


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   __a      (   unittest.TestCase				):
 __lowercase			:							Union[str, Any]       					=			MODEL_FOR_MASKED_LM_MAPPING
 __lowercase			:							Union[str, Any]       					=			TF_MODEL_FOR_MASKED_LM_MAPPING
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[Any]        =       pipeline(task='fill-mask'    ,			model='sshleifer/tiny-distilroberta-base'    ,			top_k=2    ,			framework='tf'			)
        lowercase__:       Optional[Any]        =       unmasker('My name is <mask>'			)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__    ,			decimals=6			)    ,			[
                {'sequence': 'My name is grouped', 'score': 2.1E-05, 'token': 38_015, 'token_str': ' grouped'},
                {'sequence': 'My name is accuser', 'score': 2.1E-05, 'token': 25_506, 'token_str': ' accuser'},
            ]    ,			)

        lowercase__:       Any        =       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': 38_015,
                    'token_str': ' grouped',
                },
                {
                    'sequence': 'The largest city in France is accuser',
                    'score': 2.1E-05,
                    'token': 25_506,
                    'token_str': ' accuser',
                },
            ]    ,			)

        lowercase__:       List[Any]        =       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': 13_606, 'token_str': ' Clara'},
                {'sequence': 'My name is Patrick', 'score': 2E-05, 'token': 3_499, 'token_str': ' Patrick'},
                {'sequence': 'My name is Te', 'score': 1.9E-05, 'token': 2_941, 'token_str': ' Te'},
            ]    ,			)
 @require_torch
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       pipeline(task='fill-mask'    ,			model='sshleifer/tiny-distilroberta-base'    ,			top_k=2    ,			framework='pt'			)

        lowercase__:       Dict        =       unmasker('My name is <mask>'			)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__    ,			decimals=6			)    ,			[
                {'sequence': 'My name is Maul', 'score': 2.2E-05, 'token': 35_676, 'token_str': ' Maul'},
                {'sequence': 'My name isELS', 'score': 2.2E-05, 'token': 16_416, 'token_str': 'ELS'},
            ]    ,			)

        lowercase__:       Optional[int]        =       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': 35_676,
                    'token_str': ' Maul',
                },
                {'sequence': 'The largest city in France isELS', 'score': 2.2E-05, 'token': 16_416, 'token_str': 'ELS'},
            ]    ,			)

        lowercase__:       str        =       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': 3_499, 'token_str': ' Patrick'},
                {'sequence': 'My name is Te', 'score': 2E-05, 'token': 2_941, 'token_str': ' Te'},
                {'sequence': 'My name is Clara', 'score': 2E-05, 'token': 13_606, 'token_str': ' Clara'},
            ]    ,			)

        lowercase__:       Union[str, Any]        =       unmasker('My name is <mask> <mask>'    ,			top_k=2			)

        self.assertEqual(
            nested_simplify(lowerCAmelCase__    ,			decimals=6			)    ,			[
                [
                    {
                        'score': 2.2E-05,
                        'token': 35_676,
                        'token_str': ' Maul',
                        'sequence': '<s>My name is Maul<mask></s>',
                    },
                    {'score': 2.2E-05, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
                ],
                [
                    {
                        'score': 2.2E-05,
                        'token': 35_676,
                        'token_str': ' Maul',
                        'sequence': '<s>My name is<mask> Maul</s>',
                    },
                    {'score': 2.2E-05, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
                ],
            ]    ,			)
 @require_torch_gpu
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       int        =       pipeline('fill-mask'    ,			model='hf-internal-testing/tiny-random-distilbert'    ,			device=0    ,			framework='pt'			)

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

        lowercase__:       List[str]        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       int        =       pipeline(task='fill-mask'    ,			model='distilroberta-base'    ,			top_k=2    ,			framework='pt'			)
        self.run_large_test(lowerCAmelCase__			)
 @slow
 @require_tf
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Tuple        =       pipeline(task='fill-mask'    ,			model='distilroberta-base'    ,			top_k=2    ,			framework='tf'			)
        self.run_large_test(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[Any]        =       unmasker('My name is <mask>'			)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__			)    ,			[
                {'sequence': 'My name is John', 'score': 0.0_0_8, 'token': 610, 'token_str': ' John'},
                {'sequence': 'My name is Chris', 'score': 0.0_0_7, 'token': 1_573, 'token_str': ' Chris'},
            ]    ,			)
        lowercase__:       str        =       unmasker('The largest city in France is <mask>'			)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__			)    ,			[
                {
                    'sequence': 'The largest city in France is Paris',
                    'score': 0.2_5_1,
                    'token': 2_201,
                    'token_str': ' Paris',
                },
                {
                    'sequence': 'The largest city in France is Lyon',
                    'score': 0.2_1_4,
                    'token': 12_790,
                    'token_str': ' Lyon',
                },
            ]    ,			)

        lowercase__:       List[Any]        =       unmasker('My name is <mask>'    ,			targets=[' Patrick', ' Clara', ' Teven']    ,			top_k=3			)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__			)    ,			[
                {'sequence': 'My name is Patrick', 'score': 0.0_0_5, 'token': 3_499, 'token_str': ' Patrick'},
                {'sequence': 'My name is Clara', 'score': 0.0_0_0, 'token': 13_606, 'token_str': ' Clara'},
                {'sequence': 'My name is Te', 'score': 0.0_0_0, 'token': 2_941, 'token_str': ' Te'},
            ]    ,			)
 @require_torch
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       pipeline(task='fill-mask'    ,			model='sshleifer/tiny-distilroberta-base'    ,			framework='pt'			)
        lowercase__:       str        =       None
        lowercase__:       Optional[int]        =       None
        self.run_pipeline_test(lowerCAmelCase__    ,			[]			)
 @require_tf
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       Dict        =       pipeline(task='fill-mask'    ,			model='sshleifer/tiny-distilroberta-base'    ,			framework='tf'			)
        lowercase__:       str        =       None
        lowercase__:       Optional[Any]        =       None
        self.run_pipeline_test(lowerCAmelCase__    ,			[]			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        if tokenizer is None or tokenizer.mask_token_id is None:
               self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)'			)

        lowercase__:       Any        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__			)
        lowercase__:       str        =       [
            F'This is another {tokenizer.mask_token} test',
        ]
        return fill_masker, examples
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       str        =       fill_masker.tokenizer
        lowercase__:       List[Any]        =       fill_masker.model

        lowercase__:       Any        =       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__			)},
            ]    ,			)

        lowercase__:       Optional[Any]        =       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__			)},
            ]    ,			)

        lowercase__:       Tuple        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        lowercase__:       List[str]        =       tokenizer.get_vocab()
        lowercase__:       int        =       sorted(vocab.keys()			)[:2]
        # Pipeline argument
        lowercase__:       Optional[int]        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__    ,			targets=lowerCAmelCase__			)
        lowercase__:       Optional[Any]        =       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__			)},
            ]    ,			)
        lowercase__:       int        =       {vocab[el] for el in targets}
        self.assertEqual({el['token'] for el in outputs}    ,			lowerCAmelCase__			)
        lowercase__:       Tuple        =       [tokenizer.decode([x]			) for x in target_ids]
        self.assertEqual({el['token_str'] for el in outputs}    ,			set(lowerCAmelCase__			)			)

        # Call argument
        lowercase__:       Optional[Any]        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__			)
        lowercase__:       Any        =       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__			)},
            ]    ,			)
        lowercase__:       List[str]        =       {vocab[el] for el in targets}
        self.assertEqual({el['token'] for el in outputs}    ,			lowerCAmelCase__			)
        lowercase__:       str        =       [tokenizer.decode([x]			) for x in target_ids]
        self.assertEqual({el['token_str'] for el in outputs}    ,			set(lowerCAmelCase__			)			)

        # Score equivalence
        lowercase__:       List[str]        =       fill_masker(F'This is a {tokenizer.mask_token}'    ,			targets=lowerCAmelCase__			)
        lowercase__:       int        =       [top_mask['token_str'] for top_mask in outputs]
        lowercase__:       Dict        =       [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__			):
               lowercase__:       Union[str, Any]        =       fill_masker(F'This is a {tokenizer.mask_token}'    ,			targets=lowerCAmelCase__			)
               lowercase__:       Tuple        =       [top_mask['score'] for top_mask in unmasked_targets]
               self.assertEqual(nested_simplify(lowerCAmelCase__			)    ,			nested_simplify(lowerCAmelCase__			)			)

        # Raises with invalid
        with self.assertRaises(lowerCAmelCase__			):
               lowercase__:       Optional[int]        =       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__			):
                      lowercase__:       Optional[Any]        =       fill_masker(F'This is a {tokenizer.mask_token}'    ,			targets=['']			)
               with self.assertRaises(lowerCAmelCase__			):
                      lowercase__:       Optional[int]        =       fill_masker(F'This is a {tokenizer.mask_token}'    ,			targets=''			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__    ,			top_k=2			)
        lowercase__:       List[str]        =       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__			)},
            ]    ,			)

        lowercase__:       Optional[int]        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__			)
        lowercase__:       Dict        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       List[str]        =       tokenizer.get_vocab()
        lowercase__:       int        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__			)

        # top_k=2, ntargets=3
        lowercase__:       List[Any]        =       sorted(vocab.keys()			)[:3]
        lowercase__:       Dict        =       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
        lowercase__:       int        =       [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__			):
               lowercase__:       int        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        lowercase__:       int        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       tokenizer.get_vocab()
        # String duplicates + id duplicates
        lowercase__:       Union[str, Any]        =       sorted(vocab.keys()			)[:3]
        lowercase__:       Optional[int]        =       [targets[0], targets[1], targets[0], targets[2], targets[1]]
        lowercase__:       Tuple        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       FillMaskPipeline(model=lowerCAmelCase__    ,			tokenizer=lowerCAmelCase__			)

        lowercase__:       List[str]        =       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__			)},
                ],
            ]    ,			)






import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


__lowerCAmelCase			=							'''base_with_context'''




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       lowercase__:       Tuple        =       nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']					)					)
       lowercase__:       Optional[int]        =       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)      ,							requires_grad=snake_case					)
       for lyr_num, lyr in enumerate(model.encoders					):
              lowercase__:       List[str]        =       weights[f'layers_{lyr_num}']
              lowercase__:       List[Any]        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

              lowercase__:       Any        =       ly_weight['attention']
              lowercase__:       int        =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
              lowercase__:       int        =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

              lowercase__:       List[Any]        =       nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

              lowercase__:       Optional[Any]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

       lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)
       return model




def        snake_case_					(  snake_case      ,							snake_case					)  ->    List[str]:
       lowercase__:       str        =       nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

       lowercase__:       Dict        =       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)      ,							requires_grad=snake_case					)

       for lyr_num, lyr in enumerate(model.encoders					):
              lowercase__:       str        =       weights[f'layers_{lyr_num}']
              lowercase__:       Optional[Any]        =       ly_weight['attention']

              lowercase__:       List[Any]        =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
              lowercase__:       int        =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
              lowercase__:       Union[str, Any]        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              lowercase__:       Tuple        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)
              lowercase__:       Optional[int]        =       nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

       lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)

       return model




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       int        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
       lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

       lowercase__:       int        =       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)      ,							requires_grad=snake_case					)

       lowercase__:       Dict        =       nn.Parameter(
           torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T					)					)

       for lyr_num, lyr in enumerate(model.decoders					):
              lowercase__:       Optional[Any]        =       weights[f'layers_{lyr_num}']
              lowercase__:       Any        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']					)					)

              lowercase__:       int        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T					)					)

              lowercase__:       List[str]        =       ly_weight['self_attention']
              lowercase__:       Optional[int]        =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
              lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
              lowercase__:       Tuple        =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
              lowercase__:       Union[str, Any]        =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

              lowercase__:       int        =       ly_weight['MultiHeadDotProductAttention_0']
              lowercase__:       List[Any]        =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
              lowercase__:       str        =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
              lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
              lowercase__:       int        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']					)					)

              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)
              lowercase__:       Union[str, Any]        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T					)					)
              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              lowercase__:       int        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              lowercase__:       str        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

       lowercase__:       Optional[Any]        =       nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']					)					)

       lowercase__:       Union[str, Any]        =       nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T					)					)

       return model




def        snake_case_					(  snake_case					)  ->    Any:
       lowercase__:       int        =       checkpoints.load_tax_checkpoint(args.checkpoint_path					)
       lowercase__:       Tuple        =       jnp.tree_util.tree_map(onp.array      ,							snake_case					)

       lowercase__:       List[str]        =       [
           'from __gin__ import dynamic_registration',
           'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
           'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
           'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
       ]

       lowercase__:       List[Any]        =       os.path.join(args.checkpoint_path      ,							'..'      ,							'config.gin'					)
       lowercase__:       Optional[Any]        =       inference.parse_training_gin_file(snake_case      ,							snake_case					)
       lowercase__:       str        =       inference.InferenceModel(args.checkpoint_path      ,							snake_case					)

       lowercase__:       Dict        =       DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,							variance_type='fixed_large'					)

       lowercase__:       List[Any]        =       SpectrogramNotesEncoder(
           max_length=synth_model.sequence_length['inputs']      ,							vocab_size=synth_model.model.module.config.vocab_size      ,							d_model=synth_model.model.module.config.emb_dim      ,							dropout_rate=synth_model.model.module.config.dropout_rate      ,							num_layers=synth_model.model.module.config.num_encoder_layers      ,							num_heads=synth_model.model.module.config.num_heads      ,							d_kv=synth_model.model.module.config.head_dim      ,							d_ff=synth_model.model.module.config.mlp_dim      ,							feed_forward_proj='gated-gelu'      ,							)

       lowercase__:       Dict        =       SpectrogramContEncoder(
           input_dims=synth_model.audio_codec.n_dims      ,							targets_context_length=synth_model.sequence_length['targets_context']      ,							d_model=synth_model.model.module.config.emb_dim      ,							dropout_rate=synth_model.model.module.config.dropout_rate      ,							num_layers=synth_model.model.module.config.num_encoder_layers      ,							num_heads=synth_model.model.module.config.num_heads      ,							d_kv=synth_model.model.module.config.head_dim      ,							d_ff=synth_model.model.module.config.mlp_dim      ,							feed_forward_proj='gated-gelu'      ,							)

       lowercase__:       Optional[Any]        =       TaFilmDecoder(
           input_dims=synth_model.audio_codec.n_dims      ,							targets_length=synth_model.sequence_length['targets_context']      ,							max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time      ,							d_model=synth_model.model.module.config.emb_dim      ,							num_layers=synth_model.model.module.config.num_decoder_layers      ,							num_heads=synth_model.model.module.config.num_heads      ,							d_kv=synth_model.model.module.config.head_dim      ,							d_ff=synth_model.model.module.config.mlp_dim      ,							dropout_rate=synth_model.model.module.config.dropout_rate      ,							)

       lowercase__:       Dict        =       load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,							snake_case					)
       lowercase__:       int        =       load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,							snake_case					)
       lowercase__:       Optional[int]        =       load_decoder(ta_checkpoint['target']['decoder']      ,							snake_case					)

       lowercase__:       int        =       OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'					)

       lowercase__:       List[Any]        =       SpectrogramDiffusionPipeline(
           notes_encoder=snake_case      ,							continuous_encoder=snake_case      ,							decoder=snake_case      ,							scheduler=snake_case      ,							melgan=snake_case      ,							)
       if args.save:
              pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
 __lowerCAmelCase			=							argparse.ArgumentParser()

 parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
 parser.add_argument(
     '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
 )
 parser.add_argument(
     '''--checkpoint_path''',
     default=F'''{MODEL}/checkpoint_500000''',
     type=str,
     required=False,
     help='''Path to the original jax model checkpoint.''',
 )
 __lowerCAmelCase			=							parser.parse_args()

 main(args)


