ,      ord("ÿ"    ) + 1    )    )
  )
  snake_case_				:							str						 =       bs[:]
  snake_case_				:							Tuple						 =       0
  for b in range(2**8    ):
    if b not in bs:
      bs.append(_a    )
      cs.append(2**8 + n    )
      n += 1
  snake_case_				:							Union[str, Any]						 =       [chr(_a    ) for n in cs]
  return dict(zip(_a  ,      _a    )    )

def      lowerCAmelCase__	(    _a       :			Tuple    ):
  snake_case_				:							int						 =       set()
  snake_case_				:							Union[str, Any]						 =       word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    snake_case_				:							Dict						 =       char
  return pairs




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


      '''simple docstring'''




      A     :   Tuple						=					VOCAB_FILES_NAMES
      A     :   Any						=					PRETRAINED_VOCAB_FILES_MAP
      A     :   Optional[int]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A     :   List[str]						=					['input_ids', 'attention_mask']






      def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE="replace"		,				_SCREAMING_SNAKE_CASE="<s>"		,				_SCREAMING_SNAKE_CASE="</s>"		,				_SCREAMING_SNAKE_CASE="</s>"		,				_SCREAMING_SNAKE_CASE="<s>"		,				_SCREAMING_SNAKE_CASE="<unk>"		,				_SCREAMING_SNAKE_CASE="<pad>"		,				_SCREAMING_SNAKE_CASE="<mask>"		,				_SCREAMING_SNAKE_CASE=False		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
        snake_case_				:							Tuple						 =       AddedToken(_SCREAMING_SNAKE_CASE		,				lstrip=_SCREAMING_SNAKE_CASE		,				rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) else bos_token
        snake_case_				:							Any						 =       AddedToken(_SCREAMING_SNAKE_CASE		,				lstrip=_SCREAMING_SNAKE_CASE		,				rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) else eos_token
        snake_case_				:							Dict						 =       AddedToken(_SCREAMING_SNAKE_CASE		,				lstrip=_SCREAMING_SNAKE_CASE		,				rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) else sep_token
        snake_case_				:							Union[str, Any]						 =       AddedToken(_SCREAMING_SNAKE_CASE		,				lstrip=_SCREAMING_SNAKE_CASE		,				rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) else cls_token
        snake_case_				:							List[str]						 =       AddedToken(_SCREAMING_SNAKE_CASE		,				lstrip=_SCREAMING_SNAKE_CASE		,				rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) else unk_token
        snake_case_				:							List[Any]						 =       AddedToken(_SCREAMING_SNAKE_CASE		,				lstrip=_SCREAMING_SNAKE_CASE		,				rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) else pad_token

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

        super().__init__(
            errors=_SCREAMING_SNAKE_CASE		,				bos_token=_SCREAMING_SNAKE_CASE		,				eos_token=_SCREAMING_SNAKE_CASE		,				unk_token=_SCREAMING_SNAKE_CASE		,				sep_token=_SCREAMING_SNAKE_CASE		,				cls_token=_SCREAMING_SNAKE_CASE		,				pad_token=_SCREAMING_SNAKE_CASE		,				mask_token=_SCREAMING_SNAKE_CASE		,				add_prefix_space=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)

        with open(_SCREAMING_SNAKE_CASE		,				encoding="utf-8"				) as vocab_handle:
          snake_case_				:							Union[str, Any]						 =       json.load(_SCREAMING_SNAKE_CASE				)
        snake_case_				:							Optional[int]						 =       {v: k for k, v in self.encoder.items()}
        snake_case_				:							Tuple						 =       errors  # how to handle errors in decoding
        snake_case_				:							Any						 =       bytes_to_unicode()
        snake_case_				:							Optional[Any]						 =       {v: k for k, v in self.byte_encoder.items()}
        with open(_SCREAMING_SNAKE_CASE		,				encoding="utf-8"				) as merges_handle:
          snake_case_				:							List[Any]						 =       merges_handle.read().split("\n"				)[1:-1]
        snake_case_				:							Tuple						 =       [tuple(merge.split()				) for merge in bpe_merges]
        snake_case_				:							List[str]						 =       dict(zip(_SCREAMING_SNAKE_CASE		,				range(len(_SCREAMING_SNAKE_CASE				)				)				)				)
        snake_case_				:							str						 =       {}
        snake_case_				:							Dict						 =       add_prefix_space

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






      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def       _lowerCAmelCase       (	self				) ->				Optional[int]:
        return len(self.encoder				)






      def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
        return dict(self.encoder		,				**self.added_tokens_encoder				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Dict:
        if token in self.cache:
          return self.cache[token]
        snake_case_				:							List[str]						 =       tuple(_SCREAMING_SNAKE_CASE				)
        snake_case_				:							str						 =       get_pairs(_SCREAMING_SNAKE_CASE				)

        if not pairs:
          return token

        while True:
          snake_case_				:							List[Any]						 =       min(_SCREAMING_SNAKE_CASE		,				key=lambda _SCREAMING_SNAKE_CASE				: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE		,				float("inf"				)				)				)
          if bigram not in self.bpe_ranks:
            break
          snake_case_				:							str						 =       bigram
          snake_case_				:							Tuple						 =       []
          snake_case_				:							List[str]						 =       0
          while i < len(_SCREAMING_SNAKE_CASE				):
            try:
              snake_case_				:							Optional[Any]						 =       word.index(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
            except ValueError:
              new_word.extend(word[i:]				)
              break
            else:
              new_word.extend(word[i:j]				)
              snake_case_				:							Optional[int]						 =       j

            if word[i] == first and i < len(_SCREAMING_SNAKE_CASE				) - 1 and word[i + 1] == second:
              new_word.append(first + second				)
              i += 2
            else:
              new_word.append(word[i]				)
              i += 1
          snake_case_				:							Tuple						 =       tuple(_SCREAMING_SNAKE_CASE				)
          snake_case_				:							str						 =       new_word
          if len(_SCREAMING_SNAKE_CASE				) == 1:
            break
          else:
            snake_case_				:							Dict						 =       get_pairs(_SCREAMING_SNAKE_CASE				)
        snake_case_				:							Optional[Any]						 =       " ".join(_SCREAMING_SNAKE_CASE				)
        snake_case_				:							Union[str, Any]						 =       word
        return word






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
        snake_case_				:							int						 =       []
        for token in re.findall(self.pat		,				_SCREAMING_SNAKE_CASE				):
          snake_case_				:							List[Any]						 =       "".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(_SCREAMING_SNAKE_CASE				).split(" "				)				)
        return bpe_tokens






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
        return self.encoder.get(_SCREAMING_SNAKE_CASE		,				self.encoder.get(self.unk_token				)				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				str:
        return self.decoder.get(_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
        snake_case_				:							Union[str, Any]						 =       "".join(_SCREAMING_SNAKE_CASE				)
        snake_case_				:							Any						 =       bytearray([self.byte_decoder[c] for c in text]				).decode("utf-8"		,				errors=self.errors				)
        return text






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				Tuple[str]:
        if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
          return
        snake_case_				:							List[str]						 =       os.path.join(
            _SCREAMING_SNAKE_CASE		,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
        snake_case_				:							Tuple						 =       os.path.join(
            _SCREAMING_SNAKE_CASE		,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]				)

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

        snake_case_				:							str						 =       0
        with open(_SCREAMING_SNAKE_CASE		,				"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 _SCREAMING_SNAKE_CASE				: 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!"				)
              snake_case_				:							Union[str, Any]						 =       token_index
            writer.write(" ".join(_SCREAMING_SNAKE_CASE				) + "\n"				)
            index += 1

        return vocab_file, merge_file






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:
        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        snake_case_				:							List[Any]						 =       [self.cls_token_id]
        snake_case_				:							int						 =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False				) ->				List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=_SCREAMING_SNAKE_CASE		,				token_ids_a=_SCREAMING_SNAKE_CASE		,				already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

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






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:
        snake_case_				:							Any						 =       [self.sep_token_id]
        snake_case_				:							Optional[int]						 =       [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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=False		,				**_SCREAMING_SNAKE_CASE				) ->				Tuple:
        snake_case_				:							Optional[Any]						 =       kwargs.pop("add_prefix_space"		,				self.add_prefix_space				)
        if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE				) > 0 and not text[0].isspace()):
          snake_case_				:							Tuple						 =       " " + text
        return (text, kwargs)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				) ->				dict:
        snake_case_				:							Any						 =       super()._pad(
            encoded_inputs=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding_strategy=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				)

        # Load from model defaults
        if return_attention_mask is None:
          snake_case_				:							int						 =       "attention_mask" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
          snake_case_				:							List[str]						 =       encoded_inputs[self.model_input_names[0]]
          # `global_attention_mask` need to have the same length as other (sequential) inputs.
          snake_case_				:							Tuple						 =       len(encoded_inputs["global_attention_mask"]				) != len(_SCREAMING_SNAKE_CASE				)

          if needs_to_be_padded:
            snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				) - len(encoded_inputs["global_attention_mask"]				)

            if self.padding_side == "right":
              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
              snake_case_				:							Union[str, Any]						 =       (
                  encoded_inputs["global_attention_mask"] + [-1] * difference
              )
            elif self.padding_side == "left":
              snake_case_				:							List[Any]						 =       [-1] * difference + encoded_inputs[
                  "global_attention_mask"
              ]
            else:
              raise ValueError("Invalid padding strategy:" + str(self.padding_side				)				)

        return encoded_inputs








from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]		,				_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE="relu"		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=None		,				) ->				List[str]:
								snake_case_				:							str						 =       parent
								snake_case_				:							Optional[int]						 =       batch_size
								snake_case_				:							Dict						 =       image_size
								snake_case_				:							Tuple						 =       num_channels
								snake_case_				:							Union[str, Any]						 =       embeddings_size
								snake_case_				:							int						 =       hidden_sizes
								snake_case_				:							Optional[int]						 =       depths
								snake_case_				:							Dict						 =       is_training
								snake_case_				:							Tuple						 =       use_labels
								snake_case_				:							int						 =       hidden_act
								snake_case_				:							List[str]						 =       num_labels
								snake_case_				:							List[Any]						 =       scope
								snake_case_				:							Union[str, Any]						 =       len(_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								snake_case_				:							Optional[Any]						 =       None
								if self.use_labels:
										snake_case_				:							Any						 =       ids_tensor([self.batch_size]		,				self.num_labels				)

								snake_case_				:							Optional[int]						 =       self.get_config()
								return config, pixel_values, labels






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return RegNetConfig(
								    num_channels=self.num_channels		,				embeddings_size=self.embeddings_size		,				hidden_sizes=self.hidden_sizes		,				depths=self.depths		,				hidden_act=self.hidden_act		,				num_labels=self.num_labels		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								snake_case_				:							Union[str, Any]						 =       TFRegNetModel(config=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								snake_case_				:							Optional[int]						 =       self.num_labels
								snake_case_				:							Tuple						 =       TFRegNetForImageClassification(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Optional[Any]						 =       self.prepare_config_and_inputs()
								snake_case_ ,       snake_case_ ,       snake_case_				:							Union[str, Any]						 =       config_and_inputs
								snake_case_				:							Any						 =       {"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Optional[int]						=					(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						A     :   Dict						=					(
						    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						A     :   List[Any]						=					False
						A     :   List[str]						=					False
						A     :   Optional[Any]						=					False
						A     :   List[Any]						=					False
						A     :   List[Any]						=					False






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							List[Any]						 =       TFRegNetModelTester(self				)
								snake_case_				:							Any						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								return






						@unittest.skip(reason="RegNet does not use inputs_embeds"				)
						def       _lowerCAmelCase       (	self				) ->				Dict:
								pass






						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"				)				) == 0		,				reason="TF does not support backprop for grouped convolutions on CPU."		,				)
						@slow
						def       _lowerCAmelCase       (	self				) ->				Any:
								super().test_keras_fit()






						@unittest.skip(reason="RegNet does not support input and output embeddings"				)
						def       _lowerCAmelCase       (	self				) ->				str:
								pass






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_ ,       snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							int						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							str						 =       inspect.signature(model.call				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case_				:							List[str]						 =       [*signature.parameters.keys()]

										snake_case_				:							Dict						 =       ["pixel_values"]
										self.assertListEqual(arg_names[:1]		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								def check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							List[str]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Tuple						 =       model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)		,				training=_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Union[str, Any]						 =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										snake_case_				:							Any						 =       self.model_tester.num_stages
										self.assertEqual(len(_SCREAMING_SNAKE_CASE				)		,				expected_num_stages + 1				)

										# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]				)		,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2]		,				)

								snake_case_ ,       snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				:							Optional[int]						 =       ["basic", "bottleneck"]
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												snake_case_				:							Any						 =       layer_type
												snake_case_				:							int						 =       True
												check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												snake_case_				:							Any						 =       True

												check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_ ,       snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE={}				):
										snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				).to_tuple()

										def recursive_check(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
												if isinstance(_SCREAMING_SNAKE_CASE		,				(List, Tuple)				):
														for tuple_iterable_value, dict_iterable_value in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
																recursive_check(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
												elif tuple_object is None:
														return
												else:
														self.assertTrue(
														    all(tf.equal(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				)		,				msg=(
														        "Tuple and dict output are not equal. Difference:"
														        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object				)				)}'''
														    )		,				)

												recursive_check(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								for model_class in self.all_model_classes:
										snake_case_				:							str						 =       model_class(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							List[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Union[str, Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Optional[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				{"output_hidden_states": True}				)

										snake_case_				:							List[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Dict						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				{"output_hidden_states": True}				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Any:
								for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case_				:							Dict						 =       TFRegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		snake_case_				:							int						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




@require_tf
@require_vision
class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						@cached_property
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								return (
								    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
								    if is_vision_available()
								    else None
								)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							str						 =       TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

								snake_case_				:							int						 =       self.default_image_processor
								snake_case_				:							List[Any]						 =       prepare_img()
								snake_case_				:							str						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="tf"				)

								# forward pass
								snake_case_				:							Any						 =       model(**_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)

								# verify the logits
								snake_case_				:							List[str]						 =       tf.TensorShape((1, 1000)				)
								self.assertEqual(outputs.logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       tf.constant([-0.4180, -1.5051, -3.4836]				)

								tf.debugging.assert_near(outputs.logits[0, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=1e-4				)
