
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        lowercase      :



		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       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  decoder_seq_length
				# For common tests
				UpperCamelCase						=  self.decoder_seq_length
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_attention_mask
				UpperCamelCase						=  use_labels

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

				UpperCamelCase						=  None
				UpperCamelCase						=  decoder_seq_length
				UpperCamelCase						=  2
				UpperCamelCase						=  1



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  ids_tensor([self.batch_size, self.decoder_seq_length]       ,   self.vocab_size )

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

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

				UpperCamelCase						=  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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  True
				UpperCamelCase						=  TrOCRDecoder(config=A_ ).to(A_ ).eval()
				UpperCamelCase						=  input_ids[:2]

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

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

				UpperCamelCase						=  outputs['past_key_values']

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

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

				UpperCamelCase						=  model(A_ )['last_hidden_state']
				UpperCamelCase						=  model(A_       ,   past_key_values=A_ )['last_hidden_state']

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

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



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  config_and_inputs

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






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



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  TrOCRStandaloneDecoderModelTester(self       ,   is_training=A_ )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=A_ )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past(*A_ )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				return



		@unittest.skip('The model doesn\'t support left padding' )  # and it's not used enough to be worth fixing :)
		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				pass


import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset


@pytest.mark.parametrize('dataset_size'					, [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('input_in_memory_max_size'					, ['default', 0, 100 * 2**20, 900 * 2**20]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if input_in_memory_max_size != "default":
				monkeypatch.setattr(datasets.config					, 'IN_MEMORY_MAX_SIZE'					, lowercase				)
		UpperCamelCase						=  datasets.config.IN_MEMORY_MAX_SIZE
		if input_in_memory_max_size == "default":
				assert in_memory_max_size == 0
		else:
				assert in_memory_max_size == input_in_memory_max_size
		if dataset_size and in_memory_max_size:
				UpperCamelCase						=  dataset_size < in_memory_max_size
		else:
				UpperCamelCase						=  False
		UpperCamelCase						=  is_small_dataset(lowercase				)
		assert result == expected
