




'''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   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 128      ,       _lowercase     :						int = 256      ,       _lowercase     :						float = 20_00.0      ,       _lowercase     :						int = 768      ,       _lowercase     :						int = 12      ,       _lowercase     :						int = 12      ,       _lowercase     :						int = 64      ,       _lowercase     :						int = 2_048      ,       _lowercase     :						float = 0.1      ,       )    ->							Tuple:
								super().__init__()

								A_									= nn.Sequential(
								    nn.Linear(_lowercase      ,       d_model * 4      ,       bias=_lowercase)      ,       nn.SiLU()      ,       nn.Linear(d_model * 4      ,       d_model * 4      ,       bias=_lowercase)      ,       nn.SiLU()      ,       )

								A_									= nn.Embedding(_lowercase      ,       _lowercase)
								A_									= False

								A_									= nn.Linear(_lowercase      ,       _lowercase      ,       bias=_lowercase)

								A_									= nn.Dropout(p=_lowercase)

								A_									= nn.ModuleList()
								for lyr_num in range(_lowercase):
											# FiLM conditional T5 decoder
											A_									= DecoderLayer(d_model=_lowercase      ,       d_kv=_lowercase      ,       num_heads=_lowercase      ,       d_ff=_lowercase      ,       dropout_rate=_lowercase)
											self.decoders.append(_lowercase)

								A_									= TaLayerNorm(_lowercase)

								A_									= nn.Dropout(p=_lowercase)
								A_									= nn.Linear(_lowercase      ,       _lowercase      ,       bias=_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Any)    ->							str:
								A_									= torch.mul(query_input.unsqueeze(-1)      ,       key_input.unsqueeze(-2))
								return mask.unsqueeze(-3)



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Any      ,       _lowercase     :						List[str]      ,       _lowercase     :						List[str])    ->							int:
								A_		, A_		, A_									= decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								A_									= 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)

								A_									= self.conditioning_emb(_lowercase).unsqueeze(1)

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

								A_									= 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.
								A_									= torch.broadcast_to(
								    torch.arange(_lowercase      ,       device=decoder_input_tokens.device)      ,       (batch, seq_length)      ,       )

								A_									= self.position_encoding(_lowercase)

								A_									= self.continuous_inputs_projection(_lowercase)
								inputs += position_encodings
								A_									= self.dropout(_lowercase)

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

								# Translate encoding masks to encoder-decoder masks.
								A_									= [(x, self.encoder_decoder_mask(_lowercase      ,       _lowercase)) for x, y in encodings_and_masks]

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

								for lyr in self.decoders:
											A_									= lyr(
											    _lowercase      ,       conditioning_emb=_lowercase      ,       encoder_hidden_states=_lowercase      ,       encoder_attention_mask=_lowercase      ,       )[0]

								A_									= self.decoder_norm(_lowercase)
								A_									= self.post_dropout(_lowercase)

								A_									= self.spec_out(_lowercase)
								return spec_out





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





					def __init__(	self     :						str      ,       _lowercase     :						str      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Tuple      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Tuple=1E-6)    ->							Union[str, Any]:
								super().__init__()
								A_									= nn.ModuleList()

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

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

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



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Optional[int]=None      ,       _lowercase     :						Union[str, Any]=None      ,       _lowercase     :						Any=None      ,       _lowercase     :						Optional[Any]=None      ,       _lowercase     :						str=None      ,       )    ->							Dict:
								A_									= self.layer[0](
								    _lowercase      ,       conditioning_emb=_lowercase      ,       attention_mask=_lowercase      ,       )

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

											A_									= self.layer[1](
											    _lowercase      ,       key_value_states=_lowercase      ,       attention_mask=_lowercase      ,       )

								# Apply Film Conditional Feed Forward layer
								A_									= self.layer[-1](_lowercase      ,       _lowercase)

								return (hidden_states,)





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





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



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Union[str, Any]=None      ,       _lowercase     :						Optional[Any]=None      ,       )    ->							Tuple:
								# pre_self_attention_layer_norm
								A_									= self.layer_norm(_lowercase)

								if conditioning_emb is not None:
											A_									= self.FiLMLayer(_lowercase      ,       _lowercase)

								# Self-attention block
								A_									= self.attention(_lowercase)

								A_									= hidden_states + self.dropout(_lowercase)

								return hidden_states





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





					def __init__(	self     :						Any      ,       _lowercase     :						int      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						List[str]      ,       _lowercase     :						List[str]      ,       _lowercase     :						Tuple)    ->							Union[str, Any]:
								super().__init__()
								A_									= Attention(query_dim=_lowercase      ,       heads=_lowercase      ,       dim_head=_lowercase      ,       out_bias=_lowercase      ,       scale_qk=_lowercase)
								A_									= TaLayerNorm(_lowercase      ,       eps=_lowercase)
								A_									= nn.Dropout(_lowercase)



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						List[str]      ,       _lowercase     :						Any=None      ,       _lowercase     :						List[str]=None      ,       )    ->							Tuple:
								A_									= self.layer_norm(_lowercase)
								A_									= self.attention(
								    _lowercase      ,       encoder_hidden_states=_lowercase      ,       attention_mask=attention_mask.squeeze(1)      ,       )
								A_									= hidden_states + self.dropout(_lowercase)
								return layer_output





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Any      ,       _lowercase     :						Optional[int])    ->							Union[str, Any]:
								super().__init__()
								A_									= TaDenseGatedActDense(d_model=_lowercase      ,       d_ff=_lowercase      ,       dropout_rate=_lowercase)
								A_									= TaFiLMLayer(in_features=d_model * 4      ,       out_features=_lowercase)
								A_									= TaLayerNorm(_lowercase      ,       eps=_lowercase)
								A_									= nn.Dropout(_lowercase)



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						str=None)    ->							int:
								A_									= self.layer_norm(_lowercase)
								if conditioning_emb is not None:
											A_									= self.film(_lowercase      ,       _lowercase)

								A_									= self.DenseReluDense(_lowercase)
								A_									= hidden_states + self.dropout(_lowercase)
								return hidden_states





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





					def __init__(	self     :						List[Any]      ,       _lowercase     :						List[str]      ,       _lowercase     :						Any      ,       _lowercase     :						Tuple)    ->							Any:
								super().__init__()
								A_									= nn.Linear(_lowercase      ,       _lowercase      ,       bias=_lowercase)
								A_									= nn.Linear(_lowercase      ,       _lowercase      ,       bias=_lowercase)
								A_									= nn.Linear(_lowercase      ,       _lowercase      ,       bias=_lowercase)
								A_									= nn.Dropout(_lowercase)
								A_									= NewGELUActivation()



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						List[Any])    ->							List[Any]:
								A_									= self.act(self.wi_a(_lowercase))
								A_									= self.wi_a(_lowercase)
								A_									= hidden_gelu * hidden_linear
								A_									= self.dropout(_lowercase)

								A_									= self.wo(_lowercase)
								return hidden_states





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





					def __init__(	self     :						Any      ,       _lowercase     :						Dict      ,       _lowercase     :						Tuple=1E-6)    ->							List[Any]:
								super().__init__()
								A_									= nn.Parameter(torch.ones(_lowercase))
								A_									= eps



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Dict)    ->							str:
								# 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

								A_									= hidden_states.to(torch.floataa).pow(2).mean(-1      ,       keepdim=_lowercase)
								A_									= hidden_states * torch.rsqrt(variance + self.variance_epsilon)

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

								return self.weight * hidden_states





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						torch.Tensor)    ->							torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.04_47_15 * torch.pow(_lowercase      ,       3.0))))





class   __UpperCAmelCase				(     nn.Module				):



					'''simple docstring'''





					def __init__(	self     :						Optional[int]      ,       _lowercase     :						Dict      ,       _lowercase     :						Optional[Any])    ->							Dict:
								super().__init__()
								A_									= nn.Linear(_lowercase      ,       out_features * 2      ,       bias=_lowercase)



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Optional[Any])    ->							Dict:
								A_									= self.scale_bias(_lowercase)
								A_		, A_									= torch.chunk(_lowercase      ,       2      ,       -1)
								A_									= x * (1 + scale) + shift
								return x










'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		ViTImageProcessor if is_vision_available() else None



					@property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								A_									= (3, 32, 128)
								A_									= tempfile.mkdtemp()

								# fmt: off
								A_									= ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
								# fmt: on
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))

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

								A_									= {
								    'do_normalize': False,
								    'do_resize': True,
								    'image_processor_type': 'ViTImageProcessor',
								    'resample': 3,
								    'size': {'height': 32, 'width': 128},
								}
								A_									= os.path.join(self.tmpdirname      ,       _lowercase)
								with open(self.image_processor_file      ,       'w'      ,       encoding='utf-8') as fp:
											json.dump(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Optional[int])    ->							int:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								shutil.rmtree(self.tmpdirname)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								A_									= np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta)

								A_									= Image.fromarray(np.moveaxis(_lowercase      ,       0      ,       -1))

								return image_input



					def     __snake_case						(	self     :						Optional[Any])    ->							List[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)
								A_									= MgpstrProcessor.from_pretrained(self.tmpdirname      ,       use_fast=_lowercase)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)

								A_									= self.get_tokenizer(bos_token='(BOS)'      ,       eos_token='(EOS)')
								A_									= self.get_image_processor(do_normalize=_lowercase      ,       padding_value=1.0)

								A_									= MgpstrProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token='(BOS)'      ,       eos_token='(EOS)'      ,       do_normalize=_lowercase      ,       padding_value=1.0)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= self.prepare_image_inputs()

								A_									= image_processor(_lowercase      ,       return_tensors='np')
								A_									= processor(images=_lowercase      ,       return_tensors='np')

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()      ,       input_processor[key].sum()      ,       delta=1E-2)



					def     __snake_case						(	self     :						Any)    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'

								A_									= processor(text=_lowercase)

								A_									= tokenizer(_lowercase)
								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key])



					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       ['pixel_values', 'labels'])

								# test if it raises when no input is passed
								with pytest.raises(_lowercase):
											processor()



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								A_									= processor.char_decode(_lowercase)
								A_									= tokenizer.batch_decode(_lowercase)
								A_									= [seq.replace(' '      ,       '') for seq in decoded_tok]

								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= None
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       processor.model_input_names)



					def     __snake_case						(	self     :						List[str])    ->							Any:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= torch.randn(1      ,       27      ,       38)
								A_									= torch.randn(1      ,       27      ,       50_257)
								A_									= torch.randn(1      ,       27      ,       30_522)

								A_									= processor.batch_decode([char_input, bpe_input, wp_input])

								self.assertListEqual(list(results.keys())      ,       ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'])




