import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.roberta.modeling_flax_roberta import (
					    FlaxRobertaForCausalLM,
					    FlaxRobertaForMaskedLM,
					    FlaxRobertaForMultipleChoice,
					    FlaxRobertaForQuestionAnswering,
					    FlaxRobertaForSequenceClassification,
					    FlaxRobertaForTokenClassification,
					    FlaxRobertaModel,
					)
class     lowerCAmelCase_     (       unittest.TestCase ):







							def __init__(   self      :     Tuple ,			_A      :     str ,			_A      :     List[str]=13 ,			_A      :     Optional[int]=7 ,			_A      :     List[Any]=True ,			_A      :     int=True ,			_A      :     int=True ,			_A      :     Tuple=True ,			_A      :     Tuple=99 ,			_A      :     Tuple=32 ,			_A      :     List[str]=5 ,			_A      :     Any=4 ,			_A      :     Any=37 ,			_A      :     Tuple="gelu" ,			_A      :     Optional[int]=0.1 ,			_A      :     List[str]=0.1 ,			_A      :     int=512 ,			_A      :     List[str]=16 ,			_A      :     str=2 ,			_A      :     Dict=0.02 ,			_A      :     str=4 ,			):
												_UpperCamelCase			     =     parent
												_UpperCamelCase			     =     batch_size
												_UpperCamelCase			     =     seq_length
												_UpperCamelCase			     =     is_training
												_UpperCamelCase			     =     use_attention_mask
												_UpperCamelCase			     =     use_token_type_ids
												_UpperCamelCase			     =     use_labels
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     hidden_size
												_UpperCamelCase			     =     num_hidden_layers
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     intermediate_size
												_UpperCamelCase			     =     hidden_act
												_UpperCamelCase			     =     hidden_dropout_prob
												_UpperCamelCase			     =     attention_probs_dropout_prob
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     type_vocab_size
												_UpperCamelCase			     =     type_sequence_label_size
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     num_choices







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size		)

												_UpperCamelCase			     =     None
												if self.use_attention_mask:
																	_UpperCamelCase			     =     random_attention_mask([self.batch_size, self.seq_length]		)

												_UpperCamelCase			     =     None
												if self.use_token_type_ids:
																	_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size		)

												_UpperCamelCase			     =     RobertaConfig(
												    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=_A ,			initializer_range=self.initializer_range ,			)

												return config, input_ids, token_type_ids, attention_mask







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.prepare_config_and_inputs()
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     config_and_inputs
												_UpperCamelCase			     =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     self.prepare_config_and_inputs()
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     config_and_inputs

												_UpperCamelCase			     =     True
												_UpperCamelCase			     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			vocab_size=2		)

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)




@require_flax
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     True

							UpperCAmelCase				   =     (
							    (
							        FlaxRobertaModel,
							        FlaxRobertaForCausalLM,
							        FlaxRobertaForMaskedLM,
							        FlaxRobertaForSequenceClassification,
							        FlaxRobertaForTokenClassification,
							        FlaxRobertaForMultipleChoice,
							        FlaxRobertaForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     FlaxRobertaModelTester(self		)







							@slow
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												for model_class_name in self.all_model_classes:
																	_UpperCamelCase			     =     model_class_name.from_pretrained('''roberta-base''' ,			from_pt=_A		)
																	_UpperCamelCase			     =     model(np.ones((1, 1)		)		)
																	self.assertIsNotNone(_A		)

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "trocr"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "num_attention_heads": "decoder_attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "decoder_layers",
							}







							def __init__(   self      :     List[str] ,			_A      :     Optional[Any]=5_0265 ,			_A      :     Optional[Any]=1024 ,			_A      :     Optional[Any]=12 ,			_A      :     Any=16 ,			_A      :     Any=4096 ,			_A      :     Optional[Any]="gelu" ,			_A      :     Union[str, Any]=512 ,			_A      :     Dict=0.1 ,			_A      :     List[str]=0.0 ,			_A      :     Optional[Any]=0.0 ,			_A      :     Union[str, Any]=2 ,			_A      :     Any=0.02 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     str=False ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     int=0 ,			_A      :     Any=2 ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_embedding
												_UpperCamelCase			     =     use_learned_position_embeddings
												_UpperCamelCase			     =     layernorm_embedding

												super().__init__(
												    pad_token_id=_A ,			bos_token_id=_A ,			eos_token_id=_A ,			decoder_start_token_id=_A ,			**_A ,			)
