


"""simple docstring"""







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
       lowerCamelCase_       :       Optional[Any]      						=  """pt"""
elif is_tf_available():
       lowerCamelCase_       :       Optional[int]      						=  """tf"""
else:
       lowerCamelCase_       :       Any      						=  """jax"""






class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  PerceiverTokenizer
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     super().setUp()
     a					=PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname						)


 @cached_property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					PerceiverTokenizer:
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=False			,   __A=20			,   __A=5						) ->					Tuple[str, list]:
     # XXX The default common tokenizer tests assume that every ID is decodable on its own.
     # This assumption is invalid for Perceiver because single bytes might not be
     # valid utf-8 (byte 128 for instance).
     # Here we're overriding the smallest possible method to provide
     # a clean sequence without making the same assumption.

     a					=[]
     for i in range(len(__A						)						):
         try:
             a					=tokenizer.decode([i]			,   clean_up_tokenization_spaces=__A						)
         except UnicodeDecodeError:
             pass
         toks.append((i, tok)						)

     a					=list(filter(lambda __A						: re.match(r'''^[ a-zA-Z]+$'''			,   t[1]						)			,   __A						)						)
     a					=list(filter(lambda __A						: [t[0]] == tokenizer.encode(t[1]			,   add_special_tokens=__A						)			,   __A						)						)
     if max_length is not None and len(__A						) > max_length:
         a					=toks[:max_length]
     if min_length is not None and len(__A						) < min_length and len(__A						) > 0:
         while len(__A						) < min_length:
             a					=toks + toks
        # toks_str = [t[1] for t in toks]
     a					=[t[0] for t in toks]

     # Ensure consistency
     a					=tokenizer.decode(__A			,   clean_up_tokenization_spaces=__A						)
     if " " not in output_txt and len(__A						) > 1:
         a					=(
             tokenizer.decode([toks_ids[0]]			,   clean_up_tokenization_spaces=__A						)
             + ''' '''
             + tokenizer.decode(toks_ids[1:]			,   clean_up_tokenization_spaces=__A						)
         )
     if with_prefix_space:
         a					=''' ''' + output_txt
     a					=tokenizer.encode(__A			,   add_special_tokens=__A						)
     return output_txt, output_ids


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.perceiver_tokenizer
     a					='''Unicode €.'''
     a					=tokenizer(__A						)
     a					=[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['''input_ids''']			,   __A						)

     # decoding
     a					=tokenizer.decode(__A						)
     self.assertEqual(__A			,   '''[CLS]Unicode €.[SEP]'''						)

     a					=tokenizer('''e è é ê ë'''						)
     a					=[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['''input_ids''']			,   __A						)
     # decoding
     a					=tokenizer.decode(__A						)
     self.assertEqual(__A			,   '''[CLS]e è é ê ë[SEP]'''						)

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''						)						)			,   '''[CLS]e è é ê ë[SEP]'''						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=self.perceiver_tokenizer
     a					=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     a					=[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
     # fmt: on
     a					=tokenizer(__A			,   padding=__A			,   return_tensors=__A						)
     self.assertIsInstance(__A			,   __A						)

     if FRAMEWORK != "jax":
         a					=list(batch.input_ids.numpy()[0]						)
     else:
         a					=list(batch.input_ids.tolist()[0]						)

     self.assertListEqual(__A			,   __A						)

     self.assertEqual((2, 38)			,   batch.input_ids.shape						)
     self.assertEqual((2, 38)			,   batch.attention_mask.shape						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=self.perceiver_tokenizer
     a					=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     a					=tokenizer(__A			,   padding=__A			,   return_tensors=__A						)
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('''input_ids'''			,   __A						)
     self.assertIn('''attention_mask'''			,   __A						)
     self.assertNotIn('''decoder_input_ids'''			,   __A						)
     self.assertNotIn('''decoder_attention_mask'''			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=self.perceiver_tokenizer
     a					=[
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     a					=tokenizer(
         text_target=__A			,   max_length=32			,   padding='''max_length'''			,   truncation=__A			,   return_tensors=__A						)
     self.assertEqual(32			,   targets['''input_ids'''].shape[1]						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     # safety check on max_len default value so we are sure the test works
     a					=self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
             self.assertNotEqual(tokenizer.model_max_length			,   42						)

        # Now let's start the test
     a					=self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
             # Isolate this from the other tests because we save additional tokens/etc
             a					=tempfile.mkdtemp()

             a					=''' He is very happy, UNwant\u00E9d,running'''
             a					=tokenizer.encode(__A			,   add_special_tokens=__A						)
             tokenizer.save_pretrained(__A						)

             a					=tokenizer.__class__.from_pretrained(__A						)
             a					=after_tokenizer.encode(__A			,   add_special_tokens=__A						)
             self.assertListEqual(__A			,   __A						)

             shutil.rmtree(__A						)

     a					=self.get_tokenizers(model_max_length=42						)
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
             # Isolate this from the other tests because we save additional tokens/etc
             a					=tempfile.mkdtemp()

             a					=''' He is very happy, UNwant\u00E9d,running'''
             tokenizer.add_tokens(['''bim''', '''bambam''']						)
             a					=tokenizer.additional_special_tokens
             additional_special_tokens.append('''new_additional_special_token'''						)
             tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}						)
             a					=tokenizer.encode(__A			,   add_special_tokens=__A						)
             tokenizer.save_pretrained(__A						)

             a					=tokenizer.__class__.from_pretrained(__A						)
             a					=after_tokenizer.encode(__A			,   add_special_tokens=__A						)
             self.assertListEqual(__A			,   __A						)
             self.assertIn('''new_additional_special_token'''			,   after_tokenizer.additional_special_tokens						)
             self.assertEqual(after_tokenizer.model_max_length			,   42						)

             a					=tokenizer.__class__.from_pretrained(__A			,   model_max_length=43						)
             self.assertEqual(tokenizer.model_max_length			,   43						)

             shutil.rmtree(__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=[]
     if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

     if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

     for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer_utils.save_pretrained(__A						)

             with open(os.path.join(__A			,   '''special_tokens_map.json'''						)			,   encoding='''utf-8'''						) as json_file:
                 a					=json.load(__A						)

             with open(os.path.join(__A			,   '''tokenizer_config.json'''						)			,   encoding='''utf-8'''						) as json_file:
                 a					=json.load(__A						)

             a					=[f'''<extra_id_{i}>''' for i in range(125						)]

             a					=added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]
             a					=added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]

             with open(os.path.join(__A			,   '''special_tokens_map.json'''						)			,   '''w'''			,   encoding='''utf-8'''						) as outfile:
                 json.dump(__A			,   __A						)
             with open(os.path.join(__A			,   '''tokenizer_config.json'''						)			,   '''w'''			,   encoding='''utf-8'''						) as outfile:
                 json.dump(__A			,   __A						)

             # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
             # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
             # "special_tokens_map.json" files
             a					=tokenizer_class.from_pretrained(
                 __A			,   )
             self.assertIn(
                 '''an_additional_special_token'''			,   tokenizer_without_change_in_init.additional_special_tokens						)
             self.assertEqual(
                 ['''an_additional_special_token''']			,   tokenizer_without_change_in_init.convert_ids_to_tokens(
                     tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']						)						)			,   )

             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             a					=added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,   lstrip=__A						)]
             a					=tokenizer_class.from_pretrained(
                 __A			,   additional_special_tokens=__A			,   )

             self.assertIn('''a_new_additional_special_token'''			,   tokenizer.additional_special_tokens						)
             self.assertEqual(
                 ['''a_new_additional_special_token''']			,   tokenizer.convert_ids_to_tokens(
                     tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']						)						)			,   )


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178]						)			,   '''�'''						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     pass





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     a					=self.get_tokenizers(fast=__A			,   do_lower_case=__A						)
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
             a					=['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
             a					=tokenizer.convert_tokens_to_string(__A						)

             self.assertIsInstance(__A			,   __A						)



"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 				__A    :






 """simple docstring"""




 __lowerCAmelCase			 =  42

 # setable values
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  None


 @classmethod
 def 				SCREAMING_SNAKE_CASE							(		cls			,   __A			,   __A			,   __A						) ->					List[str]:
     return cls(common=__A			,   init_noise_sigma=__A			,   timesteps=__A						)






@dataclass
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  42






class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCAmelCase			 =  42


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return True


 @register_to_config
 def __init__(		self			,   __A = 1000			,   __A = 0.0_001			,   __A = 0.02			,   __A = "linear"			,   __A = None			,   __A = "fixed_small"			,   __A = True			,   __A = "epsilon"			,   __A = jnp.floataa			,   ) ->					List[Any]:
     a					=dtype


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A = None						) ->					DDPMSchedulerState:
     if common is None:
         a					=CommonSchedulerState.create(self						)

     # standard deviation of the initial noise distribution
     a					=jnp.array(1.0			,   dtype=self.dtype						)

     a					=jnp.arange(0			,   self.config.num_train_timesteps						).round()[::-1]

     return DDPMSchedulerState.create(
         common=__A			,   init_noise_sigma=__A			,   timesteps=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None						) ->					jnp.ndarray:
     return sample


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = ()						) ->					DDPMSchedulerState:
     a					=self.config.num_train_timesteps // num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # rounding to avoid issues when num_inference_step is power of 3
     a					=(jnp.arange(0			,   __A						) * step_ratio).round()[::-1]

     return state.replace(
         num_inference_steps=__A			,   timesteps=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A=None			,   __A=None						) ->					str:
     a					=state.common.alphas_cumprod[t]
     a					=jnp.where(t > 0			,   state.common.alphas_cumprod[t - 1]			,   jnp.array(1.0			,   dtype=self.dtype						)						)

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     a					=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         a					=self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         a					=jnp.clip(__A			,   a_min=1E-2_0						)
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         a					=jnp.log(jnp.clip(__A			,   a_min=1E-2_0						)						)
     elif variance_type == "fixed_large":
         a					=state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         a					=jnp.log(state.common.betas[t]						)
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         a					=variance
         a					=state.common.betas[t]
         a					=(predicted_variance + 1) / 2
         a					=frac * max_log + (1 - frac) * min_log

     return variance


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A = None			,   __A = True			,   ) ->					Union[FlaxDDPMSchedulerOutput, Tuple]:
     a					=timestep

     if key is None:
         a					=jax.random.PRNGKey(0						)

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         a		,				a					=jnp.split(__A			,   sample.shape[1]			,   axis=1						)
     else:
         a					=None

     # 1. compute alphas, betas
     a					=state.common.alphas_cumprod[t]
     a					=jnp.where(t > 0			,   state.common.alphas_cumprod[t - 1]			,   jnp.array(1.0			,   dtype=self.dtype						)						)
     a					=1 - alpha_prod_t
     a					=1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if self.config.prediction_type == "epsilon":
         a					=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         a					=model_output
     elif self.config.prediction_type == "v_prediction":
         a					=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
             ''' for the FlaxDDPMScheduler.'''						)

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
         a					=jnp.clip(__A			,   -1			,   1						)

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     a					=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     a					=state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     a					=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
         a					=jax.random.split(__A			,   num=1						)
         a					=jax.random.normal(__A			,   shape=model_output.shape			,   dtype=self.dtype						)
         return (self._get_variance(__A			,   __A			,   predicted_variance=__A						) ** 0.5) * noise

     a					=jnp.where(t > 0			,   random_variance()			,   jnp.zeros(model_output.shape			,   dtype=self.dtype						)						)

     a					=pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__A			,   state=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return add_noise_common(state.common			,   __A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return get_velocity_common(state.common			,   __A			,   __A			,   __A						)





 def __len__(		self						) ->					Optional[int]:
     return self.config.num_train_timesteps