





# 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        __SCREAMING_SNAKE_CASE					:
							snake_case     :      CommonSchedulerState

							# setable values
							snake_case     :      jnp.ndarray
							snake_case     :      jnp.ndarray
							snake_case     :      Optional[int]						     =       None






							@classmethod
							def 				_lowerCamelCase		(						cls  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														return cls(common=__lowerCAmelCase  , init_noise_sigma=__lowerCAmelCase  , timesteps=__lowerCAmelCase  )






@dataclass
class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      DDPMSchedulerState






class        __SCREAMING_SNAKE_CASE					(     _a   ,		_a       ):
							snake_case     :      Any						     =       [e.name for e in FlaxKarrasDiffusionSchedulers]

							snake_case     :      jnp.dtype






							@property
							def 				_lowerCamelCase		(						self  ):
														return True






							@register_to_config
							def __init__(						self  , __lowerCAmelCase = 1000  , __lowerCAmelCase = 0.0001  , __lowerCAmelCase = 0.02  , __lowerCAmelCase = "linear"  , __lowerCAmelCase = None  , __lowerCAmelCase = "fixed_small"  , __lowerCAmelCase = True  , __lowerCAmelCase = "epsilon"  , __lowerCAmelCase = jnp.floataa  , ):
														UpperCamelCase__				     =			dtype






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase = None  ):
														if common is None:
																					UpperCamelCase__				     =			CommonSchedulerState.create(self  )

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

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

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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  ):
														return sample






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = ()  ):
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			(jnp.arange(0  , __lowerCAmelCase  ) * step_ratio).round()[::-1]

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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase=None  , __lowerCAmelCase=None  ):
														UpperCamelCase__				     =			state.common.alphas_cumprod[t]
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

														# hacks - were probably added for training stability
														if variance_type == "fixed_small":
																					UpperCamelCase__				     =			jnp.clip(__lowerCAmelCase  , a_min=1E-20  )
														# for rl-diffuser https://arxiv.org/abs/2205.09991
														elif variance_type == "fixed_small_log":
																					UpperCamelCase__				     =			jnp.log(jnp.clip(__lowerCAmelCase  , a_min=1E-20  )  )
														elif variance_type == "fixed_large":
																					UpperCamelCase__				     =			state.common.betas[t]
														elif variance_type == "fixed_large_log":
																					# Glide max_log
																					UpperCamelCase__				     =			jnp.log(state.common.betas[t]  )
														elif variance_type == "learned":
																					return predicted_variance
														elif variance_type == "learned_range":
																					UpperCamelCase__				     =			variance
																					UpperCamelCase__				     =			state.common.betas[t]
																					UpperCamelCase__				     =			(predicted_variance + 1) / 2
																					UpperCamelCase__				     =			frac * max_log + (1 - frac) * min_log

														return variance






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  , __lowerCAmelCase = True  , ):
														UpperCamelCase__				     =			timestep

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

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

														# 1. compute alphas, betas
														UpperCamelCase__				     =			state.common.alphas_cumprod[t]
														UpperCamelCase__				     =			jnp.where(t > 0  , state.common.alphas_cumprod[t - 1]  , jnp.array(1.0  , dtype=self.dtype  )  )
														UpperCamelCase__				     =			1 - alpha_prod_t
														UpperCamelCase__				     =			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":
																					UpperCamelCase__				     =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
														elif self.config.prediction_type == "sample":
																					UpperCamelCase__				     =			model_output
														elif self.config.prediction_type == "v_prediction":
																					UpperCamelCase__				     =			(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:
																					UpperCamelCase__				     =			jnp.clip(__lowerCAmelCase  , -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
														UpperCamelCase__				     =			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

														UpperCamelCase__				     =			pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample, state)

														return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase  , state=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , ):
														return add_noise_common(state.common  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , ):
														return get_velocity_common(state.common  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )







							def __len__(						self  ):
														return self.config.num_train_timesteps











import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      Any						     =       MODEL_FOR_MASKED_LM_MAPPING
							snake_case     :      Dict						     =       TF_MODEL_FOR_MASKED_LM_MAPPING






							def 				_lowerCamelCase		(						self  ):
														super().tearDown()
														# clean-up as much as possible GPU memory occupied by PyTorch
														gc.collect()
														if is_torch_available():
																					import torch

																					torch.cuda.empty_cache()






							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , top_k=2  , framework="""tf"""  )
														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {"""sequence""": """My name is grouped""", """score""": 2.1E-05, """token""": 38015, """token_str""": """ grouped"""},
														        {"""sequence""": """My name is accuser""", """score""": 2.1E-05, """token""": 25506, """token_str""": """ accuser"""},
														    ]  , )

														UpperCamelCase__				     =			unmasker("""The largest city in France is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {
														            """sequence""": """The largest city in France is grouped""",
														            """score""": 2.1E-05,
														            """token""": 38015,
														            """token_str""": """ grouped""",
														        },
														        {
														            """sequence""": """The largest city in France is accuser""",
														            """score""": 2.1E-05,
														            """token""": 25506,
														            """token_str""": """ accuser""",
														        },
														    ]  , )

														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  , targets=[""" Patrick""", """ Clara""", """ Teven"""]  , top_k=3  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 13606, """token_str""": """ Clara"""},
														        {"""sequence""": """My name is Patrick""", """score""": 2E-05, """token""": 3499, """token_str""": """ Patrick"""},
														        {"""sequence""": """My name is Te""", """score""": 1.9E-05, """token""": 2941, """token_str""": """ Te"""},
														    ]  , )






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , top_k=2  , framework="""pt"""  )

														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {"""sequence""": """My name is Maul""", """score""": 2.2E-05, """token""": 35676, """token_str""": """ Maul"""},
														        {"""sequence""": """My name isELS""", """score""": 2.2E-05, """token""": 16416, """token_str""": """ELS"""},
														    ]  , )

														UpperCamelCase__				     =			unmasker("""The largest city in France is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {
														            """sequence""": """The largest city in France is Maul""",
														            """score""": 2.2E-05,
														            """token""": 35676,
														            """token_str""": """ Maul""",
														        },
														        {"""sequence""": """The largest city in France isELS""", """score""": 2.2E-05, """token""": 16416, """token_str""": """ELS"""},
														    ]  , )

														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  , targets=[""" Patrick""", """ Clara""", """ Teven"""]  , top_k=3  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {"""sequence""": """My name is Patrick""", """score""": 2.1E-05, """token""": 3499, """token_str""": """ Patrick"""},
														        {"""sequence""": """My name is Te""", """score""": 2E-05, """token""": 2941, """token_str""": """ Te"""},
														        {"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 13606, """token_str""": """ Clara"""},
														    ]  , )

														UpperCamelCase__				     =			unmasker("""My name is <mask> <mask>"""  , top_k=2  )

														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        [
														            {
														                """score""": 2.2E-05,
														                """token""": 35676,
														                """token_str""": """ Maul""",
														                """sequence""": """<s>My name is Maul<mask></s>""",
														            },
														            {"""score""": 2.2E-05, """token""": 16416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
														        ],
														        [
														            {
														                """score""": 2.2E-05,
														                """token""": 35676,
														                """token_str""": """ Maul""",
														                """sequence""": """<s>My name is<mask> Maul</s>""",
														            },
														            {"""score""": 2.2E-05, """token""": 16416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
														        ],
														    ]  , )






							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline("""fill-mask"""  , model="""hf-internal-testing/tiny-random-distilbert"""  , device=0  , framework="""pt"""  )

														# convert model to fp16
														pipe.model.half()

														UpperCamelCase__				     =			pipe("""Paris is the [MASK] of France."""  )
														# We actually don't care about the result, we just want to make sure
														# it works, meaning the float16 tensor got casted back to float32
														# for postprocessing.
														self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""distilroberta-base"""  , top_k=2  , framework="""pt"""  )
														self.run_large_test(__lowerCAmelCase  )






							@slow
							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""distilroberta-base"""  , top_k=2  , framework="""tf"""  )
														self.run_large_test(__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  )  , [
														        {"""sequence""": """My name is John""", """score""": 0.008, """token""": 610, """token_str""": """ John"""},
														        {"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 1573, """token_str""": """ Chris"""},
														    ]  , )
														UpperCamelCase__				     =			unmasker("""The largest city in France is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  )  , [
														        {
														            """sequence""": """The largest city in France is Paris""",
														            """score""": 0.251,
														            """token""": 2201,
														            """token_str""": """ Paris""",
														        },
														        {
														            """sequence""": """The largest city in France is Lyon""",
														            """score""": 0.214,
														            """token""": 12790,
														            """token_str""": """ Lyon""",
														        },
														    ]  , )

														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  , targets=[""" Patrick""", """ Clara""", """ Teven"""]  , top_k=3  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  )  , [
														        {"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3499, """token_str""": """ Patrick"""},
														        {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 13606, """token_str""": """ Clara"""},
														        {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2941, """token_str""": """ Te"""},
														    ]  , )






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , framework="""pt"""  )
														UpperCamelCase__				     =			None
														UpperCamelCase__				     =			None
														self.run_pipeline_test(__lowerCAmelCase  , []  )






							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , framework="""tf"""  )
														UpperCamelCase__				     =			None
														UpperCamelCase__				     =			None
														self.run_pipeline_test(__lowerCAmelCase  , []  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														if tokenizer is None or tokenizer.mask_token_id is None:
																					self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)"""  )

														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														UpperCamelCase__				     =			[
														    f"""This is another {tokenizer.mask_token} test""",
														]
														return fill_masker, examples






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			fill_masker.tokenizer
														UpperCamelCase__				     =			fill_masker.model

														UpperCamelCase__				     =			fill_masker(
														    f"""This is a {tokenizer.mask_token}"""  , )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														    ]  , )

														UpperCamelCase__				     =			fill_masker([f"""This is a {tokenizer.mask_token}"""]  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														    ]  , )

														UpperCamelCase__				     =			fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""]  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        ],
														        [
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        ],
														    ]  , )

														with self.assertRaises(__lowerCAmelCase  ):
																					fill_masker([None]  )
														# No mask_token is not supported
														with self.assertRaises(__lowerCAmelCase  ):
																					fill_masker("""This is"""  )

														self.run_test_top_k(__lowerCAmelCase  , __lowerCAmelCase  )
														self.run_test_targets(__lowerCAmelCase  , __lowerCAmelCase  )
														self.run_test_top_k_targets(__lowerCAmelCase  , __lowerCAmelCase  )
														self.fill_mask_with_duplicate_targets_and_top_k(__lowerCAmelCase  , __lowerCAmelCase  )
														self.fill_mask_with_multiple_masks(__lowerCAmelCase  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			tokenizer.get_vocab()
														UpperCamelCase__				     =			sorted(vocab.keys()  )[:2]
														# Pipeline argument
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  , targets=__lowerCAmelCase  )
														UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														    ]  , )
														UpperCamelCase__				     =			{vocab[el] for el in targets}
														self.assertEqual({el["""token"""] for el in outputs}  , __lowerCAmelCase  )
														UpperCamelCase__				     =			[tokenizer.decode([x]  ) for x in target_ids]
														self.assertEqual({el["""token_str"""] for el in outputs}  , set(__lowerCAmelCase  )  )

														# Call argument
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , targets=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														    ]  , )
														UpperCamelCase__				     =			{vocab[el] for el in targets}
														self.assertEqual({el["""token"""] for el in outputs}  , __lowerCAmelCase  )
														UpperCamelCase__				     =			[tokenizer.decode([x]  ) for x in target_ids]
														self.assertEqual({el["""token_str"""] for el in outputs}  , set(__lowerCAmelCase  )  )

														# Score equivalence
														UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , targets=__lowerCAmelCase  )
														UpperCamelCase__				     =			[top_mask["""token_str"""] for top_mask in outputs]
														UpperCamelCase__				     =			[top_mask["""score"""] for top_mask in outputs]

														# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
														if set(__lowerCAmelCase  ) == set(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , targets=__lowerCAmelCase  )
																					UpperCamelCase__				     =			[top_mask["""score"""] for top_mask in unmasked_targets]
																					self.assertEqual(nested_simplify(__lowerCAmelCase  )  , nested_simplify(__lowerCAmelCase  )  )

														# Raises with invalid
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , targets=[]  )
														# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
														if "" not in tokenizer.get_vocab():
																					with self.assertRaises(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , targets=[""""""]  )
																					with self.assertRaises(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , targets=""""""  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  , top_k=2  )
														UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														    ]  , )

														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , top_k=2  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														    ]  , )
														self.assertEqual(nested_simplify(__lowerCAmelCase  )  , nested_simplify(__lowerCAmelCase  )  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			tokenizer.get_vocab()
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )

														# top_k=2, ntargets=3
														UpperCamelCase__				     =			sorted(vocab.keys()  )[:3]
														UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , top_k=2  , targets=__lowerCAmelCase  )

														# If we use the most probably targets, and filter differently, we should still
														# have the same results
														UpperCamelCase__				     =			[el["""token_str"""] for el in sorted(__lowerCAmelCase  , key=lambda __lowerCAmelCase  : x["score"]  , reverse=__lowerCAmelCase  )]
														# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
														if set(__lowerCAmelCase  ).issubset(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			fill_masker(f"""This is a {tokenizer.mask_token}"""  , top_k=3  , targets=__lowerCAmelCase  )
																					# They should yield exactly the same result
																					self.assertEqual(nested_simplify(__lowerCAmelCase  )  , nested_simplify(__lowerCAmelCase  )  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														UpperCamelCase__				     =			tokenizer.get_vocab()
														# String duplicates + id duplicates
														UpperCamelCase__				     =			sorted(vocab.keys()  )[:3]
														UpperCamelCase__				     =			[targets[0], targets[1], targets[0], targets[2], targets[1]]
														UpperCamelCase__				     =			fill_masker(f"""My name is {tokenizer.mask_token}"""  , targets=__lowerCAmelCase  , top_k=10  )

														# The target list contains duplicates, so we can't output more
														# than them
														self.assertEqual(len(__lowerCAmelCase  )  , 3  )







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )

														UpperCamelCase__				     =			fill_masker(
														    f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""  , top_k=2  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        ],
														        [
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        ],
														        [
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														            {"""sequence""": ANY(__lowerCAmelCase  ), """score""": ANY(__lowerCAmelCase  ), """token""": ANY(__lowerCAmelCase  ), """token_str""": ANY(__lowerCAmelCase  )},
														        ],
														    ]  , )




