
"""simple docstring"""






from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''time_series_transformer'''
     UpperCAmelCase__         =      {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }



     def __init__(							self :Optional[int]      ,  _lowerCamelCase :Optional[int] = None      ,  _lowerCamelCase :Optional[int] = None      ,  _lowerCamelCase :str = "student_t"      ,  _lowerCamelCase :str = "nll"      ,  _lowerCamelCase :int = 1      ,  _lowerCamelCase :List[int] = [1, 2, 3, 4, 5, 6, 7]      ,  _lowerCamelCase :Optional[Union[str, bool]] = "mean"      ,  _lowerCamelCase :int = 0      ,  _lowerCamelCase :int = 0      ,  _lowerCamelCase :int = 0      ,  _lowerCamelCase :int = 0      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :int = 32      ,  _lowerCamelCase :int = 32      ,  _lowerCamelCase :int = 2      ,  _lowerCamelCase :int = 2      ,  _lowerCamelCase :int = 2      ,  _lowerCamelCase :int = 2      ,  _lowerCamelCase :bool = True      ,  _lowerCamelCase :str = "gelu"      ,  _lowerCamelCase :int = 64      ,  _lowerCamelCase :float = 0.1      ,  _lowerCamelCase :float = 0.1      ,  _lowerCamelCase :float = 0.1      ,  _lowerCamelCase :float = 0.1      ,  _lowerCamelCase :float = 0.1      ,  _lowerCamelCase :int = 100      ,  _lowerCamelCase :float = 0.02      ,  _lowerCamelCase :Tuple=True      ,  **_lowerCamelCase :List[Any]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=prediction_length
      UpperCamelCase_     :					int   	=context_length or prediction_length
      UpperCamelCase_     :					Any   	=distribution_output
      UpperCamelCase_     :					List[Any]   	=loss
      UpperCamelCase_     :					Tuple   	=input_size
      UpperCamelCase_     :					Any   	=num_time_features
      UpperCamelCase_     :					Union[str, Any]   	=lags_sequence
      UpperCamelCase_     :					Tuple   	=scaling
      UpperCamelCase_     :					Tuple   	=num_dynamic_real_features
      UpperCamelCase_     :					Tuple   	=num_static_real_features
      UpperCamelCase_     :					Optional[Any]   	=num_static_categorical_features
      if cardinality and num_static_categorical_features > 0:
       if len(_lowercase   ) != num_static_categorical_features:
        raise ValueError(
            'The cardinality should be a list of the same length as `num_static_categorical_features`'   )
       UpperCamelCase_     :					Optional[int]   	=cardinality
      else:
       UpperCamelCase_     :					Optional[Any]   	=[0]
      if embedding_dimension and num_static_categorical_features > 0:
       if len(_lowercase   ) != num_static_categorical_features:
        raise ValueError(
            'The embedding dimension should be a list of the same length as `num_static_categorical_features`'   )
       UpperCamelCase_     :					Optional[int]   	=embedding_dimension
      else:
       UpperCamelCase_     :					Dict   	=[min(50      ,  (cat + 1) // 2   ) for cat in self.cardinality]
      UpperCamelCase_     :					List[Any]   	=num_parallel_samples

      # Transformer architecture configuration
      UpperCamelCase_     :					Optional[Any]   	=input_size * len(_lowercase   ) + self._number_of_features
      UpperCamelCase_     :					List[Any]   	=d_model
      UpperCamelCase_     :					Dict   	=encoder_attention_heads
      UpperCamelCase_     :					Any   	=decoder_attention_heads
      UpperCamelCase_     :					Tuple   	=encoder_ffn_dim
      UpperCamelCase_     :					List[str]   	=decoder_ffn_dim
      UpperCamelCase_     :					List[str]   	=encoder_layers
      UpperCamelCase_     :					List[str]   	=decoder_layers

      UpperCamelCase_     :					Union[str, Any]   	=dropout
      UpperCamelCase_     :					Optional[Any]   	=attention_dropout
      UpperCamelCase_     :					List[Any]   	=activation_dropout
      UpperCamelCase_     :					Optional[int]   	=encoder_layerdrop
      UpperCamelCase_     :					List[Any]   	=decoder_layerdrop

      UpperCamelCase_     :					Tuple   	=activation_function
      UpperCamelCase_     :					List[Any]   	=init_std

      UpperCamelCase_     :					List[str]   	=use_cache

      super().__init__(is_encoder_decoder=_lowercase      ,  **_lowercase   )




     @property
     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      return (
          sum(self.embedding_dimension   )
          + self.num_dynamic_real_features
          + self.num_time_features
          + self.num_static_real_features
          + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
      )






'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		ViTImageProcessor if is_vision_available() else None



					@property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								A_									= (3, 32, 128)
								A_									= tempfile.mkdtemp()

								# fmt: off
								A_									= ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
								# fmt: on
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')

								A_									= {
								    'do_normalize': False,
								    'do_resize': True,
								    'image_processor_type': 'ViTImageProcessor',
								    'resample': 3,
								    'size': {'height': 32, 'width': 128},
								}
								A_									= os.path.join(self.tmpdirname      ,       _lowercase)
								with open(self.image_processor_file      ,       'w'      ,       encoding='utf-8') as fp:
											json.dump(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Optional[int])    ->							int:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								shutil.rmtree(self.tmpdirname)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								A_									= np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta)

								A_									= Image.fromarray(np.moveaxis(_lowercase      ,       0      ,       -1))

								return image_input



					def     __snake_case						(	self     :						Optional[Any])    ->							List[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)
								A_									= MgpstrProcessor.from_pretrained(self.tmpdirname      ,       use_fast=_lowercase)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)

								A_									= self.get_tokenizer(bos_token='(BOS)'      ,       eos_token='(EOS)')
								A_									= self.get_image_processor(do_normalize=_lowercase      ,       padding_value=1.0)

								A_									= MgpstrProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token='(BOS)'      ,       eos_token='(EOS)'      ,       do_normalize=_lowercase      ,       padding_value=1.0)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= self.prepare_image_inputs()

								A_									= image_processor(_lowercase      ,       return_tensors='np')
								A_									= processor(images=_lowercase      ,       return_tensors='np')

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()      ,       input_processor[key].sum()      ,       delta=1E-2)



					def     __snake_case						(	self     :						Any)    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'

								A_									= processor(text=_lowercase)

								A_									= tokenizer(_lowercase)
								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key])



					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       ['pixel_values', 'labels'])

								# test if it raises when no input is passed
								with pytest.raises(_lowercase):
											processor()



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								A_									= processor.char_decode(_lowercase)
								A_									= tokenizer.batch_decode(_lowercase)
								A_									= [seq.replace(' '      ,       '') for seq in decoded_tok]

								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= None
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       processor.model_input_names)



					def     __snake_case						(	self     :						List[str])    ->							Any:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= torch.randn(1      ,       27      ,       38)
								A_									= torch.randn(1      ,       27      ,       50_257)
								A_									= torch.randn(1      ,       27      ,       30_522)

								A_									= processor.batch_decode([char_input, bpe_input, wp_input])

								self.assertListEqual(list(results.keys())      ,       ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'])




