


import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM





@require_torch
class       A    :


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase=9_9      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=1_6      ,				lowerCAmelCase=7      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=False      ,				lowerCAmelCase=True      ,				lowerCAmelCase=2      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=4      ,				lowerCAmelCase=4      ,				lowerCAmelCase=3_0      ,				lowerCAmelCase=0      ,				lowerCAmelCase=1      ,				lowerCAmelCase=2      ,				lowerCAmelCase=None      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			decoder_seq_length
									# For common tests
									__lowercase=			self.decoder_seq_length
									__lowercase=			is_training
									__lowercase=			use_attention_mask
									__lowercase=			use_labels

									__lowercase=			vocab_size
									__lowercase=			d_model
									__lowercase=			d_model
									__lowercase=			decoder_layers
									__lowercase=			decoder_layers
									__lowercase=			decoder_ffn_dim
									__lowercase=			decoder_attention_heads
									__lowercase=			decoder_attention_heads
									__lowercase=			eos_token_id
									__lowercase=			bos_token_id
									__lowercase=			pad_token_id
									__lowercase=			decoder_start_token_id
									__lowercase=			use_cache
									__lowercase=			max_position_embeddings

									__lowercase=			None
									__lowercase=			decoder_seq_length
									__lowercase=			2
									__lowercase=			1


							def 			_A       (self					):
									__lowercase=			ids_tensor([self.batch_size, self.decoder_seq_length]      ,				self.vocab_size					)

									__lowercase=			None
									if self.use_attention_mask:
											__lowercase=			ids_tensor([self.batch_size, self.decoder_seq_length]      ,				vocab_size=2					)

									__lowercase=			None
									if self.use_labels:
											__lowercase=			ids_tensor([self.batch_size, self.decoder_seq_length]      ,				self.vocab_size					)

									__lowercase=			TrOCRConfig(
									    vocab_size=self.vocab_size      ,				d_model=self.d_model      ,				decoder_layers=self.decoder_layers      ,				decoder_ffn_dim=self.decoder_ffn_dim      ,				decoder_attention_heads=self.decoder_attention_heads      ,				eos_token_id=self.eos_token_id      ,				bos_token_id=self.bos_token_id      ,				use_cache=self.use_cache      ,				pad_token_id=self.pad_token_id      ,				decoder_start_token_id=self.decoder_start_token_id      ,				max_position_embeddings=self.max_position_embeddings      ,				)

									return (config, input_ids, attention_mask, lm_labels)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				):
									__lowercase=			True
									__lowercase=			TrOCRDecoder(config=lowerCAmelCase					).to(lowerCAmelCase					).eval()
									__lowercase=			input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									__lowercase=			model(lowerCAmelCase      ,				use_cache=lowerCAmelCase					)
									__lowercase=			model(lowerCAmelCase					)
									__lowercase=			model(lowerCAmelCase      ,				use_cache=lowerCAmelCase					)

									self.parent.assertTrue(len(lowerCAmelCase					) == len(lowerCAmelCase					)					)
									self.parent.assertTrue(len(lowerCAmelCase					) == len(lowerCAmelCase					) + 1					)

									__lowercase=			outputs['past_key_values']

									# create hypothetical next token and extent to next_input_ids
									__lowercase=			ids_tensor((2, 1)      ,				config.vocab_size - 1					) + 1

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

									__lowercase=			model(lowerCAmelCase					)['last_hidden_state']
									__lowercase=			model(lowerCAmelCase      ,				past_key_values=lowerCAmelCase					)['last_hidden_state']

									# select random slice
									__lowercase=			ids_tensor((1,)      ,				output_from_past.shape[-1]					).item()
									__lowercase=			output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									__lowercase=			output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(lowerCAmelCase      ,				lowerCAmelCase      ,				atol=1E-3					)







							def 			_A       (self					):
									__lowercase=			self.prepare_config_and_inputs()
									__lowercase,							__lowercase,							__lowercase,							__lowercase=			config_and_inputs

									__lowercase=			{'input_ids': input_ids, 'attention_mask': attention_mask}
									return config, inputs_dict





@require_torch
class       A    (   A_					,				A_					,				A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Union[str, Any]       =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
							UpperCamelCase_   :							Optional[int]       =(TrOCRForCausalLM,) if is_torch_available() else ()
							UpperCamelCase_   :							Optional[Any]       ={'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
							UpperCamelCase_   :							Tuple       =True
							UpperCamelCase_   :							List[str]       =False


							def 			_A       (self					):
									__lowercase=			TrOCRStandaloneDecoderModelTester(self      ,				is_training=lowerCAmelCase					)
									__lowercase=			ConfigTester(self      ,				config_class=lowerCAmelCase					)


							def 			_A       (self					):
									pass


							def 			_A       (self					):
									pass


							def 			_A       (self					):
									pass


							def 			_A       (self					):
									self.config_tester.run_common_tests()


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*lowerCAmelCase					)


							def 			_A       (self					):
									return







							@unittest.skip('The model doesn\'t support left padding'					)  # and it's not used enough to be worth fixing :)
							def 			_A       (self					):
									pass










import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCAmelCase           =		[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]







def        _lowerCamelCase(	lowercase__	)						->	str:

		'''simple docstring'''







		for file in FILES_TO_FIND:
				if not (transformers_path / file).exists():
						return False
		return True


if __name__ == "__main__":
	lowerCAmelCase           =		argparse.ArgumentParser()
	parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
	lowerCAmelCase           =		parser.parse_args()
	if args.check_lib:
		lowerCAmelCase           =		importlib.import_module('''transformers''')
		lowerCAmelCase           =		Path(transformers_module.__file__).parent
	else:
		lowerCAmelCase           =		Path.cwd() / '''build/lib/transformers'''
	if not test_custom_files_are_present(transformers_path):
		raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






