



'''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  ,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  ,)		->   List[str]:
        _UpperCAmelCase  :						List[str]			     = parent
        _UpperCAmelCase  :						Optional[int]			     = batch_size
        _UpperCAmelCase  :						Optional[Any]			     = decoder_seq_length
        # For common tests
        _UpperCAmelCase  :						str			     = self.decoder_seq_length
        _UpperCAmelCase  :						Any			     = is_training
        _UpperCAmelCase  :						List[Any]			     = use_attention_mask
        _UpperCAmelCase  :						List[Any]			     = use_labels

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

        _UpperCAmelCase  :						List[str]			     = None
        _UpperCAmelCase  :						str			     = decoder_seq_length
        _UpperCAmelCase  :						Union[str, Any]			     = 2
        _UpperCAmelCase  :						Tuple			     = 1







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						List[Any]			     = ids_tensor([self.batch_size, self.decoder_seq_length]  ,self.vocab_size							)

        _UpperCAmelCase  :						Any			     = None
        if self.use_attention_mask:
               _UpperCAmelCase  :						Union[str, Any]			     = ids_tensor([self.batch_size, self.decoder_seq_length]  ,vocab_size=2							)

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

        _UpperCAmelCase  :						Tuple			     = 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 _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,)		->   List[str]:
        _UpperCAmelCase  :						Union[str, Any]			     = True
        _UpperCAmelCase  :						Optional[int]			     = TrOCRDecoder(config=__a							).to(__a							).eval()
        _UpperCAmelCase  :						str			     = input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        _UpperCAmelCase  :						str			     = model(__a  ,use_cache=__a							)
        _UpperCAmelCase  :						List[str]			     = model(__a							)
        _UpperCAmelCase  :						Any			     = model(__a  ,use_cache=__a							)

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

        _UpperCAmelCase  :						int			     = outputs["""past_key_values"""]

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

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

        _UpperCAmelCase  :						List[Any]			     = model(__a							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Union[str, Any]			     = model(__a  ,past_key_values=__a							)["""last_hidden_state"""]

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

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







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						List[str]			     = self.prepare_config_and_inputs()
        _UpperCAmelCase  :						Dict			     = config_and_inputs

        _UpperCAmelCase  :						Optional[Any]			     = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict







@require_torch
class 							lowercase       (  __a					,					__a					,					__a					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 UpperCAmelCase							=  (TrOCRForCausalLM,) if is_torch_available() else ()
 UpperCAmelCase							=  {'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
 UpperCAmelCase							=  True
 UpperCAmelCase							=  False







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						Dict			     = TrOCRStandaloneDecoderModelTester(self  ,is_training=__a							)
        _UpperCAmelCase  :						Tuple			     = ConfigTester(self  ,config_class=__a							)







 def _snake_case (							self							)		->   Optional[Any]:
        pass







 def _snake_case (							self							)		->   Optional[int]:
        pass







 def _snake_case (							self							)		->   int:
        pass







 def _snake_case (							self							)		->   Tuple:
        self.config_tester.run_common_tests()







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						str			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*__a							)







 def _snake_case (							self							)		->   Tuple:
        return







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











from __future__ import annotations

import math

lowerCamelCase       :       List[Any]        =				'''2020.9.26'''
lowerCamelCase       :       str        =				'''xcodz-dot, cclaus, dhruvmanila'''
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float					):
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in locals().values()					):
														__lowercase					:							str			=   F"Input values must either be float or int: {list(locals().values()					)}"
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   ((x * distance) / (z + distance)) * scale
							__lowercase					:							Tuple			=   ((y * distance) / (z + distance)) * scale
							return projected_x, projected_y
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  float					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														raise TypeError("""Axis must be a str"""					)
							__lowercase					:							Optional[int]			=   locals()
							del input_variables["axis"]
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in input_variables.values()					):
														__lowercase					:							List[str]			=   (
														    """Input values except axis must either be float or int: """
														    F"{list(input_variables.values()					)}"
														)
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							Tuple			=   (angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__lowercase					:							int			=   x * math.cos(lowerCAmelCase_					) - y * math.sin(lowerCAmelCase_					)
														__lowercase					:							Tuple			=   y * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   z
							elif axis == "x":
														__lowercase					:							str			=   y * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							Dict			=   z * math.cos(lowerCAmelCase_					) + y * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   x
							elif axis == "y":
														__lowercase					:							List[str]			=   x * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   z * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[Any]			=   y
							else:
														raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'"""					)

							return new_x, new_y, new_z


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
	print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')