






"""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 			UpperCamelCase_       :





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

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

				UpperCAmelCase_     : str			     =					None
				UpperCAmelCase_     : int			     =					decoder_seq_length
				UpperCAmelCase_     : Tuple			     =					2
				UpperCAmelCase_     : Tuple			     =					1





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : Dict			     =					ids_tensor([self.batch_size, self.decoder_seq_length]	,	self.vocab_size						)

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

				UpperCAmelCase_     : Union[str, Any]			     =					None
				if self.use_labels:
							UpperCAmelCase_     : Any			     =					ids_tensor([self.batch_size, self.decoder_seq_length]	,	self.vocab_size						)

				UpperCAmelCase_     : 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      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: int	,	)						->			Tuple:
				UpperCAmelCase_     : Union[str, Any]			     =					True
				UpperCAmelCase_     : Tuple			     =					TrOCRDecoder(config=lowerCAmelCase_						).to(lowerCAmelCase_						).eval()
				UpperCAmelCase_     : List[str]			     =					input_ids[:2]

				input_ids[input_ids == 0] += 1
				# first forward pass
				UpperCAmelCase_     : Union[str, Any]			     =					model(lowerCAmelCase_	,	use_cache=lowerCAmelCase_						)
				UpperCAmelCase_     : List[str]			     =					model(lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					model(lowerCAmelCase_	,	use_cache=lowerCAmelCase_						)

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

				UpperCAmelCase_     : Optional[int]			     =					outputs["past_key_values"]

				# create hypothetical next token and extent to next_input_ids
				UpperCAmelCase_     : List[str]			     =					ids_tensor((2, 1)	,	config.vocab_size - 1						) + 1

				# append to next input_ids and
				UpperCAmelCase_     : Union[str, Any]			     =					torch.cat([input_ids, next_tokens]	,	dim=-1						)

				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_						)["last_hidden_state"]
				UpperCAmelCase_     : Union[str, Any]			     =					model(lowerCAmelCase_	,	past_key_values=lowerCAmelCase_						)["last_hidden_state"]

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

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





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Any:
				UpperCAmelCase_     : str			     =					self.prepare_config_and_inputs()
				UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					config_and_inputs

				UpperCAmelCase_     : Dict			     =					{"input_ids": input_ids, "attention_mask": attention_mask}
				return config, inputs_dict







@require_torch
class 			UpperCamelCase_       (__A				,						__A				,						__A				,						unittest.TestCase     ):
	__magic_name__	      =				(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
	__magic_name__	      =				(TrOCRForCausalLM,) if is_torch_available() else ()
	__magic_name__	      =				{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
	__magic_name__	      =				True
	__magic_name__	      =				False





	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			int:
				UpperCAmelCase_     : Tuple			     =					TrOCRStandaloneDecoderModelTester(self	,	is_training=lowerCAmelCase_						)
				UpperCAmelCase_     : List[str]			     =					ConfigTester(self	,	config_class=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			str:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Optional[Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Any:
				self.config_tester.run_common_tests()





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Tuple:
				UpperCAmelCase_     : List[Any]			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Union[str, Any]:
				return





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











"""simple docstring"""
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt







class 			UpperCamelCase_       :





	def __init__(      self					: str						)						->			Dict:
				UpperCAmelCase_     : List[Any]			     =					""
				UpperCAmelCase_     : int			     =					""
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : int			     =					0
				UpperCAmelCase_     : List[Any]			     =					256
				UpperCAmelCase_     : Dict			     =					0
				UpperCAmelCase_     : List[Any]			     =					0
				UpperCAmelCase_     : str			     =					0
				UpperCAmelCase_     : List[str]			     =					0





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Dict						)						->			Optional[Any]:
				UpperCAmelCase_     : Dict			     =					cva.imread(lowerCAmelCase_	,	0						)
				UpperCAmelCase_     : Union[str, Any]			     =					copy.deepcopy(self.img						)
				UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					plt.hist(self.img.ravel()	,	256	,	[0, 256]	,	label="x"						)
				UpperCAmelCase_     : List[Any]			     =					np.sum(lowerCAmelCase_						)
				for i in range(len(lowerCAmelCase_						)						):
							UpperCAmelCase_     : List[Any]			     =					x[i] / self.k
							self.sk += prk
							UpperCAmelCase_     : Optional[Any]			     =					(self.L - 1) * self.sk
							if self.rem != 0:
										UpperCAmelCase_     : Any			     =					int(last % last						)
							UpperCAmelCase_     : List[str]			     =					int(last + 1 if self.rem >= 0.5 else last						)
							self.last_list.append(lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[Any]			     =					int(np.ma.count(self.img						) / self.img[1].size						)
							UpperCAmelCase_     : Dict			     =					self.img[1].size
				for i in range(self.number_of_cols						):
							for j in range(self.number_of_rows						):
										UpperCAmelCase_     : Any			     =					self.img[j][i]
										if num != self.last_list[num]:
													UpperCAmelCase_     : Tuple			     =					self.last_list[num]
				cva.imwrite("output_data/output.jpg"	,	self.img						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Union[str, Any]:
				plt.hist(self.img.ravel()	,	256	,	[0, 256]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				cva.imshow("Output-Image"	,	self.img						)
				cva.imshow("Input-Image"	,	self.original_image						)
				cva.waitKey(5_000						)
				cva.destroyAllWindows()


if __name__ == "__main__":
			lowerCamelCase_					=      os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
			lowerCamelCase_					=      ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()



