
import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
					    GPTNeoXForCausalLM,
					    GPTNeoXForQuestionAnswering,
					    GPTNeoXForSequenceClassification,
					    GPTNeoXForTokenClassification,
					    GPTNeoXModel,
					)

class    lowerCAmelCase__     :






		def __init__(		self		:							str							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							str=13							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=7							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Tuple=99							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=64							,			SCREAMING_SNAKE_CASE__		:							Tuple=5							,			SCREAMING_SNAKE_CASE__		:							Tuple=4							,			SCREAMING_SNAKE_CASE__		:							Dict=37							,			SCREAMING_SNAKE_CASE__		:							List[str]="gelu"							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=0.1							,			SCREAMING_SNAKE_CASE__		:							List[str]=0.1							,			SCREAMING_SNAKE_CASE__		:							List[Any]=5_12							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=16							,			SCREAMING_SNAKE_CASE__		:							Any=2							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=0.02							,			SCREAMING_SNAKE_CASE__		:							List[Any]=3							,			SCREAMING_SNAKE_CASE__		:							Dict=4							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=None							,			)     ->							Optional[int]:
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	seq_length
						__lowerCamelCase    					=	is_training
						__lowerCamelCase    					=	use_input_mask
						__lowerCamelCase    					=	use_token_type_ids
						__lowerCamelCase    					=	use_labels
						__lowerCamelCase    					=	vocab_size
						__lowerCamelCase    					=	hidden_size
						__lowerCamelCase    					=	num_hidden_layers
						__lowerCamelCase    					=	num_attention_heads
						__lowerCamelCase    					=	intermediate_size
						__lowerCamelCase    					=	hidden_act
						__lowerCamelCase    					=	hidden_dropout_prob
						__lowerCamelCase    					=	attention_probs_dropout_prob
						__lowerCamelCase    					=	max_position_embeddings
						__lowerCamelCase    					=	type_vocab_size
						__lowerCamelCase    					=	type_sequence_label_size
						__lowerCamelCase    					=	initializer_range
						__lowerCamelCase    					=	num_labels
						__lowerCamelCase    					=	num_choices
						__lowerCamelCase    					=	scope
						__lowerCamelCase    					=	vocab_size - 1






		def  __A       (		self		:							Any  )     ->							int:
						__lowerCamelCase    					=	ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size  )

						__lowerCamelCase    					=	None
						if self.use_input_mask:
										__lowerCamelCase    					=	random_attention_mask([self.batch_size, self.seq_length]  )

						__lowerCamelCase    					=	None
						if self.use_labels:
										__lowerCamelCase    					=	ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels  )

						__lowerCamelCase    					=	self.get_config()

						return config, input_ids, input_mask, token_labels






		def  __A       (		self		:							Optional[Any]  )     ->							List[str]:
						return GPTNeoXConfig(
						    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							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			type_vocab_size=self.type_vocab_size							,			is_decoder=SCREAMING_SNAKE_CASE__							,			initializer_range=self.initializer_range							,			pad_token_id=self.pad_token_id							,			)






		def  __A       (		self		:							Union[str, Any]  )     ->							int:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.prepare_config_and_inputs()

						__lowerCamelCase    					=	True

						return config, input_ids, input_mask, token_labels






		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							int:
						__lowerCamelCase    					=	GPTNeoXModel(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)  )






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							str:
						__lowerCamelCase    					=	True
						__lowerCamelCase    					=	GPTNeoXModel(SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)  )






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]  )     ->							int:
						__lowerCamelCase    					=	GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			labels=SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)  )






		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]  )     ->							Dict:
						__lowerCamelCase    					=	self.num_labels
						__lowerCamelCase    					=	GPTNeoXForQuestionAnswering(SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__  )
						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  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]  )     ->							Optional[int]:
						__lowerCamelCase    					=	self.num_labels
						__lowerCamelCase    					=	GPTNeoXForSequenceClassification(SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	ids_tensor([self.batch_size]							,			self.type_sequence_label_size  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			labels=SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							int:
						__lowerCamelCase    					=	self.num_labels
						__lowerCamelCase    					=	GPTNeoXForTokenClassification(SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			labels=SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)  )






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							Tuple:
						__lowerCamelCase    					=	True
						__lowerCamelCase    					=	GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()

						# first forward pass
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			use_cache=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						__lowerCamelCase    					=	ids_tensor((self.batch_size, 3)							,			config.vocab_size  )
						__lowerCamelCase    					=	ids_tensor((self.batch_size, 3)							,			vocab_size=2  )

						# append to next input_ids and
						__lowerCamelCase    					=	torch.cat([input_ids, next_tokens]							,			dim=-1  )
						__lowerCamelCase    					=	torch.cat([input_mask, next_mask]							,			dim=-1  )

						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			output_hidden_states=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	output_from_no_past['''hidden_states'''][0]
						__lowerCamelCase    					=	model(
						    SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			past_key_values=SCREAMING_SNAKE_CASE__							,			output_hidden_states=SCREAMING_SNAKE_CASE__							,			)['''hidden_states'''][0]

						# select random slice
						__lowerCamelCase    					=	ids_tensor((1,)							,			output_from_past.shape[-1]  ).item()
						__lowerCamelCase    					=	output_from_no_past[:, -3:, random_slice_idx].detach()
						__lowerCamelCase    					=	output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			atol=1e-3  )  )







		def  __A       (		self		:							Optional[Any]  )     ->							Tuple:
						__lowerCamelCase    					=	self.prepare_config_and_inputs()
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	config_and_inputs
						__lowerCamelCase    					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
						return config, inputs_dict





@require_torch
class    lowerCAmelCase__     (	__lowercase  ,	__lowercase  ,	__lowercase  ,	unittest.TestCase			):
		a__       :						Optional[Any]        =		(
		    (
		        GPTNeoXModel,
		        GPTNeoXForCausalLM,
		        GPTNeoXForQuestionAnswering,
		        GPTNeoXForSequenceClassification,
		        GPTNeoXForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		a__       :						List[Any]        =		(GPTNeoXForCausalLM,) if is_torch_available() else ()
		a__       :						Dict        =		(
		    {
		        """feature-extraction""": GPTNeoXModel,
		        """question-answering""": GPTNeoXForQuestionAnswering,
		        """text-classification""": GPTNeoXForSequenceClassification,
		        """text-generation""": GPTNeoXForCausalLM,
		        """token-classification""": GPTNeoXForTokenClassification,
		        """zero-shot""": GPTNeoXForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		a__       :						Dict        =		False
		a__       :						List[Any]        =		False
		a__       :						int        =		False
		a__       :						Union[str, Any]        =		False






		def  __A       (		self		:							Dict  )     ->							Optional[int]:
						__lowerCamelCase    					=	GPTNeoXModelTester(self  )
						__lowerCamelCase    					=	ConfigTester(self							,			config_class=SCREAMING_SNAKE_CASE__							,			hidden_size=64							,			num_attention_heads=8  )






		def  __A       (		self		:							Dict  )     ->							Optional[Any]:
						self.config_tester.run_common_tests()






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[str]  )     ->							Any:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[Any]  )     ->							Union[str, Any]:
						# This regression test was failing with PyTorch < 1.3
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_decoder()

						__lowerCamelCase    					=	None

						self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Dict  )     ->							Dict:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							int  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							str  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Dict  )     ->							str:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__  )






		@unittest.skip(reason='''Feed forward chunking is not implemented'''  )
		def  __A       (		self		:							Optional[int]  )     ->							int:
						pass







		@parameterized.expand([('''linear''',), ('''dynamic''',)]  )
		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]  )     ->							int:
						__lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCamelCase    					=	ids_tensor([1, 10]							,			config.vocab_size  )
						__lowerCamelCase    					=	ids_tensor([1, int(config.max_position_embeddings * 1.5  )]							,			config.vocab_size  )

						set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
						__lowerCamelCase    					=	GPTNeoXModel(SCREAMING_SNAKE_CASE__  )
						original_model.to(SCREAMING_SNAKE_CASE__  )
						original_model.eval()
						__lowerCamelCase    					=	original_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state
						__lowerCamelCase    					=	original_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state

						set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
						__lowerCamelCase    					=	{'''type''': scaling_type, '''factor''': 10.0}
						__lowerCamelCase    					=	GPTNeoXModel(SCREAMING_SNAKE_CASE__  )
						scaled_model.to(SCREAMING_SNAKE_CASE__  )
						scaled_model.eval()
						__lowerCamelCase    					=	scaled_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state
						__lowerCamelCase    					=	scaled_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state

						# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
						# maximum sequence length, so the outputs for the short input should match.
						if scaling_type == "dynamic":
										self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			atol=1e-5  )  )
						else:
										self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			atol=1e-5  )  )

						# The output should be different for long inputs
						self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			atol=1e-5  )  )





@require_torch
class    lowerCAmelCase__     (	unittest.TestCase			):






		@slow
		def  __A       (		self		:							Any  )     ->							str:
						__lowerCamelCase    					=	AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )
						for checkpointing in [True, False]:
										__lowerCamelCase    					=	GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )

										if checkpointing:
														model.gradient_checkpointing_enable()
										else:
														model.gradient_checkpointing_disable()
										model.to(SCREAMING_SNAKE_CASE__  )

										__lowerCamelCase    					=	tokenizer('''My favorite food is'''							,			return_tensors='''pt'''  ).to(SCREAMING_SNAKE_CASE__  )
										# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
										# See: https://github.com/huggingface/transformers/pull/24193
										__lowerCamelCase    					=	'''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

										__lowerCamelCase    					=	model.generate(**SCREAMING_SNAKE_CASE__							,			do_sample=SCREAMING_SNAKE_CASE__							,			max_new_tokens=20  )
										__lowerCamelCase    					=	tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )[0]

										self.assertEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )





import unittest

import numpy as np
import requests

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					SCREAMING_SNAKE_CASE__   :			str												=    False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]=7							,			SCREAMING_SNAKE_CASE__		:							List[str]=3							,			SCREAMING_SNAKE_CASE__		:							Tuple=18							,			SCREAMING_SNAKE_CASE__		:							int=30							,			SCREAMING_SNAKE_CASE__		:							Any=4_00							,			SCREAMING_SNAKE_CASE__		:							Any=None							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			)     ->							Union[str, Any]:
						__lowerCamelCase    					=	size if size is not None else {'''height''': 20, '''width''': 20}
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	image_size
						__lowerCamelCase    					=	min_resolution
						__lowerCamelCase    					=	max_resolution
						__lowerCamelCase    					=	size
						__lowerCamelCase    					=	do_normalize
						__lowerCamelCase    					=	do_convert_rgb
						__lowerCamelCase    					=	[5_12, 10_24, 20_48, 40_96]
						__lowerCamelCase    					=	patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}






		def  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







		def  __A       (		self		:							int  )     ->							Any:
						__lowerCamelCase    					=	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
						__lowerCamelCase    					=	Image.open(requests.get(SCREAMING_SNAKE_CASE__							,			stream=SCREAMING_SNAKE_CASE__  ).raw  ).convert('''RGB'''  )
						return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Dict        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self  )






		@property
		def  __A       (		self		:							Optional[int]  )     ->							Optional[int]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )






		def  __A       (		self		:							int  )     ->							str:
						__lowerCamelCase    					=	self.image_processor_tester.prepare_dummy_image()

						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						__lowerCamelCase    					=	20_48

						__lowerCamelCase    					=	image_processor(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  )
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,			torch.tensor(0.0606  )							,			atol=1e-3							,			rtol=1e-3  )  )






		def  __A       (		self		:							Union[str, Any]  )     ->							Dict:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			Image.Image  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							Dict  )     ->							str:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			Image.Image  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						__lowerCamelCase    					=	True

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
														__lowerCamelCase    					=	image_processor(
														    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches

										__lowerCamelCase    					=	'''Hello'''

										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							List[str]  )     ->							Any:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			numpify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			np.ndarray  )

						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)







		def  __A       (		self		:							Tuple  )     ->							List[str]:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			torchify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			torch.Tensor  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Any        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self							,			num_channels=4  )
						__lowerCamelCase    					=	3






		@property
		def  __A       (		self		:							List[Any]  )     ->							Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )







		def  __A       (		self		:							Optional[int]  )     ->							str:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			Image.Image  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)



