






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     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=13     ,						_UpperCamelCase=7     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=99     ,						_UpperCamelCase=64     ,						_UpperCamelCase=5     ,						_UpperCamelCase=4     ,						_UpperCamelCase=37     ,						_UpperCamelCase="gelu"     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=5_12     ,						_UpperCamelCase=16     ,						_UpperCamelCase=2     ,						_UpperCamelCase=0.02     ,						_UpperCamelCase=3     ,						_UpperCamelCase=4     ,						_UpperCamelCase=None     ,						):






												"""simple docstring"""
												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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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       UpperCamelCase__      (	self   ):






												"""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=_UpperCamelCase     ,						initializer_range=self.initializer_range     ,						pad_token_id=self.pad_token_id     ,						)






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						self.prepare_config_and_inputs()

												lowerCAmelCase__    						=						True

												return config, input_ids, input_mask, token_labels






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						GPTNeoXModel(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase   )
												lowerCAmelCase__    						=						model(_UpperCamelCase   )
												self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						True
												lowerCAmelCase__    						=						GPTNeoXModel(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase   )
												self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						GPTNeoXForCausalLM(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						labels=_UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.num_labels
												lowerCAmelCase__    						=						GPTNeoXForQuestionAnswering(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase   )
												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     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.num_labels
												lowerCAmelCase__    						=						GPTNeoXForSequenceClassification(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						ids_tensor([self.batch_size]     ,						self.type_sequence_label_size   )
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						labels=_UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.num_labels
												lowerCAmelCase__    						=						GPTNeoXForTokenClassification(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						labels=_UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						True
												lowerCAmelCase__    						=						GPTNeoXForCausalLM(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()

												# first forward pass
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						use_cache=_UpperCamelCase   )
												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(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						output_hidden_states=_UpperCamelCase   )
												lowerCAmelCase__    						=						output_from_no_past['hidden_states'][0]
												lowerCAmelCase__    						=						model(
												    _UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						past_key_values=_UpperCamelCase     ,						output_hidden_states=_UpperCamelCase     ,						)['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(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-3   )   )







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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     __SCREAMING_SNAKE_CASE					(   __lowercase		,					__lowercase		,					__lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_SCREAMING_SNAKE_CASE	:       List[str]   =					(GPTNeoXForCausalLM,) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE	:       Tuple   =					(
						    {
						        '''feature-extraction''': GPTNeoXModel,
						        '''question-answering''': GPTNeoXForQuestionAnswering,
						        '''text-classification''': GPTNeoXForSequenceClassification,
						        '''text-generation''': GPTNeoXForCausalLM,
						        '''token-classification''': GPTNeoXForTokenClassification,
						        '''zero-shot''': GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_SCREAMING_SNAKE_CASE	:       Dict   =					False
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					False
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					False
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					False






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						GPTNeoXModelTester(self   )
												lowerCAmelCase__    						=						ConfigTester(self     ,						config_class=_UpperCamelCase     ,						hidden_size=64     ,						num_attention_heads=8   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# 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(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase   )






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






												"""simple docstring"""
												pass







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






												"""simple docstring"""
												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(_UpperCamelCase   )
												original_model.to(_UpperCamelCase   )
												original_model.eval()
												lowerCAmelCase__    						=						original_model(_UpperCamelCase   ).last_hidden_state
												lowerCAmelCase__    						=						original_model(_UpperCamelCase   ).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(_UpperCamelCase   )
												scaled_model.to(_UpperCamelCase   )
												scaled_model.eval()
												lowerCAmelCase__    						=						scaled_model(_UpperCamelCase   ).last_hidden_state
												lowerCAmelCase__    						=						scaled_model(_UpperCamelCase   ).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(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-5   )   )
												else:
																		self.assertFalse(torch.allclose(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-5   )   )

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




@require_torch
class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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(_UpperCamelCase   )

																		lowerCAmelCase__    						=						tokenizer('My favorite food is'     ,						return_tensors='pt'   ).to(_UpperCamelCase   )
																		# 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(**_UpperCamelCase     ,						do_sample=_UpperCamelCase     ,						max_new_tokens=20   )
																		lowerCAmelCase__    						=						tokenizer.batch_decode(_UpperCamelCase   )[0]

																		self.assertEqual(_UpperCamelCase     ,						_UpperCamelCase   )













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





