

from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =42






class _UpperCamelCase				(				__A				,						__A ):
			'''simple docstring'''







			@register_to_config
			def __init__(      self				:			Any   ,   a				:			int = 32   ,   a				:			int = 64   ,   a				:			int = 20   ,   a				:			int = 768   ,   a				:			int=77   ,   a				:			str=4   ,   a				:			float = 0.0   ,   a				:			str = "silu"   ,   a				:			Optional[str] = None   ,   a				:			Optional[str] = None   ,   a				:			Optional[str] = "linear"   ,   a				:			Optional[str] = "prd"   ,   a				:			Optional[int] = None   ,   a				:			Optional[int] = None   ,   a				:			Optional[int] = None   ,   )				->							int:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							attention_head_dim
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_attention_heads * attention_head_dim
				SCREAMING_SNAKE_CASE :   Any					  =							additional_embeddings

				SCREAMING_SNAKE_CASE :   int					  =							time_embed_dim or inner_dim
				SCREAMING_SNAKE_CASE :   List[Any]					  =							embedding_proj_dim or embedding_dim
				SCREAMING_SNAKE_CASE :   Any					  =							clip_embed_dim or embedding_dim

				SCREAMING_SNAKE_CASE :   Any					  =							Timesteps(a   ,   a   ,   0						)
				SCREAMING_SNAKE_CASE :   Dict					  =							TimestepEmbedding(a   ,   a   ,   out_dim=a   ,   act_fn=a						)

				SCREAMING_SNAKE_CASE :   str					  =							nn.Linear(a   ,   a						)

				if embedding_proj_norm_type is None:
					SCREAMING_SNAKE_CASE :   Dict					  =							None
				elif embedding_proj_norm_type == "layer":
					SCREAMING_SNAKE_CASE :   Tuple					  =							nn.LayerNorm(a						)
				else:
					raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"						)

				SCREAMING_SNAKE_CASE :   str					  =							nn.Linear(a   ,   a						)

				if encoder_hid_proj_type is None:
					SCREAMING_SNAKE_CASE :   Tuple					  =							None
				elif encoder_hid_proj_type == "linear":
					SCREAMING_SNAKE_CASE :   Any					  =							nn.Linear(a   ,   a						)
				else:
					raise ValueError(F"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"						)

				SCREAMING_SNAKE_CASE :   Any					  =							nn.Parameter(torch.zeros(1   ,   num_embeddings + additional_embeddings   ,   a						)						)

				if added_emb_type == "prd":
					SCREAMING_SNAKE_CASE :   Tuple					  =							nn.Parameter(torch.zeros(1   ,   1   ,   a						)						)
				elif added_emb_type is None:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							None
				else:
					raise ValueError(
					    F"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."						)

				SCREAMING_SNAKE_CASE :   Dict					  =							nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            a   ,   a   ,   a   ,   dropout=a   ,   activation_fn="gelu"   ,   attention_bias=a   ,   )
				        for d in range(a						)
				    ]						)

				if norm_in_type == "layer":
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							nn.LayerNorm(a						)
				elif norm_in_type is None:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							None
				else:
					raise ValueError(F"Unsupported norm_in_type: {norm_in_type}."						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							nn.LayerNorm(a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							nn.Linear(a   ,   a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.full(
				    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]   ,   -1_0000.0						)
				causal_attention_mask.triu_(1						)
				SCREAMING_SNAKE_CASE :   Dict					  =							causal_attention_mask[None, ...]
				self.register_buffer("causal_attention_mask"   ,   a   ,   persistent=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							nn.Parameter(torch.zeros(1   ,   a						)						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							nn.Parameter(torch.zeros(1   ,   a						)						)
			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Dict[str, AttentionProcessor]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							{}

				def fn_recursive_add_processors(a				:			str   ,   a				:			torch.nn.Module   ,   a				:			Dict[str, AttentionProcessor]						):
					if hasattr(a   ,   "set_processor"						):
						SCREAMING_SNAKE_CASE :   int					  =							module.processor

					for sub_name, child in module.named_children():
						fn_recursive_add_processors(F"{name}.{sub_name}"   ,   a   ,   a						)

					return processors

				for name, module in self.named_children():
					fn_recursive_add_processors(a   ,   a   ,   a						)

				return processors
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Union[AttentionProcessor, Dict[str, AttentionProcessor]]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							len(self.attn_processors.keys()						)

				if isinstance(a   ,   a						) and len(a						) != count:
					raise ValueError(
					    F"A dict of processors was passed, but the number of processors {len(a						)} does not match the"
					    F" number of attention layers: {count}. Please make sure to pass {count} processor classes."						)

				def fn_recursive_attn_processor(a				:			str   ,   a				:			torch.nn.Module   ,   a				:			int						):
					if hasattr(a   ,   "set_processor"						):
						if not isinstance(a   ,   a						):
							module.set_processor(a						)
						else:
							module.set_processor(processor.pop(F"{name}.processor"						)						)

					for sub_name, child in module.named_children():
						fn_recursive_attn_processor(F"{name}.{sub_name}"   ,   a   ,   a						)

				for name, module in self.named_children():
					fn_recursive_attn_processor(a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							str:

				"""simple docstring"""

				self.set_attn_processor(AttnProcessor()						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Union[str, Any]   ,   a				:			Union[torch.Tensor, float, int]   ,   a				:			torch.FloatTensor   ,   a				:			Optional[torch.FloatTensor] = None   ,   a				:			Optional[torch.BoolTensor] = None   ,   a				:			bool = True   ,   )				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_states.shape[0]

				SCREAMING_SNAKE_CASE :   Any					  =							timestep
				if not torch.is_tensor(a						):
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.tensor([timesteps]   ,   dtype=torch.long   ,   device=hidden_states.device						)
				elif torch.is_tensor(a						) and len(timesteps.shape						) == 0:
					SCREAMING_SNAKE_CASE :   List[str]					  =							timesteps[None].to(hidden_states.device						)

				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							timesteps * torch.ones(a   ,   dtype=timesteps.dtype   ,   device=timesteps.device						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.time_proj(a						)

				# timesteps does not contain any weights and will always return f32 tensors
				# but time_embedding might be fp16, so we need to cast here.
				SCREAMING_SNAKE_CASE :   List[Any]					  =							timesteps_projected.to(dtype=self.dtype						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.time_embedding(a						)

				if self.embedding_proj_norm is not None:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self.embedding_proj_norm(a						)

				SCREAMING_SNAKE_CASE :   str					  =							self.embedding_proj(a						)
				if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.encoder_hidden_states_proj(a						)
				elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
					raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.proj_in(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.positional_embedding.to(hidden_states.dtype						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							0

				if encoder_hidden_states is not None:
					additional_embeds.append(a						)
					additional_embeddings_len += encoder_hidden_states.shape[1]

				if len(proj_embeddings.shape						) == 2:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							proj_embeddings[:, None, :]

				if len(hidden_states.shape						) == 2:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_states[:, None, :]

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							additional_embeds + [
				    proj_embeddings,
				    time_embeddings[:, None, :],
				    hidden_states,
				]

				if self.prd_embedding is not None:
					SCREAMING_SNAKE_CASE :   Any					  =							self.prd_embedding.to(hidden_states.dtype						).expand(a   ,   -1   ,   -1						)
					additional_embeds.append(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.cat(
				    a   ,   dim=1   ,   )

				# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
				SCREAMING_SNAKE_CASE :   str					  =							additional_embeddings_len + proj_embeddings.shape[1] + 1
				if positional_embeddings.shape[1] < hidden_states.shape[1]:
					SCREAMING_SNAKE_CASE :   Any					  =							F.pad(
					    a   ,   (
					        0,
					        0,
					        additional_embeddings_len,
					        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
					    )   ,   value=0.0   ,   )

				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_states + positional_embeddings

				if attention_mask is not None:
					SCREAMING_SNAKE_CASE :   int					  =							(1 - attention_mask.to(hidden_states.dtype						)) * -1_0000.0
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							F.pad(a   ,   (0, self.additional_embeddings)   ,   value=0.0						)
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype						)
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							attention_mask.repeat_interleave(self.config.num_attention_heads   ,   dim=0						)

				if self.norm_in is not None:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.norm_in(a						)

				for block in self.transformer_blocks:
					SCREAMING_SNAKE_CASE :   str					  =							block(a   ,   attention_mask=a						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.norm_out(a						)

				if self.prd_embedding is not None:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hidden_states[:, -1]
				else:
					SCREAMING_SNAKE_CASE :   Any					  =							hidden_states[:, additional_embeddings_len:]

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.proj_to_clip_embeddings(a						)

				if not return_dict:
					return (predicted_image_embedding,)

				return PriorTransformerOutput(predicted_image_embedding=a						)






			def 				__UpperCamelCase			(      self				:			str   ,   a				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							(prior_latents * self.clip_std) + self.clip_mean
				return prior_latents




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     LEDTokenizer
				A_   :     Union[str, Any]								=     LEDTokenizerFast
				A_   :     Tuple								=     True


				def __UpperCamelCase						(							self      :  str       )		->     str:
											super().setUp()
											A      						=    [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )
											A      						=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write(json.dumps(__UpperCamelCase       ) + '\n'       )
											with open(self.merges_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write('\n'.join(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  str						, **__UpperCamelCase      :  str       )		->     Tuple:
											kwargs.update(self.special_tokens_map       )
											return self.tokenizer_class.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  List[Any]       )		->     str:
											kwargs.update(self.special_tokens_map       )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Optional[int]       )		->     Dict:
											return "lower newer", "lower newer"


				@cached_property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Any:
											return LEDTokenizer.from_pretrained('allenai/led-base-16384'       )


				@cached_property
				def __UpperCamelCase						(							self      :  str       )		->     str:
											return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'       )


				@require_torch
				def __UpperCamelCase						(							self      :  str       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											A      						=    [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, max_length=len(__UpperCamelCase       )						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )

																		self.assertEqual((2, 9)						, batch.input_ids.shape       )
																		self.assertEqual((2, 9)						, batch.attention_mask.shape       )
																		A      						=    batch.input_ids.tolist()[0]
																		self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIn('input_ids'						, __UpperCamelCase       )
																		self.assertIn('attention_mask'						, __UpperCamelCase       )
																		self.assertNotIn('labels'						, __UpperCamelCase       )
																		self.assertNotIn('decoder_attention_mask'						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[Any]       )		->     Union[str, Any]:
											A      						=    [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(text_target=__UpperCamelCase						, max_length=32						, padding='max_length'						, return_tensors='pt'       )
																		self.assertEqual(32						, targets['input_ids'].shape[1]       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(
																		    ['I am a small frog' * 1_024, 'I am a small frog']						, padding=__UpperCamelCase						, truncation=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )
																		self.assertEqual(batch.input_ids.shape						, (2, 5_122)       )


				@require_torch
				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.']
											A      						=    [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    tokenizer(text_target=__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    inputs['input_ids']
																		A      						=    targets['input_ids']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )


				@require_torch
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    ['Summary of the text.', 'Another summary.']
																		A      						=    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase       )
																		A      						=    [[0] * len(__UpperCamelCase       ) for x in encoded_output['input_ids']]
																		A      						=    tokenizer.pad(__UpperCamelCase       )
																		self.assertSequenceEqual(outputs['global_attention_mask']						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     int:
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     Any:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									A      						=    self.rust_tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    self.tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    'A, <mask> AllenNLP sentence.'
																									A      						=    tokenizer_r.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									A      						=    tokenizer_p.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									self.assertEqual(sum(tokens_r['token_type_ids']       )						, sum(tokens_p['token_type_ids']       )       )
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )						, sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )						, )

																									A      						=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
																									A      						=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )
																									self.assertSequenceEqual(tokens_p['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )
																									self.assertSequenceEqual(tokens_r['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )

																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )