



'''simple docstring'''
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ImageGPTImageProcessor






class 							lowercase__     (							unittest.TestCase       ):





	def __init__(			self :    Optional[int]			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Tuple=7			,lowerCamelCase__ :    str=3			,lowerCamelCase__ :    List[Any]=18			,lowerCamelCase__ :    Optional[Any]=30			,lowerCamelCase__ :    List[str]=400			,lowerCamelCase__ :    Dict=True			,lowerCamelCase__ :    Any=None			,lowerCamelCase__ :    Union[str, Any]=True			,):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       size if size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase :		Dict			    =       parent
							_UpperCamelCase :		str			    =       batch_size
							_UpperCamelCase :		Optional[int]			    =       num_channels
							_UpperCamelCase :		List[Any]			    =       image_size
							_UpperCamelCase :		Dict			    =       min_resolution
							_UpperCamelCase :		Optional[Any]			    =       max_resolution
							_UpperCamelCase :		Any			    =       do_resize
							_UpperCamelCase :		Any			    =       size
							_UpperCamelCase :		str			    =       do_normalize





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
							            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
							        ]			),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}






@require_torch
@require_vision
class 							lowercase__     (							lowercase						, unittest.TestCase       ):
	lowercase__      =      ImageGPTImageProcessor if is_vision_available() else None





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       ImageGPTImageProcessingTester(self			)





	@property
	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(lowerCamelCase__			,'clusters'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_resize'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'size'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_normalize'			)			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       self.image_processing_class.from_dict(self.image_processor_dict			)
							self.assertEqual(image_processor.size			,{'height': 18, 'width': 18}			)

							_UpperCamelCase :		Optional[Any]			    =       self.image_processing_class.from_dict(self.image_processor_dict			,size=42			)
							self.assertEqual(image_processor.size			,{'height': 42, 'width': 42}			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       self.image_processing_class(**self.image_processor_dict			)
							_UpperCamelCase :		Optional[int]			    =       json.loads(image_processor.to_json_string()			)
							for key, value in self.image_processor_dict.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowerCamelCase__			,obj[key]			)			)
													else:
																			self.assertEqual(obj[key]			,lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       self.image_processing_class(**self.image_processor_dict			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													_UpperCamelCase :		Any			    =       os.path.join(lowerCamelCase__			,'image_processor.json'			)
													image_processor_first.to_json_file(lowerCamelCase__			)
													_UpperCamelCase :		Optional[int]			    =       self.image_processing_class.from_json_file(lowerCamelCase__			).to_dict()

							_UpperCamelCase :		Dict			    =       image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowerCamelCase__			,image_processor_second[key]			)			)
													else:
																			self.assertEqual(image_processor_first[key]			,lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       self.image_processing_class(**self.image_processor_dict			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(lowerCamelCase__			)
													_UpperCamelCase :		Optional[int]			    =       self.image_processing_class.from_pretrained(lowerCamelCase__			).to_dict()

							_UpperCamelCase :		Optional[int]			    =       image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowerCamelCase__			,image_processor_second[key]			)			)
													else:
																			self.assertEqual(image_processor_first[key]			,lowerCamelCase__			)





	@unittest.skip('ImageGPT requires clusters at initialization'			)
	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							pass






def A__   (					):
						_UpperCamelCase :		List[str]			    =       load_dataset('hf-internal-testing/fixtures_image_utils'  ,  split='test'							)

						_UpperCamelCase :		Any			    =       Image.open(dataset[4]['file']							)
						_UpperCamelCase :		Optional[int]			    =       Image.open(dataset[5]['file']							)

						_UpperCamelCase :		List[Any]			    =       [imagea, imagea]

						return images






@require_vision
@require_torch
class 							lowercase__     (							unittest.TestCase       ):





	@slow
	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'			)

							_UpperCamelCase :		Any			    =       prepare_images()

							# test non-batched
							_UpperCamelCase :		Any			    =       image_processing(images[0]			,return_tensors='pt'			)

							self.assertIsInstance(encoding.input_ids			,torch.LongTensor			)
							self.assertEqual(encoding.input_ids.shape			,(1, 1024)			)

							_UpperCamelCase :		Union[str, Any]			    =       [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()			,lowerCamelCase__			)

							# test batched
							_UpperCamelCase :		Any			    =       image_processing(lowerCamelCase__			,return_tensors='pt'			)

							self.assertIsInstance(encoding.input_ids			,torch.LongTensor			)
							self.assertEqual(encoding.input_ids.shape			,(2, 1024)			)

							_UpperCamelCase :		int			    =       [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()			,lowerCamelCase__			)







'''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


snake_case_			:     int  	=  logging.get_logger(__name__)

snake_case_			:     List[str]  	=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """gptj"""
	lowercase__      =      {
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(			self :    Any			,lowerCamelCase__ :    Optional[Any]=50400			,lowerCamelCase__ :    Tuple=2048			,lowerCamelCase__ :    Tuple=4096			,lowerCamelCase__ :    int=28			,lowerCamelCase__ :    Optional[Any]=16			,lowerCamelCase__ :    Optional[Any]=64			,lowerCamelCase__ :    List[Any]=None			,lowerCamelCase__ :    List[Any]="gelu_new"			,lowerCamelCase__ :    Optional[Any]=0.0			,lowerCamelCase__ :    List[str]=0.0			,lowerCamelCase__ :    List[Any]=0.0			,lowerCamelCase__ :    Tuple=1E-5			,lowerCamelCase__ :    int=0.0_2			,lowerCamelCase__ :    Any=True			,lowerCamelCase__ :    str=50256			,lowerCamelCase__ :    Any=50256			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       n_positions
							_UpperCamelCase :		Union[str, Any]			    =       n_embd
							_UpperCamelCase :		Any			    =       n_layer
							_UpperCamelCase :		Optional[int]			    =       n_head
							_UpperCamelCase :		List[str]			    =       n_inner
							_UpperCamelCase :		List[Any]			    =       rotary_dim
							_UpperCamelCase :		int			    =       activation_function
							_UpperCamelCase :		Dict			    =       resid_pdrop
							_UpperCamelCase :		Any			    =       embd_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       attn_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       layer_norm_epsilon
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		str			    =       use_cache

							_UpperCamelCase :		Union[str, Any]			    =       bos_token_id
							_UpperCamelCase :		Any			    =       eos_token_id

							super().__init__(
							    bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,tie_word_embeddings=lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Tuple			,lowerCamelCase__ :    PretrainedConfig			,lowerCamelCase__ :    str = "default"			,lowerCamelCase__ :    List[PatchingSpec] = None			,lowerCamelCase__ :    bool = False			,):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			,task=lowerCamelCase__			,patching_specs=lowerCamelCase__			,use_past=lowerCamelCase__			)
							if not getattr(self._config			,'pad_token_id'			,lowerCamelCase__			):
													# TODO: how to do that better?
													_UpperCamelCase :		int			    =       0





	@property
	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
							if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__			,direction='inputs'			)
													_UpperCamelCase :		Tuple			    =       {0: 'batch', 1: 'past_sequence + sequence'}
							else:
													_UpperCamelCase :		Any			    =       {0: 'batch', 1: 'sequence'}

							return common_inputs





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return self._config.n_layer





	@property
	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							return self._config.n_head





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional[TensorType] = None			,):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       super(lowerCamelCase__			,self			).generate_dummy_inputs(
							    lowerCamelCase__			,batch_size=lowerCamelCase__			,seq_length=lowerCamelCase__			,is_pair=lowerCamelCase__			,framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							_UpperCamelCase :		Tuple			    =       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

																			_UpperCamelCase    ,      _UpperCamelCase :		str			    =       common_inputs['input_ids'].shape
																			# Not using the same length for past_key_values
																			_UpperCamelCase :		Optional[int]			    =       seqlen + 2
																			_UpperCamelCase :		List[Any]			    =       (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			_UpperCamelCase :		Optional[Any]			    =       [
																			    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																			]

							_UpperCamelCase :		Union[str, Any]			    =       common_inputs['attention_mask']
							if self.use_past:
													_UpperCamelCase :		Any			    =       ordered_inputs['attention_mask'].dtype
													_UpperCamelCase :		List[str]			    =       torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__			,lowerCamelCase__			,dtype=lowerCamelCase__			)]			,dim=1			)

							return ordered_inputs





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return 13


