






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       :			Dict ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Any=99 ,	UpperCamelCase_       :			int=13 ,	UpperCamelCase_       :			List[str]=16 ,	UpperCamelCase_       :			Tuple=7 ,	UpperCamelCase_       :			List[str]=True ,	UpperCamelCase_       :			List[str]=True ,	UpperCamelCase_       :			str=True ,	UpperCamelCase_       :			List[Any]=False ,	UpperCamelCase_       :			List[Any]=True ,	UpperCamelCase_       :			int=2 ,	UpperCamelCase_       :			str=32 ,	UpperCamelCase_       :			str=4 ,	UpperCamelCase_       :			Dict=4 ,	UpperCamelCase_       :			int=30 ,	UpperCamelCase_       :			Optional[int]=0 ,	UpperCamelCase_       :			str=1 ,	UpperCamelCase_       :			Any=2 ,	UpperCamelCase_       :			Union[str, Any]=None ,	):


         """simple docstring"""



         __A         =	parent
         __A         =	batch_size
         __A         =	decoder_seq_length
         # For common tests
         __A         =	self.decoder_seq_length
         __A         =	is_training
         __A         =	use_attention_mask
         __A         =	use_labels

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

         __A         =	None
         __A         =	decoder_seq_length
         __A         =	2
         __A         =	1




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	ids_tensor([self.batch_size, self.decoder_seq_length] ,	self.vocab_size				)

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

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

         __A         =	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 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			str ,	):


         """simple docstring"""



         __A         =	True
         __A         =	TrOCRDecoder(config=UpperCamelCase_				).to(UpperCamelCase_				).eval()
         __A         =	input_ids[:2]

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

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

         __A         =	outputs["""past_key_values"""]

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

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

         __A         =	model(UpperCamelCase_				)["""last_hidden_state"""]
         __A         =	model(UpperCamelCase_ ,	past_key_values=UpperCamelCase_				)["""last_hidden_state"""]

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

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





     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	self.prepare_config_and_inputs()
         __A    ,	__A    ,	__A    ,	__A         =	config_and_inputs

         __A         =	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
         return config, inputs_dict


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







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE   						=				(TrOCRForCausalLM,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE   						=				{"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				False




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	TrOCRStandaloneDecoderModelTester(self ,	is_training=UpperCamelCase_				)
         __A         =	ConfigTester(self ,	config_class=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			int				):


         """simple docstring"""



         pass




     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         pass




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         pass




     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return





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


         """simple docstring"""



         pass












import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__a      :       List[Any]				  =   "Usage of script: script_name <size_of_canvas:int>"

__a      :       Dict				  =   [0] * 100 + [1] * 10
random.shuffle(choice)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      list[list[bool]]:





    """simple docstring"""

    __A         =	[[False for i in range(__lowercase    )] for j in range(__lowercase    )]
    return canvas



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[list[bool]]    )						->      None:





    """simple docstring"""

    for i, row in enumerate(__lowercase    ):
        for j, _ in enumerate(__lowercase    ):
            __A         =	bool(random.getrandbits(1    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[list[bool]]    )						->      list[list[bool]]:





    """simple docstring"""

    __A         =	np.array(__lowercase    )
    __A         =	np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowercase    ):
        for c, pt in enumerate(__lowercase    ):
            __A         =	__judge_point(
                __lowercase			,				current_canvas[r - 1 : r + 2, c - 1 : c + 2]    )

    __A         =	next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    __A         =	current_canvas.tolist()
    return return_canvas



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			bool			,				__lowercase   :			list[list[bool]]    )						->      bool:





    """simple docstring"""

    __A         =	0
    __A         =	0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    __A         =	pt
    if pt:
        if alive < 2:
            __A         =	False
        elif alive == 2 or alive == 3:
            __A         =	True
        elif alive > 3:
            __A         =	False
    else:
        if alive == 3:
            __A         =	True

    return state


if __name__ == "__main__":
  if len(sys.argv) != 2:
    raise Exception(usage_doc)

  __a      :       str				  =   int(sys.argv[1])
  # main working structure of this module.
  __a      :       List[Any]				  =   create_canvas(canvas_size)
  seed(c)
  __a  ,__a      :       int				  =   plt.subplots()
  fig.show()
  __a      :       Union[str, Any]				  =   ListedColormap(["w", "k"])
  try:
    while True:
      __a      :       Dict				  =   run(c)
      ax.matshow(c, cmap=cmap)
      fig.canvas.draw()
      ax.cla()
  except KeyboardInterrupt:
    # do nothing.
    pass




