





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 ):

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=1_8       ,  __lowerCAmelCase=3_0       ,  __lowerCAmelCase=4_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=True       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		size if size is not None else {'height': 1_8, 'width': 1_8}
				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		min_resolution
				lowerCamelCase__								=		max_resolution
				lowerCamelCase__								=		do_resize
				lowerCamelCase__								=		size
				lowerCamelCase__								=		do_normalize





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				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    (    _a			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						ImageGPTImageProcessor if is_vision_available() else None





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ImageGPTImageProcessingTester(self							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

				lowerCamelCase__								=		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							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)

				with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCamelCase__								=		os.path.join(__lowerCAmelCase       ,  '''image_processor.json'''							)
					image_processor_first.to_json_file(__lowerCAmelCase							)
					lowerCamelCase__								=		self.image_processing_class.from_json_file(__lowerCAmelCase							).to_dict()

				lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)

				with tempfile.TemporaryDirectory() as tmpdirname:
					image_processor_first.save_pretrained(__lowerCAmelCase							)
					lowerCamelCase__								=		self.image_processing_class.from_pretrained(__lowerCAmelCase							).to_dict()

				lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





def lowerCAmelCase__()				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		load_dataset('''hf-internal-testing/fixtures_image_utils'''       ,split='''test'''							)

	lowerCamelCase__								=		Image.open(dataset[4]['''file''']							)
	lowerCamelCase__								=		Image.open(dataset[5]['''file''']							)

	lowerCamelCase__								=		[imagea, imagea]

	return images






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

			'''simple docstring'''







			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''							)

				lowerCamelCase__								=		prepare_images()

				# test non-batched
				lowerCamelCase__								=		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)							)

				lowerCamelCase__								=		[3_0_6, 1_9_1, 1_9_1]
				self.assertEqual(encoding.input_ids[0, :3].tolist()       ,  __lowerCAmelCase							)

				# test batched
				lowerCamelCase__								=		image_processing(__lowerCAmelCase       ,  return_tensors='''pt'''							)

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

				lowerCamelCase__								=		[3_0_3, 1_3, 1_3]
				self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,  __lowerCAmelCase							)







import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     SqueezeBertForMaskedLM,
     SqueezeBertForMultipleChoice,
     SqueezeBertForQuestionAnswering,
     SqueezeBertForSequenceClassification,
     SqueezeBertForTokenClassification,
     SqueezeBertModel,
 )






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase=13,      lowerCamelCase=7,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=False,      lowerCamelCase=True,      lowerCamelCase=99,      lowerCamelCase=32,      lowerCamelCase=5,      lowerCamelCase=4,      lowerCamelCase=64,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=5_12,      lowerCamelCase=16,      lowerCamelCase=2,      lowerCamelCase=0.0_2,      lowerCamelCase=3,      lowerCamelCase=4,      lowerCamelCase=None,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=4,      lowerCamelCase=1,      )       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		parent
           _lowercase       :							Optional[Any]      		=		batch_size
           _lowercase       :							Any      		=		seq_length
           _lowercase       :							Optional[Any]      		=		is_training
           _lowercase       :							Optional[Any]      		=		use_input_mask
           _lowercase       :							List[Any]      		=		use_token_type_ids
           _lowercase       :							List[str]      		=		use_labels
           _lowercase       :							str      		=		vocab_size
           _lowercase       :							List[str]      		=		hidden_size
           _lowercase       :							Dict      		=		num_hidden_layers
           _lowercase       :							List[str]      		=		num_attention_heads
           _lowercase       :							int      		=		intermediate_size
           _lowercase       :							Union[str, Any]      		=		hidden_act
           _lowercase       :							int      		=		hidden_dropout_prob
           _lowercase       :							List[Any]      		=		attention_probs_dropout_prob
           _lowercase       :							Dict      		=		max_position_embeddings
           _lowercase       :							Union[str, Any]      		=		type_vocab_size
           _lowercase       :							List[Any]      		=		type_sequence_label_size
           _lowercase       :							Any      		=		initializer_range
           _lowercase       :							List[str]      		=		num_labels
           _lowercase       :							Any      		=		num_choices
           _lowercase       :							Tuple      		=		scope
           _lowercase       :							Optional[Any]      		=		q_groups
           _lowercase       :							List[str]      		=		k_groups
           _lowercase       :							Optional[int]      		=		v_groups
           _lowercase       :							List[str]      		=		post_attention_groups
           _lowercase       :							Union[str, Any]      		=		intermediate_groups
           _lowercase       :							int      		=		output_groups
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		ids_tensor([self.batch_size, self.seq_length],      self.vocab_size)

           _lowercase       :							Any      		=		None
           if self.use_input_mask:
               _lowercase       :							Tuple      		=		random_attention_mask([self.batch_size, self.seq_length])

           _lowercase       :							Dict      		=		None
           _lowercase       :							int      		=		None
           _lowercase       :							List[Any]      		=		None
           if self.use_labels:
               _lowercase       :							List[Any]      		=		ids_tensor([self.batch_size],      self.type_sequence_label_size)
               _lowercase       :							int      		=		ids_tensor([self.batch_size, self.seq_length],      self.num_labels)
               _lowercase       :							Dict      		=		ids_tensor([self.batch_size],      self.num_choices)

           _lowercase       :							Optional[Any]      		=		self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           return SqueezeBertConfig(
               embedding_size=self.hidden_size,      vocab_size=self.vocab_size,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      intermediate_size=self.intermediate_size,      hidden_act=self.hidden_act,      attention_probs_dropout_prob=self.hidden_dropout_prob,      attention_dropout=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      initializer_range=self.initializer_range,      q_groups=self.q_groups,      k_groups=self.k_groups,      v_groups=self.v_groups,      post_attention_groups=self.post_attention_groups,      intermediate_groups=self.intermediate_groups,      output_groups=self.output_groups,      )
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		SqueezeBertModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Dict      		=		model(lowerCamelCase,      lowerCamelCase)
           _lowercase       :							Any      		=		model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		SqueezeBertForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[Any]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.vocab_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		SqueezeBertForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							List[Any]      		=		model(
               lowerCamelCase,      attention_mask=lowerCamelCase,      start_positions=lowerCamelCase,      end_positions=lowerCamelCase)
           self.parent.assertEqual(result.start_logits.shape,      (self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape,      (self.batch_size, self.seq_length))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.num_labels
           _lowercase       :							int      		=		SqueezeBertForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Any      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.num_labels
           _lowercase       :							List[str]      		=		SqueezeBertForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase       :							Union[str, Any]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.num_labels))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		self.num_choices
           _lowercase       :							str      		=		SqueezeBertForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Dict      		=		input_ids.unsqueeze(1).expand(-1,      self.num_choices,      -1).contiguous()
           _lowercase       :							int      		=		input_mask.unsqueeze(1).expand(-1,      self.num_choices,      -1).contiguous()
           _lowercase       :							Optional[Any]      		=		model(
               lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase,      )
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_choices))




       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.prepare_config_and_inputs()
           ((_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase))       :							Dict      		=		config_and_inputs
           _lowercase       :							Tuple      		=		{'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	Union[str, Any]     =			(
           (
               SqueezeBertModel,
               SqueezeBertForMaskedLM,
               SqueezeBertForMultipleChoice,
               SqueezeBertForQuestionAnswering,
               SqueezeBertForSequenceClassification,
               SqueezeBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       lowercase_ :	Optional[int]     =			(
           {
               """feature-extraction""": SqueezeBertModel,
               """fill-mask""": SqueezeBertForMaskedLM,
               """question-answering""": SqueezeBertForQuestionAnswering,
               """text-classification""": SqueezeBertForSequenceClassification,
               """token-classification""": SqueezeBertForTokenClassification,
               """zero-shot""": SqueezeBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowercase_ :	Tuple     =			False
       lowercase_ :	List[str]     =			True
       lowercase_ :	int     =			False
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							str      		=		SqueezeBertModelTester(self)
           _lowercase       :							Dict      		=		ConfigTester(self,      config_class=lowerCamelCase,      dim=37)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase)




       @slow
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase       :							List[Any]      		=		SqueezeBertModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_sentencepiece
@require_tokenizers
@require_torch
class _lowerCamelCase(						unittest.TestCase   ):
       @slow
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli')

           _lowercase       :							Optional[int]      		=		torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]])
           _lowercase       :							List[str]      		=		model(lowerCamelCase)[0]
           _lowercase       :							Union[str, Any]      		=		torch.Size((1, 3))
           self.assertEqual(output.shape,      lowerCamelCase)
           _lowercase       :							Tuple      		=		torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]])
           self.assertTrue(torch.allclose(lowerCamelCase,      lowerCamelCase,      atol=1E-4))


