"""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 						__A							:






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

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

       _lowerCAmelCase						:	str        =					None
       _lowerCAmelCase						:	Optional[Any]        =					decoder_seq_length
       _lowerCAmelCase						:	int        =					2
       _lowerCAmelCase						:	Union[str, Any]        =					1






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					ids_tensor([self.batch_size, self.decoder_seq_length]    ,					self.vocab_size  )

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

       _lowerCAmelCase						:	Dict        =					None
       if self.use_labels:
         _lowerCAmelCase						:	Optional[int]        =					ids_tensor([self.batch_size, self.decoder_seq_length]    ,					self.vocab_size  )

       _lowerCAmelCase						:	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    ,					a__    ,					a__    ,					a__    ,					a__    ,					):
       _lowerCAmelCase						:	int        =					True
       _lowerCAmelCase						:	List[Any]        =					TrOCRDecoder(config=a__  ).to(a__  ).eval()
       _lowerCAmelCase						:	Optional[Any]        =					input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       _lowerCAmelCase						:	Any        =					model(a__    ,					use_cache=a__  )
       _lowerCAmelCase						:	List[str]        =					model(a__  )
       _lowerCAmelCase						:	List[str]        =					model(a__    ,					use_cache=a__  )

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

       _lowerCAmelCase						:	Any        =					outputs["""past_key_values"""]

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

       # append to next input_ids and
       _lowerCAmelCase						:	Optional[int]        =					torch.cat([input_ids, next_tokens]    ,					dim=-1  )

       _lowerCAmelCase						:	Optional[Any]        =					model(a__  )["""last_hidden_state"""]
       _lowerCAmelCase						:	Union[str, Any]        =					model(a__    ,					past_key_values=a__  )["""last_hidden_state"""]

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

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					self.prepare_config_and_inputs()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					config_and_inputs

       _lowerCAmelCase						:	List[Any]        =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict







@require_torch
class 						__A							(			lowercase_   ,     lowercase_   ,     lowercase_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Any		 =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
     _UpperCamelCase							:      List[Any]		 =		(TrOCRForCausalLM,) if is_torch_available() else ()
     _UpperCamelCase							:      Dict		 =		{"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
     _UpperCamelCase							:      List[str]		 =		True
     _UpperCamelCase							:      str		 =		False






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					TrOCRStandaloneDecoderModelTester(self    ,					is_training=a__  )
       _lowerCAmelCase						:	Dict        =					ConfigTester(self    ,					config_class=a__  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       pass






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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*a__  )






     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 dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[torch.FloatTensor]		 =		None
     _UpperCamelCase							:      torch.FloatTensor		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=512    ,					a__="cls"    ,					a__=False    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					project_dim
       _lowerCAmelCase						:	List[str]        =					pooler_fn
       _lowerCAmelCase						:	Any        =					learn_encoder
       _lowerCAmelCase						:	Optional[int]        =					use_attention_mask







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		[R"pooler", R"logit_scale"]
     _UpperCamelCase							:      List[Any]		 =		[R"position_ids", R"predictions.decoder.bias"]
     _UpperCamelCase							:      List[Any]		 =		"roberta"
     _UpperCamelCase							:      Optional[int]		 =		RobertaSeriesConfig






     def __init__(		self    ,					a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:	str        =					XLMRobertaModel(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
       _lowerCAmelCase						:	List[Any]        =					getattr(a__    ,					"""has_pre_transformation"""    ,					a__  )
       if self.has_pre_transformation:
         _lowerCAmelCase						:	List[str]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
         _lowerCAmelCase						:	Optional[int]        =					nn.LayerNorm(config.hidden_size    ,					eps=config.layer_norm_eps  )
       self.post_init()






     def    __A  (		self    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					):
       _lowerCAmelCase						:	List[str]        =					return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:	Optional[int]        =					self.base_model(
           input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					position_ids=a__    ,					head_mask=a__    ,					inputs_embeds=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					output_attentions=a__    ,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,					return_dict=a__    ,					)

       if self.has_pre_transformation:
         _lowerCAmelCase						:	Optional[Any]        =					outputs["""hidden_states"""][-2]
         _lowerCAmelCase						:	Optional[Any]        =					self.pre_LN(a__  )
         _lowerCAmelCase						:	int        =					self.transformation_pre(a__  )

         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					self.transformation(outputs.last_hidden_state  )
         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)




