





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_A   :       Dict    					=    'scheduler_config.json'


class   __SCREAMING_SNAKE_CASE     (					_snake_case   ):
       _UpperCAmelCase :    str					       =       1
       _UpperCAmelCase :    str					       =       2
       _UpperCAmelCase :    Optional[int]					       =       3
       _UpperCAmelCase :    str					       =       4
       _UpperCAmelCase :    List[str]					       =       5


@dataclass
class   __SCREAMING_SNAKE_CASE     (					_snake_case   ):
       _UpperCAmelCase :    Dict					       =       4_2


class   __SCREAMING_SNAKE_CASE     :
       _UpperCAmelCase :    Optional[Any]					       =       SCHEDULER_CONFIG_NAME
       _UpperCAmelCase :    List[str]					       =       ["dtype"]
       _UpperCAmelCase :    Optional[Any]					       =       []
       _UpperCAmelCase :    Tuple					       =       True







       @classmethod
       def 		__lowerCamelCase       ( cls	:      Dict						,   A	:      Dict[str, Any] = None						,   A	:      Optional[str] = None						,   A	:      Optional[Any]=False						,   **A	:      str						,   )  ->Optional[int]:
          lowerCamelCase__  :		Union[str, Any]     =				cls.load_config(
              pretrained_model_name_or_path=__lowerCamelCase						,   subfolder=__lowerCamelCase						,   return_unused_kwargs=__lowerCamelCase						,   **__lowerCamelCase						,   )
          lowerCamelCase__  :		Optional[Any]     =				cls.from_config(__lowerCamelCase						,   return_unused_kwargs=__lowerCamelCase						,   **__lowerCamelCase     )

          if hasattr(__lowerCamelCase						,   '''create_state'''     ) and getattr(__lowerCamelCase						,   '''has_state'''						,   __lowerCamelCase     ):
             lowerCamelCase__  :		str     =				scheduler.create_state()

          if return_unused_kwargs:
             return scheduler, state, unused_kwargs

          return scheduler, state







       def 		__lowerCamelCase       ( self	:      Optional[int]						,   A	:      Union[str, os.PathLike]						,   A	:      bool = False						,   **A	:      Optional[int]     )  ->Any:
          self.save_config(save_directory=__lowerCamelCase						,   push_to_hub=__lowerCamelCase						,   **__lowerCamelCase     )







       @property
       def 		__lowerCamelCase       ( self	:      int     )  ->str:
          return self._get_compatibles()







       @classmethod
       def 		__lowerCamelCase       ( cls	:      List[str]     )  ->List[Any]:
          lowerCamelCase__  :		int     =				list(set([cls.__name__] + cls._compatibles     )     )
          lowerCamelCase__  :		int     =				importlib.import_module(__name__.split('''.'''     )[0]     )
          lowerCamelCase__  :		Tuple     =				[
              getattr(__lowerCamelCase						,   __lowerCamelCase     ) for c in compatible_classes_str if hasattr(__lowerCamelCase						,   __lowerCamelCase     )
          ]
          return compatible_classes



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							Optional[Any]:







   """simple docstring"""
   assert len(UpperCamelCase_	) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase_	) - x.ndim)	)     ,    UpperCamelCase_	)



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase=0.9_99     ,    UpperCAmelCase=jnp.floataa	)							->							int:







   """simple docstring"""

   def alpha_bar(UpperCAmelCase	):
      return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

   lowerCamelCase__  :		Dict     =				[]
   for i in range(UpperCamelCase_	):
      lowerCamelCase__  :		Optional[int]     =				i / num_diffusion_timesteps
      lowerCamelCase__  :		Union[str, Any]     =				(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(UpperCamelCase_	) / alpha_bar(UpperCamelCase_	)     ,    UpperCamelCase_	)	)
   return jnp.array(UpperCamelCase_     ,    dtype=UpperCamelCase_	)


@flax.struct.dataclass
class   __SCREAMING_SNAKE_CASE     :
       _UpperCAmelCase :    List[str]					       =       4_2
       _UpperCAmelCase :    str					       =       4_2
       _UpperCAmelCase :    Any					       =       4_2







       @classmethod
       def 		__lowerCamelCase       ( cls	:      Tuple						,   A	:      Union[str, Any]     )  ->List[str]:
          lowerCamelCase__  :		str     =				scheduler.config

          if config.trained_betas is not None:
             lowerCamelCase__  :		Tuple     =				jnp.asarray(config.trained_betas						,   dtype=scheduler.dtype     )
          elif config.beta_schedule == "linear":
             lowerCamelCase__  :		List[str]     =				jnp.linspace(config.beta_start						,   config.beta_end						,   config.num_train_timesteps						,   dtype=scheduler.dtype     )
          elif config.beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             lowerCamelCase__  :		List[Any]     =				(
                 jnp.linspace(
                     config.beta_start**0.5						,   config.beta_end**0.5						,   config.num_train_timesteps						,   dtype=scheduler.dtype     )
                 ** 2
             )
          elif config.beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             lowerCamelCase__  :		Optional[Any]     =				betas_for_alpha_bar(config.num_train_timesteps						,   dtype=scheduler.dtype     )
          else:
             raise NotImplementedError(
                 F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"     )

          lowerCamelCase__  :		int     =				1.0 - betas

          lowerCamelCase__  :		Tuple     =				jnp.cumprod(__lowerCamelCase						,   axis=0     )

          return cls(
              alphas=__lowerCamelCase						,   betas=__lowerCamelCase						,   alphas_cumprod=__lowerCamelCase						,   )



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase	)							->							int:







   """simple docstring"""
   lowerCamelCase__  :		Optional[Any]     =				state.alphas_cumprod

   lowerCamelCase__  :		List[str]     =				alphas_cumprod[timesteps] ** 0.5
   lowerCamelCase__  :		Any     =				sqrt_alpha_prod.flatten()
   lowerCamelCase__  :		Optional[Any]     =				broadcast_to_shape_from_left(UpperCamelCase_     ,    original_samples.shape	)

   lowerCamelCase__  :		List[str]     =				(1 - alphas_cumprod[timesteps]) ** 0.5
   lowerCamelCase__  :		Tuple     =				sqrt_one_minus_alpha_prod.flatten()
   lowerCamelCase__  :		List[Any]     =				broadcast_to_shape_from_left(UpperCamelCase_     ,    original_samples.shape	)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase	)							->							Optional[Any]:







   """simple docstring"""
   lowerCamelCase__  :		List[Any]     =				get_sqrt_alpha_prod(UpperCamelCase_     ,    UpperCamelCase_     ,    UpperCamelCase_     ,    UpperCamelCase_	)
   lowerCamelCase__  :		List[Any]     =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples




def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase	)							->							str:







   """simple docstring"""
   lowerCamelCase__  :		Tuple     =				get_sqrt_alpha_prod(UpperCamelCase_     ,    UpperCamelCase_     ,    UpperCamelCase_     ,    UpperCamelCase_	)
   lowerCamelCase__  :		Union[str, Any]     =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity







'''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						):
 UpperCAmelCase				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	:Union[str, Any]      =      ['''[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
    UpperCAmelCase__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      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''' )

    UpperCAmelCase__	:Dict      =      {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    UpperCAmelCase__	: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        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[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        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

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

    UpperCAmelCase__	: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        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	: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        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer(__lowerCamelCase )
    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,       encoded_processor[key] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	: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        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

    UpperCAmelCase__	:Optional[Any]      =      processor.batch_decode([char_input, bpe_input, wp_input] )

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



