



'''simple docstring'''




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      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase=99	,				lowercase=13	,				lowercase=16	,				lowercase=7	,				lowercase=True	,				lowercase=True	,				lowercase=True	,				lowercase=False	,				lowercase=True	,				lowercase=2	,				lowercase=32	,				lowercase=4	,				lowercase=4	,				lowercase=30	,				lowercase=0	,				lowercase=1	,				lowercase=2	,				lowercase=None	,				):
				A_     :						Tuple     =					parent
				A_     :						str     =					batch_size
				A_     :						int     =					decoder_seq_length
				# For common tests
				A_     :						int     =					self.decoder_seq_length
				A_     :						List[str]     =					is_training
				A_     :						str     =					use_attention_mask
				A_     :						Union[str, Any]     =					use_labels

				A_     :						List[Any]     =					vocab_size
				A_     :						List[Any]     =					d_model
				A_     :						int     =					d_model
				A_     :						str     =					decoder_layers
				A_     :						Optional[Any]     =					decoder_layers
				A_     :						List[Any]     =					decoder_ffn_dim
				A_     :						Union[str, Any]     =					decoder_attention_heads
				A_     :						List[str]     =					decoder_attention_heads
				A_     :						Optional[Any]     =					eos_token_id
				A_     :						int     =					bos_token_id
				A_     :						Dict     =					pad_token_id
				A_     :						Dict     =					decoder_start_token_id
				A_     :						Optional[int]     =					use_cache
				A_     :						Any     =					max_position_embeddings

				A_     :						str     =					None
				A_     :						Union[str, Any]     =					decoder_seq_length
				A_     :						str     =					2
				A_     :						Dict     =					1


	def    _a     (self							):
				A_     :						Any     =					ids_tensor([self.batch_size, self.decoder_seq_length]	,				self.vocab_size							)

				A_     :						Dict     =					None
				if self.use_attention_mask:
							A_     :						Optional[Any]     =					ids_tensor([self.batch_size, self.decoder_seq_length]	,				vocab_size=2							)

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

				A_     :						Any     =					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	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				):
				A_     :						List[str]     =					True
				A_     :						Optional[int]     =					TrOCRDecoder(config=lowercase							).to(lowercase							).eval()
				A_     :						Union[str, Any]     =					input_ids[:2]

				input_ids[input_ids == 0] += 1
				# first forward pass
				A_     :						Any     =					model(lowercase	,				use_cache=lowercase							)
				A_     :						Any     =					model(lowercase							)
				A_     :						Dict     =					model(lowercase	,				use_cache=lowercase							)

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

				A_     :						Union[str, Any]     =					outputs["""past_key_values"""]

				# create hypothetical next token and extent to next_input_ids
				A_     :						Union[str, Any]     =					ids_tensor((2, 1)	,				config.vocab_size - 1							) + 1

				# append to next input_ids and
				A_     :						List[str]     =					torch.cat([input_ids, next_tokens]	,				dim=-1							)

				A_     :						Optional[Any]     =					model(lowercase							)["""last_hidden_state"""]
				A_     :						str     =					model(lowercase	,				past_key_values=lowercase							)["""last_hidden_state"""]

				# select random slice
				A_     :						Union[str, Any]     =					ids_tensor((1,)	,				output_from_past.shape[-1]							).item()
				A_     :						Dict     =					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
				A_     :						Dict     =					output_from_past[:, 0, random_slice_idx].detach()

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


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.prepare_config_and_inputs()
				A_,   A_,   A_,   A_     :						Tuple     =					config_and_inputs

				A_     :						Dict     =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_torch
class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Dict		      = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE       :		Tuple		      = (TrOCRForCausalLM,) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = True
	__SCREAMING_SNAKE_CASE       :		Tuple		      = False


	def    _a     (self							):
				A_     :						List[Any]     =					TrOCRStandaloneDecoderModelTester(self	,				is_training=lowercase							)
				A_     :						int     =					ConfigTester(self	,				config_class=lowercase							)


	def    _a     (self							):
				pass


	def    _a     (self							):
				pass


	def    _a     (self							):
				pass


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


	def    _a     (self							):
				A_     :						List[str]     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past(*lowercase							)


	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




'''simple docstring'''




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:Dict       = {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:str       = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						lowerCamelCase							:Tuple       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)