



"""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    __lowercase     :







						"""simple docstring"""







						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   ,		):
											snake_case_					:		Tuple 					=					parent
											snake_case_					:		Tuple 					=					batch_size
											snake_case_					:		List[str] 					=					decoder_seq_length
											# For common tests
											snake_case_					:		List[str] 					=					self.decoder_seq_length
											snake_case_					:		List[Any] 					=					is_training
											snake_case_					:		List[Any] 					=					use_attention_mask
											snake_case_					:		Dict 					=					use_labels

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

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					ids_tensor([self.batch_size, self.decoder_seq_length]   ,		self.vocab_size       )

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

											snake_case_					:		Tuple 					=					None
											if self.use_labels:
																snake_case_					:		List[Any] 					=					ids_tensor([self.batch_size, self.decoder_seq_length]   ,		self.vocab_size       )

											snake_case_					:		int 					=					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   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		):
											snake_case_					:		Optional[int] 					=					True
											snake_case_					:		Optional[int] 					=					TrOCRDecoder(config=lowercase__       ).to(lowercase__       ).eval()
											snake_case_					:		List[str] 					=					input_ids[:2]

											input_ids[input_ids == 0] += 1
											# first forward pass
											snake_case_					:		Tuple 					=					model(lowercase__   ,		use_cache=lowercase__       )
											snake_case_					:		Optional[Any] 					=					model(lowercase__       )
											snake_case_					:		Optional[Any] 					=					model(lowercase__   ,		use_cache=lowercase__       )

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

											snake_case_					:		List[str] 					=					outputs["""past_key_values"""]

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

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

											snake_case_					:		Optional[Any] 					=					model(lowercase__       )["""last_hidden_state"""]
											snake_case_					:		List[Any] 					=					model(lowercase__   ,		past_key_values=lowercase__       )["""last_hidden_state"""]

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

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					self.prepare_config_and_inputs()
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		List[str] 					=					config_and_inputs

											snake_case_					:		Union[str, Any] 					=					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict







@require_torch
class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						_A		:							Any							       =		(TrOCRForCausalLM,) if is_torch_available() else ()
						_A		:							Optional[Any]							       =		{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
						_A		:							Optional[int]							       =		True
						_A		:							int							       =		False







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					TrOCRStandaloneDecoderModelTester(self   ,		is_training=lowercase__       )
											snake_case_					:		Tuple 					=					ConfigTester(self   ,		config_class=lowercase__       )







						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											pass







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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_decoder_model_past(*lowercase__       )







						def 						__UpperCamelCase							(self       ):
											return







						@unittest.skip("""The model doesn't support left padding"""       )  # and it's not used enough to be worth fixing :)
						def 						__UpperCamelCase							(self       ):
											pass









"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							int							       =		["""pixel_values"""]







						def __init__(self   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 0.9   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 1 / 2_55   ,		lowercase__ = True   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = None   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )
											snake_case_					:		Tuple 					=					size if size is not None else {"""shortest_edge""": 2_24}
											snake_case_					:		Union[str, Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		str 					=					crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
											snake_case_					:		Dict 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		Union[str, Any] 					=					do_resize
											snake_case_					:		List[str] 					=					size
											snake_case_					:		str 					=					crop_pct
											snake_case_					:		str 					=					resample
											snake_case_					:		Optional[Any] 					=					do_center_crop
											snake_case_					:		Dict 					=					crop_size
											snake_case_					:		int 					=					do_rescale
											snake_case_					:		Optional[int] 					=					rescale_factor
											snake_case_					:		str 					=					do_normalize
											snake_case_					:		str 					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											snake_case_					:		List[str] 					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		Tuple 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
											if crop_pct is not None:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					int(size["""shortest_edge"""] / crop_pct       )
																elif "height" in size and "width" in size:
																					if size["height"] == size["width"]:
																										snake_case_					:		Dict 					=					int(size["""height"""] / crop_pct       )
																					else:
																										snake_case_					:		List[str] 					=					(int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

																snake_case_					:		List[Any] 					=					get_resize_output_image_size(lowercase__   ,		size=lowercase__   ,		default_to_square=lowercase__       )
											else:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					get_resize_output_image_size(lowercase__   ,		size=size["""shortest_edge"""]   ,		default_to_square=lowercase__       )
																elif "height" in size and "width" in size:
																					snake_case_					:		int 					=					(size["""height"""], size["""width"""])
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

											return resize(lowercase__   ,		size=lowercase__   ,		resample=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		int 					=					get_size_dict(lowercase__       )
											if "height" not in size or "width" not in size:
																raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
											return center_crop(lowercase__   ,		size=(size["""height"""], size["""width"""])   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return rescale(lowercase__   ,		scale=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return normalize(lowercase__   ,		mean=lowercase__   ,		std=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = ChannelDimension.FIRST   ,		**lowercase__   ,		):
											snake_case_					:		str 					=					do_resize if do_resize is not None else self.do_resize
											snake_case_					:		Any 					=					crop_pct if crop_pct is not None else self.crop_pct
											snake_case_					:		List[Any] 					=					resample if resample is not None else self.resample
											snake_case_					:		str 					=					do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case_					:		str 					=					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_					:		Any 					=					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_					:		str 					=					do_normalize if do_normalize is not None else self.do_normalize
											snake_case_					:		List[Any] 					=					image_mean if image_mean is not None else self.image_mean
											snake_case_					:		int 					=					image_std if image_std is not None else self.image_std

											snake_case_					:		List[Any] 					=					size if size is not None else self.size
											snake_case_					:		Optional[Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		List[Any] 					=					crop_size if crop_size is not None else self.crop_size
											snake_case_					:		int 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		List[str] 					=					make_list_of_images(lowercase__       )

											if not valid_images(lowercase__       ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

											if do_resize and size is None or resample is None:
																raise ValueError("""Size and resample must be specified if do_resize is True."""       )

											if do_center_crop and crop_pct is None:
																raise ValueError("""Crop_pct must be specified if do_center_crop is True."""       )

											if do_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

											if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

											# All transformations expect numpy arrays.
											snake_case_					:		int 					=					[to_numpy_array(lowercase__       ) for image in images]

											if do_resize:
																snake_case_					:		str 					=					[self.resize(image=lowercase__   ,		size=lowercase__   ,		crop_pct=lowercase__   ,		resample=lowercase__       ) for image in images]

											if do_center_crop:
																snake_case_					:		Optional[int] 					=					[self.center_crop(image=lowercase__   ,		size=lowercase__       ) for image in images]

											if do_rescale:
																snake_case_					:		List[Any] 					=					[self.rescale(image=lowercase__   ,		scale=lowercase__       ) for image in images]

											if do_normalize:
																snake_case_					:		Optional[Any] 					=					[self.normalize(image=lowercase__   ,		mean=lowercase__   ,		std=lowercase__       ) for image in images]

											snake_case_					:		List[Any] 					=					[to_channel_dimension_format(lowercase__   ,		lowercase__       ) for image in images]

											snake_case_					:		Dict 					=					{"""pixel_values""": images}
											return BatchFeature(data=lowercase__   ,		tensor_type=lowercase__       )




