code: 
  
  """simple docstring"""
  
  
  
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
  
  
  __A  :					Union[str, Any]												= logging.get_logger(__name__)
  
  __A  :					List[str]												= {
      'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
      'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
      'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
      'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
      'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
  }
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					"gpt2"
  		SCREAMING_SNAKE_CASE              =					["past_key_values"]
  		SCREAMING_SNAKE_CASE              =					{
  		    "hidden_size": "n_embd",
  		    "max_position_embeddings": "n_positions",
  		    "num_attention_heads": "n_head",
  		    "num_hidden_layers": "n_layer",
  		}
  
  
  		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Any=5_0_2_5_7  ,  __SCREAMING_SNAKE_CASE				:	Dict=1_0_2_4  ,  __SCREAMING_SNAKE_CASE				:	int=7_6_8  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_2  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple="gelu_new"  ,  __SCREAMING_SNAKE_CASE				:	str=0.1  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=1E-5  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.0_2  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]="cls_index"  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=True  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None  ,  __SCREAMING_SNAKE_CASE				:	int=True  ,  __SCREAMING_SNAKE_CASE				:	str=0.1  ,  __SCREAMING_SNAKE_CASE				:	Any=True  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=True  ,  __SCREAMING_SNAKE_CASE				:	str=5_0_2_5_6  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=5_0_2_5_6  ,  __SCREAMING_SNAKE_CASE				:	Any=False  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=False  ,  **__SCREAMING_SNAKE_CASE				:	Dict  ,  ):
  							A    =      vocab_size
  							A    =      n_positions
  							A    =      n_embd
  							A    =      n_layer
  							A    =      n_head
  							A    =      n_inner
  							A    =      activation_function
  							A    =      resid_pdrop
  							A    =      embd_pdrop
  							A    =      attn_pdrop
  							A    =      layer_norm_epsilon
  							A    =      initializer_range
  							A    =      summary_type
  							A    =      summary_use_proj
  							A    =      summary_activation
  							A    =      summary_first_dropout
  							A    =      summary_proj_to_labels
  							A    =      scale_attn_weights
  							A    =      use_cache
  							A    =      scale_attn_by_inverse_layer_idx
  							A    =      reorder_and_upcast_attn
  
  							A    =      bos_token_id
  							A    =      eos_token_id
  
  							super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		def __init__(self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	int = "default"  ,  __SCREAMING_SNAKE_CASE				:	str = None  ,  __SCREAMING_SNAKE_CASE				:	Tuple = False  ,  ):
  							super().__init__(__SCREAMING_SNAKE_CASE  ,  task=__SCREAMING_SNAKE_CASE  ,  patching_specs=__SCREAMING_SNAKE_CASE  ,  use_past=__SCREAMING_SNAKE_CASE)
  							if not getattr(self._config  ,  "pad_token_id"  ,  __SCREAMING_SNAKE_CASE):
  												# TODO: how to do that better?
  												A    =      0
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							A    =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
  							if self.use_past:
  												self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,  direction="inputs")
  												A    =      {0: '''batch''', 1: '''past_sequence + sequence'''}
  							else:
  												A    =      {0: '''batch''', 1: '''sequence'''}
  
  							return common_inputs
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							return self._config.n_layer
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							return self._config.n_head
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[Any] = -1  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any] = -1  ,  __SCREAMING_SNAKE_CASE				:	int = False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any] = None  ,  ):
  							A    =      super(__SCREAMING_SNAKE_CASE  ,  self).generate_dummy_inputs(
  							    __SCREAMING_SNAKE_CASE  ,  batch_size=__SCREAMING_SNAKE_CASE  ,  seq_length=__SCREAMING_SNAKE_CASE  ,  is_pair=__SCREAMING_SNAKE_CASE  ,  framework=__SCREAMING_SNAKE_CASE)
  
  							# We need to order the input in the way they appears in the forward()
  							A    =      OrderedDict({"input_ids": common_inputs["input_ids"]})
  
  							# Need to add the past_keys
  							if self.use_past:
  												if not is_torch_available():
  																	raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
  												else:
  																	import torch
  
  																	A    =      common_inputs['''input_ids'''].shape
  																	# Not using the same length for past_key_values
  																	A    =      seqlen + 2
  																	A    =      (
  																	    batch,
  																	    self.num_attention_heads,
  																	    past_key_values_length,
  																	    self._config.hidden_size // self.num_attention_heads,
  																	)
  																	A    =      [
  																	    (torch.zeros(__SCREAMING_SNAKE_CASE), torch.zeros(__SCREAMING_SNAKE_CASE)) for _ in range(self.num_layers)
  																	]
  
  							A    =      common_inputs['''attention_mask''']
  							if self.use_past:
  												A    =      ordered_inputs['''attention_mask'''].dtype
  												A    =      torch.cat(
  												    [ordered_inputs["attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  dtype=__SCREAMING_SNAKE_CASE)]  ,  dim=1)
  
  							return ordered_inputs
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							return 1_3
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      PreTrainedTokenizer,
      is_vision_available,
  )
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      nested_simplify,
      require_tf,
      require_torch,
      require_torch_or_tf,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_vision_available():
  	from PIL import Image
  else:
  
  
  
  
  
  	class     __UpperCamelCase				:
  
  
  			@staticmethod
  			def       SCREAMING_SNAKE_CASE__     (*__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	Any):
  								pass
  
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class     __UpperCamelCase				(     unittest.TestCase		):
  		SCREAMING_SNAKE_CASE              =					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  		SCREAMING_SNAKE_CASE              =					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      ImageClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,  image_processor=__SCREAMING_SNAKE_CASE  ,  top_k=2)
  							A    =      [
  							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
  							    "http://images.cocodataset.org/val2017/000000039769.jpg",
  							]
  							return image_classifier, examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
  							A    =      image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")
  
  							self.assertEqual(
  							    __SCREAMING_SNAKE_CASE  ,  [
  							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							    ]  ,  )
  
  							import datasets
  
  							A    =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,  "image"  ,  split="test")
  
  							# Accepts URL + PIL.Image + lists
  							A    =      image_classifier(
  							    [
  							        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							        # RGBA
  							        dataset[0]["file"],
  							        # LA
  							        dataset[1]["file"],
  							        # L
  							        dataset[2]["file"],
  							    ])
  							self.assertEqual(
  							    __SCREAMING_SNAKE_CASE  ,  [
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							    ]  ,  )
  
  
  		@require_torch
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							A    =      "hf-internal-testing/tiny-random-vit"
  							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE)
  
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )
  
  							A    =      image_classifier(
  							    [
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							    ]  ,  top_k=2  ,  )
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							    ]  ,  )
  
  
  		@require_tf
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							A    =      "hf-internal-testing/tiny-random-vit"
  							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE  ,  framework="tf")
  
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )
  
  							A    =      image_classifier(
  							    [
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							    ]  ,  top_k=2  ,  )
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							    ]  ,  )
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							A    =      PreTrainedTokenizer()
  
  							# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
  							A    =      pipeline(
  							    "image-classification"  ,  model="hf-internal-testing/tiny-random-vit"  ,  tokenizer=__SCREAMING_SNAKE_CASE)
  
  							self.assertIs(image_classifier.tokenizer  ,  __SCREAMING_SNAKE_CASE)
  
  
  		@slow
  		@require_torch
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							# Perceiver is not tested by `run_pipeline_test` properly.
  							# That is because the type of feature_extractor and model preprocessor need to be kept
  							# in sync, which is not the case in the current design
  							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-conv")
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
  							        {"score": 0.3_2_1, "label": "tiger cat"},
  							        {"score": 0.0_5_0_2, "label": "Egyptian cat"},
  							        {"score": 0.0_1_3_7, "label": "crib, cot"},
  							        {"score": 0.0_0_7, "label": "radiator"},
  							    ]  ,  )
  
  							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-fourier")
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
  							        {"score": 0.1_3_0_9, "label": "tiger cat"},
  							        {"score": 0.0_7_2_2, "label": "Egyptian cat"},
  							        {"score": 0.0_7_0_7, "label": "remote control, remote"},
  							        {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
  							    ]  ,  )
  
  							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-learned")
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
  							        {"score": 0.2_3_6_2, "label": "Egyptian cat"},
  							        {"score": 0.1_8_5_6, "label": "tiger cat"},
  							        {"score": 0.0_3_2_4, "label": "remote control, remote"},
  							        {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
  							    ]  ,  )
  
  
  
  
  
  