



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 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  MODEL_FOR_MASKED_LM_MAPPING
					lowerCAmelCase_				:    Any									=  TF_MODEL_FOR_MASKED_LM_MAPPING



					def    A__		(			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    A__		(			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-0_5, "token": 3_8015, "token_str": " grouped"},
							        {"sequence": "My name is accuser", "score": 2.1e-0_5, "token": 2_5506, "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-0_5,
							            "token": 3_8015,
							            "token_str": " grouped",
							        },
							        {
							            "sequence": "The largest city in France is accuser",
							            "score": 2.1e-0_5,
							            "token": 2_5506,
							            "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-0_5, "token": 1_3606, "token_str": " Clara"},
							        {"sequence": "My name is Patrick", "score": 2e-0_5, "token": 3499, "token_str": " Patrick"},
							        {"sequence": "My name is Te", "score": 1.9e-0_5, "token": 2941, "token_str": " Te"},
							    ]    ,	)



					@require_torch
					def    A__		(			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-0_5, "token": 3_5676, "token_str": " Maul"},
							        {"sequence": "My name isELS", "score": 2.2e-0_5, "token": 1_6416, "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-0_5,
							            "token": 3_5676,
							            "token_str": " Maul",
							        },
							        {"sequence": "The largest city in France isELS", "score": 2.2e-0_5, "token": 1_6416, "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-0_5, "token": 3499, "token_str": " Patrick"},
							        {"sequence": "My name is Te", "score": 2e-0_5, "token": 2941, "token_str": " Te"},
							        {"sequence": "My name is Clara", "score": 2e-0_5, "token": 1_3606, "token_str": " Clara"},
							    ]    ,	)

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

							self.assertEqual(
							    nested_simplify(lowerCAmelCase    ,	decimals=6     )    ,	[
							        [
							            {
							                "score": 2.2e-0_5,
							                "token": 3_5676,
							                "token_str": " Maul",
							                "sequence": "<s>My name is Maul<mask></s>",
							            },
							            {"score": 2.2e-0_5, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
							        ],
							        [
							            {
							                "score": 2.2e-0_5,
							                "token": 3_5676,
							                "token_str": " Maul",
							                "sequence": "<s>My name is<mask> Maul</s>",
							            },
							            {"score": 2.2e-0_5, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
							        ],
							    ]    ,	)



					@require_torch_gpu
					def    A__		(			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    A__		(			self     ):
							UpperCAmelCase_						=				pipeline(task="fill-mask"    ,	model="distilroberta-base"    ,	top_k=2    ,	framework="pt"     )
							self.run_large_test(lowerCAmelCase     )



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



					def    A__		(			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": 1_2790,
							            "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": 1_3606, "token_str": " Clara"},
							        {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
							    ]    ,	)



					@require_torch
					def    A__		(			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    A__		(			self     ):
							UpperCAmelCase_						=				pipeline(task="fill-mask"    ,	model="sshleifer/tiny-distilroberta-base"    ,	framework="tf"     )
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				None
							self.run_pipeline_test(lowerCAmelCase    ,	[]     )



					def    A__		(			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    A__		(			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    A__		(			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    A__		(			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    A__		(			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    A__		(			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    A__		(			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     )},
							        ],
							    ]    ,	)






import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							np.array:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		UpperCAmelCase_						=				"f32le"
		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-i",
		    "pipe:0",
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]

		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdin=subprocess.PIPE			,     stdout=subprocess.PIPE ) as ffmpeg_process:
						UpperCAmelCase_						=				ffmpeg_process.communicate(__SCREAMING_SNAKE_CASE )
		except FileNotFoundError as error:
				raise ValueError("ffmpeg was not found but is required to load audio files from filename" ) from error
		UpperCAmelCase_						=				output_stream[0]
		UpperCAmelCase_						=				np.frombuffer(__SCREAMING_SNAKE_CASE			,     np.floataa )
		if audio.shape[0] == 0:
				raise ValueError("Malformed soundfile" )
		return audio







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							Dict:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		UpperCAmelCase_						=				platform.system()
		if system == "Linux":
				UpperCAmelCase_						=				"alsa"
				UpperCAmelCase_						=				"default"
		elif system == "Darwin":
				UpperCAmelCase_						=				"avfoundation"
				UpperCAmelCase_						=				":0"
		elif system == "Windows":
				UpperCAmelCase_						=				"dshow"
				UpperCAmelCase_						=				"default"

		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-f",
		    format_,
		    "-i",
		    input_,
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-fflags",
		    "nobuffer",
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		UpperCAmelCase_						=				_ffmpeg_stream(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		for item in iterator:
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							int:
		if stream_chunk_s is not None:
				UpperCAmelCase_						=				stream_chunk_s
		else:
				UpperCAmelCase_						=				chunk_length_s

		UpperCAmelCase_						=				ffmpeg_microphone(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     format_for_conversion=__SCREAMING_SNAKE_CASE )
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				np.intaa
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				np.floataa
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		if stride_length_s is None:
				UpperCAmelCase_						=				chunk_length_s / 6
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		if isinstance(__SCREAMING_SNAKE_CASE			,     (int, float) ):
				UpperCAmelCase_						=				[stride_length_s, stride_length_s]

		UpperCAmelCase_						=				int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
		UpperCAmelCase_						=				int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
		UpperCAmelCase_						=				datetime.datetime.now()
		UpperCAmelCase_						=				datetime.timedelta(seconds=__SCREAMING_SNAKE_CASE )
		for item in chunk_bytes_iter(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     stride=(stride_left, stride_right)			,     stream=__SCREAMING_SNAKE_CASE ):
				# Put everything back in numpy scale
				UpperCAmelCase_						=				np.frombuffer(item["raw"]			,     dtype=__SCREAMING_SNAKE_CASE )
				UpperCAmelCase_						=				(
				    item["stride"][0] // size_of_sample,
				    item["stride"][1] // size_of_sample,
				)
				UpperCAmelCase_						=				sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False )		->							Dict:
		UpperCAmelCase_						=				B""
		UpperCAmelCase_					,						UpperCAmelCase_						=				stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}''' )
		UpperCAmelCase_						=				0
		for raw in iterator:
				acc += raw
				if stream and len(__SCREAMING_SNAKE_CASE ) < chunk_len:
						UpperCAmelCase_						=				(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(__SCREAMING_SNAKE_CASE ) >= chunk_len:
								# We are flushing the accumulator
								UpperCAmelCase_						=				(_stride_left, stride_right)
								UpperCAmelCase_						=				{"raw": acc[:chunk_len], "stride": stride}
								if stream:
										UpperCAmelCase_						=				False
								yield item
								UpperCAmelCase_						=				stride_left
								UpperCAmelCase_						=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(__SCREAMING_SNAKE_CASE ) > stride_left:
				UpperCAmelCase_						=				{"raw": acc, "stride": (_stride_left, 0)}
				if stream:
						UpperCAmelCase_						=				False
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				2**24  # 16Mo
		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdout=subprocess.PIPE			,     bufsize=__SCREAMING_SNAKE_CASE ) as ffmpeg_process:
						while True:
								UpperCAmelCase_						=				ffmpeg_process.stdout.read(__SCREAMING_SNAKE_CASE )
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError("ffmpeg was not found but is required to stream audio files from filename" ) from error

