import time
import unittest

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

from ..test_modeling_common import ids_tensor


if is_torch_available():
					import torch

					from transformers.generation import (
					    MaxLengthCriteria,
					    MaxNewTokensCriteria,
					    MaxTimeCriteria,
					    StoppingCriteriaList,
					    validate_stopping_criteria,
					)
@require_torch
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     str		):
												_UpperCamelCase			     =     3
												_UpperCamelCase			     =     250

												_UpperCamelCase			     =     ids_tensor((batch_size, length) ,			_A		)
												_UpperCamelCase			     =     torch.ones((batch_size, length) ,			device=_A ,			dtype=torch.float		) / length
												return input_ids, scores







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)

												_UpperCamelCase			     =     StoppingCriteriaList(
												    [
												        MaxLengthCriteria(max_length=10		),
												        MaxTimeCriteria(max_time=0.1		),
												    ]		)

												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(9		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(10		)
												self.assertTrue(criteria(_A ,			_A		)		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     MaxLengthCriteria(max_length=10		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(9		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(10		)
												self.assertTrue(criteria(_A ,			_A		)		)







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     MaxNewTokensCriteria(start_length=5 ,			max_new_tokens=5		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(9		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(10		)
												self.assertTrue(criteria(_A ,			_A		)		)

												_UpperCamelCase			     =     StoppingCriteriaList([criteria]		)
												self.assertEqual(criteria_list.max_length ,			10		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)

												_UpperCamelCase			     =     MaxTimeCriteria(max_time=0.1		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase			     =     MaxTimeCriteria(max_time=0.1 ,			initial_timestamp=time.time() - 0.2		)
												self.assertTrue(criteria(_A ,			_A		)		)







							def  UpperCamelCase_						(   self      :     Any		):
												validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10		)]		) ,			10		)

												with self.assertWarns(_A		):
																	validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10		)]		) ,			11		)

												_UpperCamelCase			     =     validate_stopping_criteria(StoppingCriteriaList() ,			11		)

												self.assertEqual(len(_A		) ,			1		)

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 ,			)
