


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        lowerCamelCase      (     unittest.TestCase   ):


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


	def    A(						self):
							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        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Dict        =	ImageGPTImageProcessor if is_vision_available() else None


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  ImageGPTImageProcessingTester(self)


	@property
	def    A(						self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  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    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size							,    {'''height''': 1_8, '''width''': 1_8})

							__UpperCAmelCase :       Union[str, Any]  						=  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    A(						self):
							__UpperCAmelCase :       Any  						=  self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase :       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    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase :       Dict  						=  os.path.join(lowercase__							,    '''image_processor.json''')
													image_processor_first.to_json_file(lowercase__)
													__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_json_file(lowercase__).to_dict()

							__UpperCAmelCase :       Any  						=  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    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(lowercase__)
													__UpperCAmelCase :       Dict  						=  self.image_processing_class.from_pretrained(lowercase__).to_dict()

							__UpperCAmelCase :       Optional[Any]  						=  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    A(						self):
							pass






def 						__SCREAMING_SNAKE_CASE       (					)		->    int:




						'''simple docstring'''







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

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

						__UpperCAmelCase :       int  						=  [imagea, imagea]

						return images





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


	@slow
	def    A(						self):
							__UpperCAmelCase :       int  						=  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''')

							__UpperCAmelCase :       Any  						=  prepare_images()

							# test non-batched
							__UpperCAmelCase :       int  						=  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))

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

							# test batched
							__UpperCAmelCase :       int  						=  image_processing(lowercase__							,    return_tensors='''pt''')

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

							__UpperCAmelCase :       Any  						=  [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


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		int        =	'''bert'''


	def __init__(						self							,    lowercase__=3_0_5_2_2							,    lowercase__=7_6_8							,    lowercase__=1_2							,    lowercase__=1_2							,    lowercase__=3_0_7_2							,    lowercase__="gelu"							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=5_1_2							,    lowercase__=2							,    lowercase__=0.0_2							,    lowercase__=1e-12							,    lowercase__=0							,    lowercase__="absolute"							,    lowercase__=True							,    lowercase__=None							,    **lowercase__							,    ):
							super().__init__(pad_token_id=lowercase__							,    **lowercase__)

							__UpperCAmelCase :       Tuple  						=  vocab_size
							__UpperCAmelCase :       Any  						=  hidden_size
							__UpperCAmelCase :       str  						=  num_hidden_layers
							__UpperCAmelCase :       Dict  						=  num_attention_heads
							__UpperCAmelCase :       Tuple  						=  hidden_act
							__UpperCAmelCase :       int  						=  intermediate_size
							__UpperCAmelCase :       List[Any]  						=  hidden_dropout_prob
							__UpperCAmelCase :       Union[str, Any]  						=  attention_probs_dropout_prob
							__UpperCAmelCase :       List[Any]  						=  max_position_embeddings
							__UpperCAmelCase :       Union[str, Any]  						=  type_vocab_size
							__UpperCAmelCase :       List[Any]  						=  initializer_range
							__UpperCAmelCase :       List[Any]  						=  layer_norm_eps
							__UpperCAmelCase :       List[str]  						=  position_embedding_type
							__UpperCAmelCase :       Optional[Any]  						=  use_cache
							__UpperCAmelCase :       List[Any]  						=  classifier_dropout






class        lowerCamelCase      (     _UpperCamelCase   ):


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

