



import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				nn.functional.normalize(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				nn.functional.normalize(__SCREAMING_SNAKE_CASE )
		return torch.mm(__SCREAMING_SNAKE_CASE			,     normalized_text_embeds.t() )







class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  CLIPConfig

					lowerCAmelCase_				:    List[Any]									=  ['CLIPEncoderLayer']



					def __init__(			self    ,	lowerCAmelCase     ):
							super().__init__(lowerCAmelCase     )

							UpperCAmelCase_						=				CLIPVisionModel(config.vision_config     )
							UpperCAmelCase_						=				nn.Linear(config.vision_config.hidden_size    ,	config.projection_dim    ,	bias=lowerCAmelCase     )

							UpperCAmelCase_						=				nn.Parameter(torch.ones(17    ,	config.projection_dim     )    ,	requires_grad=lowerCAmelCase     )
							UpperCAmelCase_						=				nn.Parameter(torch.ones(3    ,	config.projection_dim     )    ,	requires_grad=lowerCAmelCase     )

							UpperCAmelCase_						=				nn.Parameter(torch.ones(17     )    ,	requires_grad=lowerCAmelCase     )
							UpperCAmelCase_						=				nn.Parameter(torch.ones(3     )    ,	requires_grad=lowerCAmelCase     )



					@torch.no_grad()
					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.vision_model(lowerCAmelCase     )[1]  # pooled_output
							UpperCAmelCase_						=				self.visual_projection(lowerCAmelCase     )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							UpperCAmelCase_						=				cosine_distance(lowerCAmelCase    ,	self.special_care_embeds     ).cpu().float().numpy()
							UpperCAmelCase_						=				cosine_distance(lowerCAmelCase    ,	self.concept_embeds     ).cpu().float().numpy()

							UpperCAmelCase_						=				[]
							UpperCAmelCase_						=				image_embeds.shape[0]
							for i in range(lowerCAmelCase     ):
									UpperCAmelCase_						=				{"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}

									# increase this value to create a stronger `nfsw` filter
									# at the cost of increasing the possibility of filtering benign images
									UpperCAmelCase_						=				0.0

									for concept_idx in range(len(special_cos_dist[0]     )     ):
											UpperCAmelCase_						=				special_cos_dist[i][concept_idx]
											UpperCAmelCase_						=				self.special_care_embeds_weights[concept_idx].item()
											UpperCAmelCase_						=				round(concept_cos - concept_threshold + adjustment    ,	3     )
											if result_img["special_scores"][concept_idx] > 0:
													result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]}     )
													UpperCAmelCase_						=				0.01

									for concept_idx in range(len(cos_dist[0]     )     ):
											UpperCAmelCase_						=				cos_dist[i][concept_idx]
											UpperCAmelCase_						=				self.concept_embeds_weights[concept_idx].item()
											UpperCAmelCase_						=				round(concept_cos - concept_threshold + adjustment    ,	3     )
											if result_img["concept_scores"][concept_idx] > 0:
													result_img["bad_concepts"].append(lowerCAmelCase     )

									result.append(lowerCAmelCase     )

							UpperCAmelCase_						=				[len(res["bad_concepts"]     ) > 0 for res in result]

							return images, has_nsfw_concepts



					@torch.no_grad()
					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.vision_model(lowerCAmelCase     )[1]  # pooled_output
							UpperCAmelCase_						=				self.visual_projection(lowerCAmelCase     )

							UpperCAmelCase_						=				cosine_distance(lowerCAmelCase    ,	self.special_care_embeds     )
							UpperCAmelCase_						=				cosine_distance(lowerCAmelCase    ,	self.concept_embeds     )

							# increase this value to create a stronger `nsfw` filter
							# at the cost of increasing the possibility of filtering benign images
							UpperCAmelCase_						=				0.0

							UpperCAmelCase_						=				special_cos_dist - self.special_care_embeds_weights + adjustment
							# special_scores = special_scores.round(decimals=3)
							UpperCAmelCase_						=				torch.any(special_scores > 0    ,	dim=1     )
							UpperCAmelCase_						=				special_care * 0.01
							UpperCAmelCase_						=				special_adjustment.unsqueeze(1     ).expand(-1    ,	cos_dist.shape[1]     )

							UpperCAmelCase_						=				(cos_dist - self.concept_embeds_weights) + special_adjustment
							# concept_scores = concept_scores.round(decimals=3)
							UpperCAmelCase_						=				torch.any(concept_scores > 0    ,	dim=1     )

							return images, has_nsfw_concepts






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

