



"""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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					_a             =							ViTImageProcessor if is_vision_available() else None



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



					def 		snake_case	(	self							:  Union[str, Any]					)-> Tuple:
								lowerCamelCase__		:		int		     =(3, 32, 128)
								lowerCamelCase__		:		List[Any]		     =tempfile.mkdtemp()

								# fmt: off
								lowerCamelCase__		:		Optional[int]		     =['''[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
								lowerCamelCase__		:		Tuple		     =dict(zip(lowerCamelCase,  range(len(lowerCamelCase					)					)					)					)

								lowerCamelCase__		:		List[Any]		     =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(lowerCamelCase					) + '''\n'''					)

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



					def 		snake_case	(	self							:  int,  **lowerCamelCase							:  Dict					)-> List[str]:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase					)



					def 		snake_case	(	self							:  List[str],  **lowerCamelCase							:  Any					)-> Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase					)



					def 		snake_case	(	self							:  str					)-> List[Any]:
								shutil.rmtree(self.tmpdirname					)



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

								lowerCamelCase__		:		Tuple		     =Image.fromarray(np.moveaxis(lowerCamelCase,  0,  -1					)					)

								return image_input



					def 		snake_case	(	self							:  List[Any]					)-> Optional[Any]:
								lowerCamelCase__		:		str		     =self.get_tokenizer()
								lowerCamelCase__		:		str		     =self.get_image_processor()

								lowerCamelCase__		:		str		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)
								processor.save_pretrained(self.tmpdirname					)
								lowerCamelCase__		:		int		     =MgpstrProcessor.from_pretrained(self.tmpdirname,  use_fast=lowerCamelCase					)

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

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



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

								lowerCamelCase__		:		Dict		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)
								processor.save_pretrained(self.tmpdirname					)

								lowerCamelCase__		:		List[Any]		     =self.get_tokenizer(bos_token='''(BOS)''',  eos_token='''(EOS)'''					)
								lowerCamelCase__		:		int		     =self.get_image_processor(do_normalize=lowerCamelCase,  padding_value=1.0					)

								lowerCamelCase__		:		Any		     =MgpstrProcessor.from_pretrained(
								    self.tmpdirname,  bos_token='''(BOS)''',  eos_token='''(EOS)''',  do_normalize=lowerCamelCase,  padding_value=1.0					)

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

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



					def 		snake_case	(	self							:  Optional[Any]					)-> Union[str, Any]:
								lowerCamelCase__		:		str		     =self.get_image_processor()
								lowerCamelCase__		:		List[Any]		     =self.get_tokenizer()

								lowerCamelCase__		:		Any		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		str		     =self.prepare_image_inputs()

								lowerCamelCase__		:		Tuple		     =image_processor(lowerCamelCase,  return_tensors='''np'''					)
								lowerCamelCase__		:		Tuple		     =processor(images=lowerCamelCase,  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							:  List[Any]					)-> Tuple:
								lowerCamelCase__		:		Any		     =self.get_image_processor()
								lowerCamelCase__		:		Any		     =self.get_tokenizer()

								lowerCamelCase__		:		Any		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		List[str]		     ='''test'''

								lowerCamelCase__		:		Union[str, Any]		     =processor(text=lowerCamelCase					)

								lowerCamelCase__		:		Union[str, Any]		     =tokenizer(lowerCamelCase					)
								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key],  encoded_processor[key]					)



					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__		:		Any		     =self.get_image_processor()
								lowerCamelCase__		:		List[str]		     =self.get_tokenizer()

								lowerCamelCase__		:		Dict		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		Dict		     ='''test'''
								lowerCamelCase__		:		Optional[int]		     =self.prepare_image_inputs()

								lowerCamelCase__		:		Any		     =processor(text=lowerCamelCase,  images=lowerCamelCase					)

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

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



					def 		snake_case	(	self							:  List[Any]					)-> Optional[Any]:
								lowerCamelCase__		:		Optional[int]		     =self.get_image_processor()
								lowerCamelCase__		:		Dict		     =self.get_tokenizer()

								lowerCamelCase__		:		Union[str, Any]		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__		:		List[Any]		     =processor.char_decode(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =tokenizer.batch_decode(lowerCamelCase					)
								lowerCamelCase__		:		str		     =[seq.replace(''' ''',  ''''''					) for seq in decoded_tok]

								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)



					def 		snake_case	(	self							:  Any					)-> List[Any]:
								lowerCamelCase__		:		Dict		     =self.get_image_processor()
								lowerCamelCase__		:		int		     =self.get_tokenizer()

								lowerCamelCase__		:		Any		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		List[str]		     =None
								lowerCamelCase__		:		str		     =self.prepare_image_inputs()

								lowerCamelCase__		:		Dict		     =processor(text=lowerCamelCase,  images=lowerCamelCase					)

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




					def 		snake_case	(	self							:  List[str]					)-> Any:
								lowerCamelCase__		:		List[Any]		     =self.get_image_processor()
								lowerCamelCase__		:		int		     =self.get_tokenizer()

								lowerCamelCase__		:		Optional[int]		     =MgpstrProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =torch.randn(1,  27,  38					)
								lowerCamelCase__		:		Optional[Any]		     =torch.randn(1,  27,  5_0257					)
								lowerCamelCase__		:		List[str]		     =torch.randn(1,  27,  3_0522					)

								lowerCamelCase__		:		str		     =processor.batch_decode([char_input, bpe_input, wp_input]					)

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









"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp






def 					snake_case__       (		__lowerCamelCase    :   jnp.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1.0e4							,		__lowerCamelCase    :   bool = False							,		__lowerCamelCase    :   float = 1.0							,		):
			"""simple docstring"""
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
			lowerCamelCase__		:		Any		     =float(embedding_dim // 2     )
			lowerCamelCase__		:		List[str]		     =math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			lowerCamelCase__		:		int		     =min_timescale * jnp.exp(jnp.arange(__lowerCamelCase							,		dtype=jnp.floataa     ) * -log_timescale_increment     )
			lowerCamelCase__		:		Tuple		     =jnp.expand_dims(__lowerCamelCase							,		1     ) * jnp.expand_dims(__lowerCamelCase							,		0     )

			# scale embeddings
			lowerCamelCase__		:		List[str]		     =scale * emb

			if flip_sin_to_cos:
						lowerCamelCase__		:		int		     =jnp.concatenate([jnp.cos(__lowerCamelCase     ), jnp.sin(__lowerCamelCase     )]							,		axis=1     )
			else:
						lowerCamelCase__		:		List[str]		     =jnp.concatenate([jnp.sin(__lowerCamelCase     ), jnp.cos(__lowerCamelCase     )]							,		axis=1     )
			lowerCamelCase__		:		str		     =jnp.reshape(__lowerCamelCase							,		[jnp.shape(__lowerCamelCase     )[0], embedding_dim]     )
			return signal

class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							jnp.floataa



					@nn.compact
					def __call__(	self							:  Optional[Any],  lowerCamelCase							:  int					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_1'''					)(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =nn.silu(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_2'''					)(lowerCamelCase					)
								return temb





class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							False
					_a             =							1



					@nn.compact
					def __call__(	self							:  Any,  lowerCamelCase							:  int					)-> int:
								return get_sinusoidal_embeddings(
								    lowerCamelCase,  embedding_dim=self.dim,  flip_sin_to_cos=self.flip_sin_to_cos,  freq_shift=self.freq_shift					)




