from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
				from ..tf_utils import stable_softmax

__A  =				logging.get_logger(__name__)
@add_end_docstrings(a__		)
class  _snake_case     (					a__		):






						def __init__(       self      :			Tuple							,					**UpperCAmelCase      :			Optional[int]    ):
													super().__init__(**UpperCAmelCase    )

													requires_backends(self							,					"vision"    )
													self.check_model_type(
													    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
													    if self.framework == "tf"
													    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )






						def __call__(       self      :			Dict							,					UpperCAmelCase      :			Union[str, List[str], "Image", List["Image"]]							,					**UpperCAmelCase      :			Union[str, Any]    ):
													return super().__call__(UpperCAmelCase							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					**UpperCAmelCase      :			Union[str, Any]    ):
													__lowerCamelCase					:		Optional[int]		   =     {}
													if "candidate_labels" in kwargs:
																				__lowerCamelCase					:		Dict		   =     kwargs["candidate_labels"]
													if "hypothesis_template" in kwargs:
																				__lowerCamelCase					:		List[str]		   =     kwargs["hypothesis_template"]

													return preprocess_params, {}, {}






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			Union[str, Any]="This is a photo of {}."    ):
													__lowerCamelCase					:		Optional[Any]		   =     load_image(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.image_processor(images=[image]							,					return_tensors=self.framework    )
													__lowerCamelCase					:		str		   =     candidate_labels
													__lowerCamelCase					:		Tuple		   =     [hypothesis_template.format(UpperCAmelCase    ) for x in candidate_labels]
													__lowerCamelCase					:		Dict		   =     self.tokenizer(UpperCAmelCase							,					return_tensors=self.framework							,					padding=UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     [text_inputs]
													return inputs






						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Dict    ):
													__lowerCamelCase					:		Optional[Any]		   =     model_inputs.pop("candidate_labels"    )
													__lowerCamelCase					:		Union[str, Any]		   =     model_inputs.pop("text_inputs"    )
													if isinstance(text_inputs[0]							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		int		   =     text_inputs[0]
													else:
																				# Batching case.
																				__lowerCamelCase					:		Tuple		   =     text_inputs[0][0]

													__lowerCamelCase					:		Optional[Any]		   =     self.model(**UpperCAmelCase							,					**UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     {
													    "candidate_labels": candidate_labels,
													    "logits": outputs.logits_per_image,
													}
													return model_outputs







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			List[str]    ):
													__lowerCamelCase					:		str		   =     model_outputs.pop("candidate_labels"    )
													__lowerCamelCase					:		Union[str, Any]		   =     model_outputs["logits"][0]
													if self.framework == "pt":
																				__lowerCamelCase					:		int		   =     logits.softmax(dim=-1    ).squeeze(-1    )
																				__lowerCamelCase					:		Tuple		   =     probs.tolist()
																				if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																											__lowerCamelCase					:		str		   =     [scores]
													elif self.framework == "tf":
																				__lowerCamelCase					:		List[Any]		   =     stable_softmax(UpperCAmelCase							,					axis=-1    )
																				__lowerCamelCase					:		str		   =     probs.numpy().tolist()
													else:
																				raise ValueError(F"""Unsupported framework: {self.framework}"""    )

													__lowerCamelCase					:		str		   =     [
													    {"score": score, "label": candidate_label}
													    for score, candidate_label in sorted(zip(UpperCAmelCase							,					UpperCAmelCase    )							,					key=lambda UpperCAmelCase    : -x[0]    )
													]
													return result
"""simple docstring"""





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class  _snake_case     (					a__				, a__		):






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 128							,					UpperCAmelCase      :			int = 256							,					UpperCAmelCase      :			float = 2_0_0_0.0							,					UpperCAmelCase      :			int = 768							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			int = 2048							,					UpperCAmelCase      :			float = 0.1							,					):
													super().__init__()

													__lowerCamelCase					:		List[Any]		   =     nn.Sequential(
													    nn.Linear(UpperCAmelCase							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					nn.Linear(d_model * 4							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					)

													__lowerCamelCase					:		str		   =     nn.Embedding(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     False

													__lowerCamelCase					:		Optional[int]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(p=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     nn.ModuleList()
													for lyr_num in range(UpperCAmelCase    ):
																				# FiLM conditional T5 decoder
																				__lowerCamelCase					:		List[str]		   =     DecoderLayer(d_model=UpperCAmelCase							,					d_kv=UpperCAmelCase							,					num_heads=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )
																				self.decoders.append(UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     nn.Dropout(p=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		List[Any]		   =     torch.mul(query_input.unsqueeze(-1    )							,					key_input.unsqueeze(-2    )    )
													return mask.unsqueeze(-3    )







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     decoder_input_tokens.shape
													assert decoder_noise_time.shape == (batch,)

													# decoder_noise_time is in [0, 1), so rescale to expected timing range.
													__lowerCamelCase					:		str		   =     get_timestep_embedding(
													    decoder_noise_time * self.config.max_decoder_noise_time							,					embedding_dim=self.config.d_model							,					max_period=self.config.max_decoder_noise_time							,					).to(dtype=self.dtype    )

													__lowerCamelCase					:		Optional[int]		   =     self.conditioning_emb(UpperCAmelCase    ).unsqueeze(1    )

													assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

													__lowerCamelCase					:		Tuple		   =     decoder_input_tokens.shape[1]

													# If we want to use relative positions for audio context, we can just offset
													# this sequence by the length of encodings_and_masks.
													__lowerCamelCase					:		int		   =     torch.broadcast_to(
													    torch.arange(UpperCAmelCase							,					device=decoder_input_tokens.device    )							,					(batch, seq_length)							,					)

													__lowerCamelCase					:		Optional[Any]		   =     self.position_encoding(UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.continuous_inputs_projection(UpperCAmelCase    )
													inputs += position_encodings
													__lowerCamelCase					:		List[Any]		   =     self.dropout(UpperCAmelCase    )

													# decoder: No padding present.
													__lowerCamelCase					:		Tuple		   =     torch.ones(
													    decoder_input_tokens.shape[:2]							,					device=decoder_input_tokens.device							,					dtype=inputs.dtype    )

													# Translate encoding masks to encoder-decoder masks.
													__lowerCamelCase					:		Optional[Any]		   =     [(x, self.encoder_decoder_mask(UpperCAmelCase							,					UpperCAmelCase    )) for x, y in encodings_and_masks]

													# cross attend style: concat encodings
													__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([x[0] for x in encodings_and_encdec_masks]							,					dim=1    )
													__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([x[1] for x in encodings_and_encdec_masks]							,					dim=-1    )

													for lyr in self.decoders:
																				__lowerCamelCase					:		List[Any]		   =     lyr(
																				    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					)[0]

													__lowerCamelCase					:		Dict		   =     self.decoder_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.post_dropout(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     self.spec_out(UpperCAmelCase    )
													return spec_out

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     nn.ModuleList()

													# cond self attention: layer 0
													self.layer.append(
													    TaLayerSelfAttentionCond(d_model=UpperCAmelCase							,					d_kv=UpperCAmelCase							,					num_heads=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )    )

													# cross attention: layer 1
													self.layer.append(
													    TaLayerCrossAttention(
													        d_model=UpperCAmelCase							,					d_kv=UpperCAmelCase							,					num_heads=UpperCAmelCase							,					dropout_rate=UpperCAmelCase							,					layer_norm_epsilon=UpperCAmelCase							,					)    )

													# Film Cond MLP + dropout: last layer
													self.layer.append(
													    TaLayerFFCond(d_model=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase							,					layer_norm_epsilon=UpperCAmelCase    )    )







						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple=None							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			Dict=None							,					):
													__lowerCamelCase					:		Union[str, Any]		   =     self.layer[0](
													    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													if encoder_hidden_states is not None:
																				__lowerCamelCase					:		Tuple		   =     torch.where(encoder_attention_mask > 0							,					0							,					-1E10    ).to(
																				    encoder_hidden_states.dtype    )

																				__lowerCamelCase					:		Dict		   =     self.layer[1](
																				    UpperCAmelCase							,					key_value_states=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													# Apply Film Conditional Feed Forward layer
													__lowerCamelCase					:		List[Any]		   =     self.layer[-1](UpperCAmelCase							,					UpperCAmelCase    )

													return (hidden_states,)

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Dict    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     TaFiLMLayer(in_features=d_model * 4							,					out_features=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     Attention(query_dim=UpperCAmelCase							,					heads=UpperCAmelCase							,					dim_head=UpperCAmelCase							,					out_bias=UpperCAmelCase							,					scale_qk=UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			str=None							,					):
													# pre_self_attention_layer_norm
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )

													if conditioning_emb is not None:
																				__lowerCamelCase					:		Optional[Any]		   =     self.FiLMLayer(UpperCAmelCase							,					UpperCAmelCase    )

													# Self-attention block
													__lowerCamelCase					:		Optional[Any]		   =     self.attention(UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     hidden_states + self.dropout(UpperCAmelCase    )

													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     Attention(query_dim=UpperCAmelCase							,					heads=UpperCAmelCase							,					dim_head=UpperCAmelCase							,					out_bias=UpperCAmelCase							,					scale_qk=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			Optional[int]=None							,					):
													__lowerCamelCase					:		str		   =     self.layer_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     self.attention(
													    UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					attention_mask=attention_mask.squeeze(1    )							,					)
													__lowerCamelCase					:		List[str]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return layer_output

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     TaDenseGatedActDense(d_model=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     TaFiLMLayer(in_features=d_model * 4							,					out_features=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any=None    ):
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )
													if conditioning_emb is not None:
																				__lowerCamelCase					:		Union[str, Any]		   =     self.film(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.DenseReluDense(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     NewGELUActivation()







						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.act(self.wi_a(UpperCAmelCase    )    )
													__lowerCamelCase					:		Any		   =     self.wi_a(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     hidden_gelu * hidden_linear
													__lowerCamelCase					:		Any		   =     self.dropout(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     self.wo(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Parameter(torch.ones(UpperCAmelCase    )    )
													__lowerCamelCase					:		Tuple		   =     eps







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Any    ):
													# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
													# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
													# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
													# half-precision inputs is done in fp32

													__lowerCamelCase					:		int		   =     hidden_states.to(torch.floataa    ).pow(2    ).mean(-1							,					keepdim=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

													# convert into half-precision if necessary
													if self.weight.dtype in [torch.floataa, torch.bfloataa]:
																				__lowerCamelCase					:		Union[str, Any]		   =     hidden_states.to(self.weight.dtype    )

													return self.weight * hidden_states

class  _snake_case     (					nn.Module		):






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			torch.Tensor    ):
													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.0_4_4_7_1_5 * torch.pow(UpperCAmelCase							,					3.0    ))    ))

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]    ):
													super().__init__()
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					out_features * 2							,					bias=UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.scale_bias(UpperCAmelCase    )
													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     torch.chunk(UpperCAmelCase							,					2							,					-1    )
													__lowerCamelCase					:		List[Any]		   =     x * (1 + scale) + shift
													return x