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 					lowercase__						:


						def __init__(       self	:				str    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Union[str, Any]=13    ,		UpperCamelCase__	:				List[Any]=7    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				Any=True    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				int=True    ,		UpperCamelCase__	:				str=99    ,		UpperCamelCase__	:				int=64    ,		UpperCamelCase__	:				Tuple=5    ,		UpperCamelCase__	:				Any=4    ,		UpperCamelCase__	:				Tuple=37    ,		UpperCamelCase__	:				List[str]="gelu"    ,		UpperCamelCase__	:				Any=0.1    ,		UpperCamelCase__	:				Optional[Any]=0.1    ,		UpperCamelCase__	:				List[str]=512    ,		UpperCamelCase__	:				Dict=16    ,		UpperCamelCase__	:				List[str]=2    ,		UpperCamelCase__	:				List[Any]=0.02    ,		UpperCamelCase__	:				Tuple=3    ,		UpperCamelCase__	:				str=4    ,		UpperCamelCase__	:				str=None    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parent
												SCREAMING_SNAKE_CASE							:    Tuple							=							batch_size
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							seq_length
												SCREAMING_SNAKE_CASE							:    List[str]							=							is_training
												SCREAMING_SNAKE_CASE							:    Dict							=							use_input_mask
												SCREAMING_SNAKE_CASE							:    str							=							use_token_type_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							use_labels
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							vocab_size
												SCREAMING_SNAKE_CASE							:    List[str]							=							hidden_size
												SCREAMING_SNAKE_CASE							:    Tuple							=							num_hidden_layers
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							num_attention_heads
												SCREAMING_SNAKE_CASE							:    str							=							intermediate_size
												SCREAMING_SNAKE_CASE							:    Dict							=							hidden_act
												SCREAMING_SNAKE_CASE							:    Tuple							=							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:    List[str]							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    Any							=							max_position_embeddings
												SCREAMING_SNAKE_CASE							:    List[str]							=							type_vocab_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							type_sequence_label_size
												SCREAMING_SNAKE_CASE							:    Tuple							=							initializer_range
												SCREAMING_SNAKE_CASE							:    int							=							num_labels
												SCREAMING_SNAKE_CASE							:    Tuple							=							num_choices
												SCREAMING_SNAKE_CASE							:    Dict							=							scope
												SCREAMING_SNAKE_CASE							:    List[Any]							=							vocab_size - 1


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							None
												if self.use_input_mask:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							random_attention_mask([self.batch_size, self.seq_length]						)

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.get_config()

												return config, input_ids, input_mask, token_labels


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												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=lowercase_    ,		initializer_range=self.initializer_range    ,		pad_token_id=self.pad_token_id    ,		)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.prepare_config_and_inputs()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							True

												return config, input_ids, input_mask, token_labels


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							GPTNeoXModel(config=lowercase_						)
												model.to(lowercase_						)
												model.eval()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model(lowercase_    ,		attention_mask=lowercase_						)
												SCREAMING_SNAKE_CASE							:    Dict							=							model(lowercase_						)
												self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Optional[int]    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							True
												SCREAMING_SNAKE_CASE							:    List[Any]							=							GPTNeoXModel(lowercase_						)
												model.to(lowercase_						)
												model.eval()
												SCREAMING_SNAKE_CASE							:    int							=							model(lowercase_    ,		attention_mask=lowercase_						)
												self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							GPTNeoXForCausalLM(config=lowercase_						)
												model.to(lowercase_						)
												model.eval()
												SCREAMING_SNAKE_CASE							:    Any							=							model(lowercase_    ,		attention_mask=lowercase_    ,		labels=lowercase_						)
												self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)						)


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				List[str]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				Optional[int]    ,		UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.num_labels
												SCREAMING_SNAKE_CASE							:    List[str]							=							GPTNeoXForQuestionAnswering(lowercase_						)
												model.to(lowercase_						)
												model.eval()
												SCREAMING_SNAKE_CASE							:    Any							=							model(lowercase_    ,		attention_mask=lowercase_						)
												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	:				int    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.num_labels
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							GPTNeoXForSequenceClassification(lowercase_						)
												model.to(lowercase_						)
												model.eval()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)
												SCREAMING_SNAKE_CASE							:    Any							=							model(lowercase_    ,		attention_mask=lowercase_    ,		labels=lowercase_						)
												self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.num_labels
												SCREAMING_SNAKE_CASE							:    Any							=							GPTNeoXForTokenClassification(lowercase_						)
												model.to(lowercase_						)
												model.eval()
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							model(lowercase_    ,		attention_mask=lowercase_    ,		labels=lowercase_						)
												self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels)						)


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Optional[int]    ,		UpperCamelCase__	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							True
												SCREAMING_SNAKE_CASE							:    List[Any]							=							GPTNeoXForCausalLM(config=lowercase_						)
												model.to(lowercase_						)
												model.eval()

												# first forward pass
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model(lowercase_    ,		attention_mask=lowercase_    ,		use_cache=lowercase_						)
												SCREAMING_SNAKE_CASE							:    Dict							=							outputs.past_key_values

												# create hypothetical multiple next token and extent to next_input_ids
												SCREAMING_SNAKE_CASE							:    Tuple							=							ids_tensor((self.batch_size, 3)    ,		config.vocab_size						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							ids_tensor((self.batch_size, 3)    ,		vocab_size=2						)

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(lowercase_    ,		attention_mask=lowercase_    ,		output_hidden_states=lowercase_						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							output_from_no_past["hidden_states"][0]
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model(
												    lowercase_    ,		attention_mask=lowercase_    ,		past_key_values=lowercase_    ,		output_hidden_states=lowercase_    ,		)["hidden_states"][0]

												# select random slice
												SCREAMING_SNAKE_CASE							:    str							=							ids_tensor((1,)    ,		output_from_past.shape[-1]						).item()
												SCREAMING_SNAKE_CASE							:    Any							=							output_from_no_past[:, -3:, random_slice_idx].detach()
												SCREAMING_SNAKE_CASE							:    List[str]							=							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(lowercase_    ,		lowercase_    ,		atol=1E-3						)						)



						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							config_and_inputs
												SCREAMING_SNAKE_CASE							:    List[str]							=							{"input_ids": input_ids, "attention_mask": input_mask}
												return config, inputs_dict






@require_torch
class 					lowercase__						(   __A	,							__A	,							__A	,							unittest.TestCase):
						UpperCamelCase_     =		(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase_     =		(GPTNeoXForCausalLM,) if is_torch_available() else ()
						UpperCamelCase_     =		(
						    {
						        "feature-extraction": GPTNeoXModel,
						        "question-answering": GPTNeoXForQuestionAnswering,
						        "text-classification": GPTNeoXForSequenceClassification,
						        "text-generation": GPTNeoXForCausalLM,
						        "token-classification": GPTNeoXForTokenClassification,
						        "zero-shot": GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase_     =		False
						UpperCamelCase_     =		False
						UpperCamelCase_     =		False
						UpperCamelCase_     =		False


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							GPTNeoXModelTester(self						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							ConfigTester(self    ,		config_class=lowercase_    ,		hidden_size=64    ,		num_attention_heads=8						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												self.config_tester.run_common_tests()


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(lowercase_    ,		lowercase_    ,		lowercase_						)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(lowercase_    ,		lowercase_    ,		lowercase_						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs_for_decoder()

												SCREAMING_SNAKE_CASE							:    Tuple							=							None

												self.model_tester.create_and_check_model_as_decoder(lowercase_    ,		lowercase_    ,		lowercase_						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_    ,		lowercase_    ,		lowercase_						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*lowercase_						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*lowercase_						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*lowercase_						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*lowercase_						)


						@unittest.skip(reason='''Feed forward chunking is not implemented'''						)
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												pass



						@parameterized.expand([('''linear''',), ('''dynamic''',)]						)
						def        __A       (       self	:				int    ,		UpperCamelCase__	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE							:    str							=							ids_tensor([1, 10]    ,		config.vocab_size						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							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
												SCREAMING_SNAKE_CASE							:    List[str]							=							GPTNeoXModel(lowercase_						)
												original_model.to(lowercase_						)
												original_model.eval()
												SCREAMING_SNAKE_CASE							:    Dict							=							original_model(lowercase_						).last_hidden_state
												SCREAMING_SNAKE_CASE							:    Dict							=							original_model(lowercase_						).last_hidden_state

												set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{"type": scaling_type, "factor": 10.0}
												SCREAMING_SNAKE_CASE							:    str							=							GPTNeoXModel(lowercase_						)
												scaled_model.to(lowercase_						)
												scaled_model.eval()
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							scaled_model(lowercase_						).last_hidden_state
												SCREAMING_SNAKE_CASE							:    Any							=							scaled_model(lowercase_						).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(lowercase_    ,		lowercase_    ,		atol=1E-5						)						)
												else:
																		self.assertFalse(torch.allclose(lowercase_    ,		lowercase_    ,		atol=1E-5						)						)

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






@require_torch
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''						)
												for checkpointing in [True, False]:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''						)

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

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

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model.generate(**lowercase_    ,		do_sample=lowercase_    ,		max_new_tokens=20						)
																		SCREAMING_SNAKE_CASE							:    str							=							tokenizer.batch_decode(lowercase_						)[0]

																		self.assertEqual(lowercase_    ,		lowercase_						)






from PIL import Image

def 			lowercase_				(			_lowerCamelCase							:       Image		,	_lowerCamelCase							:       int):
    lowercase__ :   List[str]    					=				(259 * (level + 255)) / (255 * (259 - level))

    def contrast(_lowerCamelCase							:       int) -> int:
        return int(128 + factor * (c - 128))

    return img.point(_lowerCamelCase)


if __name__ == "__main__":
      # Load image
      with Image.open('''image_data/lena.jpg''') as img:
            # Change contrast to 170
            UpperCamelCase  							=						change_contrast(img, 170)
            cont_img.save('''image_data/lena_high_contrast.png''', format='''png''')



