



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       a							(			unittest.TestCase				):



		def __init__(     self     :List[str]   ,__lowercase     :int   ,__lowercase     :Optional[int]=7   ,__lowercase     :Union[str, Any]=3   ,__lowercase     :str=1_8   ,__lowercase     :str=3_0   ,__lowercase     :Optional[int]=4_0_0   ,__lowercase     :List[str]=True   ,__lowercase     :Optional[Any]=None   ,__lowercase     :Optional[int]=True   ,):
						snake_case__	:      Dict     		=	size if size is not None else {'''height''': 1_8, '''width''': 1_8}
						snake_case__	:      Optional[int]     		=	parent
						snake_case__	:      Optional[int]     		=	batch_size
						snake_case__	:      Any     		=	num_channels
						snake_case__	:      Any     		=	image_size
						snake_case__	:      Union[str, Any]     		=	min_resolution
						snake_case__	:      Tuple     		=	max_resolution
						snake_case__	:      List[str]     		=	do_resize
						snake_case__	:      Union[str, Any]     		=	size
						snake_case__	:      List[str]     		=	do_normalize






		def 	__lowerCamelCase			(     self     :Tuple  ):
						return {
						    # here we create 2 clusters for the sake of simplicity
						    "clusters": np.asarray(
						        [
						            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
						            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
						        ]  ),
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						}


@require_torch
@require_vision
class       a							(			__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Tuple											=					ImageGPTImageProcessor if is_vision_available() else None



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Any     		=	ImageGPTImageProcessingTester(self  )



		@property
		def 	__lowerCamelCase			(     self     :List[str]  ):
						return self.image_processor_tester.prepare_image_processor_dict()



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      str     		=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(__lowercase   ,'''clusters'''  )  )
						self.assertTrue(hasattr(__lowercase   ,'''do_resize'''  )  )
						self.assertTrue(hasattr(__lowercase   ,'''size'''  )  )
						self.assertTrue(hasattr(__lowercase   ,'''do_normalize'''  )  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      Dict     		=	self.image_processing_class.from_dict(self.image_processor_dict  )
						self.assertEqual(image_processor.size   ,{'''height''': 1_8, '''width''': 1_8}  )

						snake_case__	:      Optional[int]     		=	self.image_processing_class.from_dict(self.image_processor_dict   ,size=4_2  )
						self.assertEqual(image_processor.size   ,{'''height''': 4_2, '''width''': 4_2}  )



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      int     		=	self.image_processing_class(**self.image_processor_dict  )
						snake_case__	:      Union[str, Any]     		=	json.loads(image_processor.to_json_string()  )
						for key, value in self.image_processor_dict.items():
										if key == "clusters":
														self.assertTrue(np.array_equal(__lowercase   ,obj[key]  )  )
										else:
														self.assertEqual(obj[key]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      Union[str, Any]     		=	self.image_processing_class(**self.image_processor_dict  )

						with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__	:      List[str]     		=	os.path.join(__lowercase   ,'''image_processor.json'''  )
										image_processor_first.to_json_file(__lowercase  )
										snake_case__	:      str     		=	self.image_processing_class.from_json_file(__lowercase  ).to_dict()

						snake_case__	:      List[str]     		=	image_processor_first.to_dict()
						for key, value in image_processor_first.items():
										if key == "clusters":
														self.assertTrue(np.array_equal(__lowercase   ,image_processor_second[key]  )  )
										else:
														self.assertEqual(image_processor_first[key]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      str     		=	self.image_processing_class(**self.image_processor_dict  )

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

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






		@unittest.skip('''ImageGPT requires clusters at initialization'''  )
		def 	__lowerCamelCase			(     self     :List[Any]  ):
						pass



def 			_lowerCAmelCase       (					)   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      Dict     		=	load_dataset('''hf-internal-testing/fixtures_image_utils'''  ,  split='''test'''      )

				snake_case__	:      int     		=	Image.open(dataset[4]['''file''']      )
				snake_case__	:      Dict     		=	Image.open(dataset[5]['''file''']      )

				snake_case__	:      Union[str, Any]     		=	[imagea, imagea]

				return images





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



		@slow
		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Optional[Any]     		=	ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''  )

						snake_case__	:      int     		=	prepare_images()

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

						self.assertIsInstance(encoding.input_ids   ,torch.LongTensor  )
						self.assertEqual(encoding.input_ids.shape   ,(1, 1_0_2_4)  )

						snake_case__	:      int     		=	[3_0_6, 1_9_1, 1_9_1]
						self.assertEqual(encoding.input_ids[0, :3].tolist()   ,__lowercase  )

						# test batched
						snake_case__	:      Tuple     		=	image_processing(__lowercase   ,return_tensors='''pt'''  )

						self.assertIsInstance(encoding.input_ids   ,torch.LongTensor  )
						self.assertEqual(encoding.input_ids.shape   ,(2, 1_0_2_4)  )

						snake_case__	:      List[str]     		=	[3_0_3, 1_3, 1_3]
						self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,__lowercase  )






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__								=						logging.get_logger(__name__)

A__								=						{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    str											=					"""distilbert"""
		__lowerCAmelCase	:    str											=					{
		    """hidden_size""": """dim""",
		    """num_attention_heads""": """n_heads""",
		    """num_hidden_layers""": """n_layers""",
		}



		def __init__(     self     :Dict   ,__lowercase     :Tuple=3_0_5_2_2   ,__lowercase     :Optional[Any]=5_1_2   ,__lowercase     :List[str]=False   ,__lowercase     :List[str]=6   ,__lowercase     :Optional[Any]=1_2   ,__lowercase     :Tuple=7_6_8   ,__lowercase     :int=4 * 7_6_8   ,__lowercase     :List[Any]=0.1   ,__lowercase     :List[str]=0.1   ,__lowercase     :Union[str, Any]="gelu"   ,__lowercase     :List[str]=0.02   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :Dict=0.2   ,__lowercase     :Union[str, Any]=0   ,**__lowercase     :Optional[Any]   ,):
						snake_case__	:      List[str]     		=	vocab_size
						snake_case__	:      Tuple     		=	max_position_embeddings
						snake_case__	:      Optional[int]     		=	sinusoidal_pos_embds
						snake_case__	:      str     		=	n_layers
						snake_case__	:      List[Any]     		=	n_heads
						snake_case__	:      Tuple     		=	dim
						snake_case__	:      str     		=	hidden_dim
						snake_case__	:      int     		=	dropout
						snake_case__	:      Dict     		=	attention_dropout
						snake_case__	:      Tuple     		=	activation
						snake_case__	:      int     		=	initializer_range
						snake_case__	:      Optional[Any]     		=	qa_dropout
						snake_case__	:      Union[str, Any]     		=	seq_classif_dropout
						super().__init__(**__lowercase   ,pad_token_id=__lowercase  )


class       a							(			__lowerCamelCase				):



		@property
		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						if self.task == "multiple-choice":
										snake_case__	:      str     		=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
										snake_case__	:      Optional[int]     		=	{0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						    ]  )

