from typing import Any, Dict, Optional

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

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings




@maybe_allow_in_graph
class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int=0.0    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				str = "geglu"    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				str = "layer_norm"    ,  _UpperCamelCase     :				bool = False    ,  )				->Tuple:


										"""simple docstring"""


										super().__init__()
										_lowerCamelCase     :		int 						=						only_cross_attention

										_lowerCamelCase     :		Tuple 						=						(num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
										_lowerCamelCase     :		str 						=						(num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

										if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
														raise ValueError(
														    F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
														    F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""")

										# Define 3 blocks. Each block has its own normalization layer.
										# 1. Self-Attn
										if self.use_ada_layer_norm:
														_lowerCamelCase     :		Any 						=						AdaLayerNorm(_UpperCamelCase    ,  _UpperCamelCase)
										elif self.use_ada_layer_norm_zero:
														_lowerCamelCase     :		int 						=						AdaLayerNormZero(_UpperCamelCase    ,  _UpperCamelCase)
										else:
														_lowerCamelCase     :		Dict 						=						nn.LayerNorm(_UpperCamelCase    ,  elementwise_affine=_UpperCamelCase)
										_lowerCamelCase     :		Tuple 						=						Attention(
										    query_dim=_UpperCamelCase    ,  heads=_UpperCamelCase    ,  dim_head=_UpperCamelCase    ,  dropout=_UpperCamelCase    ,  bias=_UpperCamelCase    ,  cross_attention_dim=cross_attention_dim if only_cross_attention else None    ,  upcast_attention=_UpperCamelCase    ,  )

										# 2. Cross-Attn
										if cross_attention_dim is not None or double_self_attention:
														# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
														# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
														# the second cross attention block.
														_lowerCamelCase     :		Tuple 						=						(
														    AdaLayerNorm(_UpperCamelCase    ,  _UpperCamelCase)
														    if self.use_ada_layer_norm
														    else nn.LayerNorm(_UpperCamelCase    ,  elementwise_affine=_UpperCamelCase)
														)
														_lowerCamelCase     :		Dict 						=						Attention(
														    query_dim=_UpperCamelCase    ,  cross_attention_dim=cross_attention_dim if not double_self_attention else None    ,  heads=_UpperCamelCase    ,  dim_head=_UpperCamelCase    ,  dropout=_UpperCamelCase    ,  bias=_UpperCamelCase    ,  upcast_attention=_UpperCamelCase    ,  )  # is self-attn if encoder_hidden_states is none
										else:
														_lowerCamelCase     :		Dict 						=						None
														_lowerCamelCase     :		Any 						=						None

										# 3. Feed-forward
										_lowerCamelCase     :		Dict 						=						nn.LayerNorm(_UpperCamelCase    ,  elementwise_affine=_UpperCamelCase)
										_lowerCamelCase     :		List[str] 						=						FeedForward(_UpperCamelCase    ,  dropout=_UpperCamelCase    ,  activation_fn=_UpperCamelCase    ,  final_dropout=_UpperCamelCase)

										# let chunk size default to None
										_lowerCamelCase     :		Union[str, Any] 						=						None
										_lowerCamelCase     :		Optional[Any] 						=						0



						def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				int)				->Union[str, Any]:


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						chunk_size
										_lowerCamelCase     :		List[Any] 						=						dim




						def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				torch.FloatTensor    ,  _UpperCamelCase     :				Optional[torch.FloatTensor] = None    ,  _UpperCamelCase     :				Optional[torch.FloatTensor] = None    ,  _UpperCamelCase     :				Optional[torch.FloatTensor] = None    ,  _UpperCamelCase     :				Optional[torch.LongTensor] = None    ,  _UpperCamelCase     :				Dict[str, Any] = None    ,  _UpperCamelCase     :				Optional[torch.LongTensor] = None    ,  )				->Optional[int]:


										"""simple docstring"""


										if self.use_ada_layer_norm:
														_lowerCamelCase     :		List[Any] 						=						self.norma(_UpperCamelCase    ,  _UpperCamelCase)
										elif self.use_ada_layer_norm_zero:
														_lowerCamelCase     :		str 						=						self.norma(
														    _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  hidden_dtype=hidden_states.dtype)
										else:
														_lowerCamelCase     :		List[str] 						=						self.norma(_UpperCamelCase)

										_lowerCamelCase     :		List[Any] 						=						cross_attention_kwargs if cross_attention_kwargs is not None else {}

										_lowerCamelCase     :		List[str] 						=						self.attna(
										    _UpperCamelCase    ,  encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None    ,  attention_mask=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
										if self.use_ada_layer_norm_zero:
														_lowerCamelCase     :		List[str] 						=						gate_msa.unsqueeze(1) * attn_output
										_lowerCamelCase     :		Optional[Any] 						=						attn_output + hidden_states

										# 2. Cross-Attention
										if self.attna is not None:
														_lowerCamelCase     :		List[Any] 						=						(
														    self.norma(_UpperCamelCase    ,  _UpperCamelCase) if self.use_ada_layer_norm else self.norma(_UpperCamelCase)
														)

														_lowerCamelCase     :		List[Any] 						=						self.attna(
														    _UpperCamelCase    ,  encoder_hidden_states=_UpperCamelCase    ,  attention_mask=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
														_lowerCamelCase     :		Tuple 						=						attn_output + hidden_states

										# 3. Feed-forward
										_lowerCamelCase     :		List[str] 						=						self.norma(_UpperCamelCase)

										if self.use_ada_layer_norm_zero:
														_lowerCamelCase     :		Optional[int] 						=						norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

										if self._chunk_size is not None:
														# "feed_forward_chunk_size" can be used to save memory
														if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																		raise ValueError(
																		    F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""")

														_lowerCamelCase     :		str 						=						norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
														_lowerCamelCase     :		List[str] 						=						torch.cat(
														    [self.ff(_UpperCamelCase) for hid_slice in norm_hidden_states.chunk(_UpperCamelCase    ,  dim=self._chunk_dim)]    ,  dim=self._chunk_dim    ,  )
										else:
														_lowerCamelCase     :		List[Any] 						=						self.ff(_UpperCamelCase)

										if self.use_ada_layer_norm_zero:
														_lowerCamelCase     :		str 						=						gate_mlp.unsqueeze(1) * ff_output

										_lowerCamelCase     :		int 						=						ff_output + hidden_states

										return hidden_states





class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				int = 4    ,  _UpperCamelCase     :				float = 0.0    ,  _UpperCamelCase     :				str = "geglu"    ,  _UpperCamelCase     :				bool = False    ,  )				->int:


										"""simple docstring"""


										super().__init__()
										_lowerCamelCase     :		List[str] 						=						int(dim * mult)
										_lowerCamelCase     :		Optional[int] 						=						dim_out if dim_out is not None else dim

										if activation_fn == "gelu":
														_lowerCamelCase     :		int 						=						GELU(_UpperCamelCase    ,  _UpperCamelCase)
										if activation_fn == "gelu-approximate":
														_lowerCamelCase     :		Any 						=						GELU(_UpperCamelCase    ,  _UpperCamelCase    ,  approximate="""tanh""")
										elif activation_fn == "geglu":
														_lowerCamelCase     :		Union[str, Any] 						=						GEGLU(_UpperCamelCase    ,  _UpperCamelCase)
										elif activation_fn == "geglu-approximate":
														_lowerCamelCase     :		Optional[Any] 						=						ApproximateGELU(_UpperCamelCase    ,  _UpperCamelCase)

										_lowerCamelCase     :		int 						=						nn.ModuleList([])
										# project in
										self.net.append(_UpperCamelCase)
										# project dropout
										self.net.append(nn.Dropout(_UpperCamelCase))
										# project out
										self.net.append(nn.Linear(_UpperCamelCase    ,  _UpperCamelCase))
										# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
										if final_dropout:
														self.net.append(nn.Dropout(_UpperCamelCase))




						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Tuple)				->Optional[Any]:


										"""simple docstring"""


										for module in self.net:
														_lowerCamelCase     :		Optional[int] 						=						module(_UpperCamelCase)
										return hidden_states





class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				Dict    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				str = "none")				->Any:


										"""simple docstring"""


										super().__init__()
										_lowerCamelCase     :		int 						=						nn.Linear(_UpperCamelCase    ,  _UpperCamelCase)
										_lowerCamelCase     :		Optional[Any] 						=						approximate



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[int])				->List[str]:


										"""simple docstring"""


										if gate.device.type != "mps":
														return F.gelu(_UpperCamelCase    ,  approximate=self.approximate)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa)    ,  approximate=self.approximate).to(dtype=gate.dtype)




						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				Optional[int])				->Any:


										"""simple docstring"""


										_lowerCamelCase     :		Optional[Any] 						=						self.proj(_UpperCamelCase)
										_lowerCamelCase     :		List[str] 						=						self.gelu(_UpperCamelCase)
										return hidden_states





class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int)				->Any:


										"""simple docstring"""


										super().__init__()
										_lowerCamelCase     :		Tuple 						=						nn.Linear(_UpperCamelCase    ,  dim_out * 2)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				int)				->List[str]:


										"""simple docstring"""


										if gate.device.type != "mps":
														return F.gelu(_UpperCamelCase)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)




						def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				str)				->int:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						self.proj(_UpperCamelCase).chunk(2    ,  dim=-1)
										return hidden_states * self.gelu(_UpperCamelCase)





class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				Dict    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int)				->List[str]:


										"""simple docstring"""


										super().__init__()
										_lowerCamelCase     :		Any 						=						nn.Linear(_UpperCamelCase    ,  _UpperCamelCase)




						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				List[Any])				->Tuple:


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						self.proj(_UpperCamelCase)
										return x * torch.sigmoid(1.7_0_2 * x)





class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Dict)				->Any:


										"""simple docstring"""


										super().__init__()
										_lowerCamelCase     :		Dict 						=						nn.Embedding(_UpperCamelCase    ,  _UpperCamelCase)
										_lowerCamelCase     :		List[Any] 						=						nn.SiLU()
										_lowerCamelCase     :		Union[str, Any] 						=						nn.Linear(_UpperCamelCase    ,  embedding_dim * 2)
										_lowerCamelCase     :		Optional[Any] 						=						nn.LayerNorm(_UpperCamelCase    ,  elementwise_affine=_UpperCamelCase)




						def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				List[Any])				->Optional[int]:


										"""simple docstring"""


										_lowerCamelCase     :		List[str] 						=						self.linear(self.silu(self.emb(_UpperCamelCase)))
										_lowerCamelCase     :		Optional[int] 						=						torch.chunk(_UpperCamelCase    ,  2)
										_lowerCamelCase     :		Dict 						=						self.norm(_UpperCamelCase) * (1 + scale) + shift
										return x





class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				List[str])				->Dict:


										"""simple docstring"""


										super().__init__()

										_lowerCamelCase     :		str 						=						CombinedTimestepLabelEmbeddings(_UpperCamelCase    ,  _UpperCamelCase)

										_lowerCamelCase     :		List[Any] 						=						nn.SiLU()
										_lowerCamelCase     :		Optional[Any] 						=						nn.Linear(_UpperCamelCase    ,  6 * embedding_dim    ,  bias=_UpperCamelCase)
										_lowerCamelCase     :		Union[str, Any] 						=						nn.LayerNorm(_UpperCamelCase    ,  elementwise_affine=_UpperCamelCase    ,  eps=1E-6)




						def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Dict=None)				->int:


										"""simple docstring"""


										_lowerCamelCase     :		Union[str, Any] 						=						self.linear(self.silu(self.emb(_UpperCamelCase    ,  _UpperCamelCase    ,  hidden_dtype=_UpperCamelCase)))
										_lowerCamelCase     :		int 						=						emb.chunk(6    ,  dim=1)
										_lowerCamelCase     :		List[Any] 						=						self.norm(_UpperCamelCase) * (1 + scale_msa[:, None]) + shift_msa[:, None]
										return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class 	__snake_case	( nn.Module		):





						'''simple docstring'''






						def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Optional[str] = None    ,  _UpperCamelCase     :				float = 1E-5)				->List[str]:


										"""simple docstring"""


										super().__init__()
										_lowerCamelCase     :		int 						=						num_groups
										_lowerCamelCase     :		List[Any] 						=						eps

										if act_fn is None:
														_lowerCamelCase     :		str 						=						None
										else:
														_lowerCamelCase     :		List[Any] 						=						get_activation(_UpperCamelCase)

										_lowerCamelCase     :		Any 						=						nn.Linear(_UpperCamelCase    ,  out_dim * 2)




						def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Dict)				->Dict:


										"""simple docstring"""


										if self.act:
														_lowerCamelCase     :		Optional[Any] 						=						self.act(_UpperCamelCase)
										_lowerCamelCase     :		Any 						=						self.linear(_UpperCamelCase)
										_lowerCamelCase     :		str 						=						emb[:, :, None, None]
										_lowerCamelCase     :		List[Any] 						=						emb.chunk(2    ,  dim=1)

										_lowerCamelCase     :		str 						=						F.group_norm(_UpperCamelCase    ,  self.num_groups    ,  eps=self.eps)
										_lowerCamelCase     :		List[Any] 						=						x * (1 + scale) + shift
										return x





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 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       ViTImageProcessor if is_vision_available() else None



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						(3, 32, 128)
          _lowerCamelCase     :		str 						=						tempfile.mkdtemp()

          # fmt: off
          _lowerCamelCase     :		Dict 						=						["""[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
          _lowerCamelCase     :		str 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))

          _lowerCamelCase     :		int 						=						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(_UpperCamelCase) + """\n""")

          _lowerCamelCase     :		Any 						=						{
              """do_normalize""": False,
              """do_resize""": True,
              """image_processor_type""": """ViTImageProcessor""",
              """resample""": 3,
              """size""": {"""height""": 32, """width""": 128},
          }
          _lowerCamelCase     :		Union[str, Any] 						=						os.path.join(self.tmpdirname    ,  _UpperCamelCase)
          with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              json.dump(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  **_UpperCamelCase     :				Any)				->Tuple:


          """simple docstring"""


          return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  **_UpperCamelCase     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          shutil.rmtree(self.tmpdirname)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						np.random.randint(255    ,  size=(3, 30, 400)    ,  dtype=np.uinta)

          _lowerCamelCase     :		int 						=						Image.fromarray(np.moveaxis(_UpperCamelCase    ,  0    ,  -1))

          return image_input



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()
          _lowerCamelCase     :		Tuple 						=						self.get_image_processor()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		int 						=						MgpstrProcessor.from_pretrained(self.tmpdirname    ,  use_fast=_UpperCamelCase)

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

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_tokenizer()
          _lowerCamelCase     :		Optional[Any] 						=						self.get_image_processor()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)

          _lowerCamelCase     :		Tuple 						=						self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)""")
          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor(do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          _lowerCamelCase     :		Tuple 						=						MgpstrProcessor.from_pretrained(
              self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=_UpperCamelCase    ,  padding_value=1.0)

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

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[str] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Optional[int] 						=						image_processor(_UpperCamelCase    ,  return_tensors="""np""")
          _lowerCamelCase     :		int 						=						processor(images=_UpperCamelCase    ,  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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						"""test"""

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						tokenizer(_UpperCamelCase)
          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[Any] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						"""test"""
          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		int 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

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

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Dict 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          _lowerCamelCase     :		Any 						=						processor.char_decode(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						tokenizer.batch_decode(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						[seq.replace(""" """    ,  """""") for seq in decoded_tok]

          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_image_processor()
          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						None
          _lowerCamelCase     :		Union[str, Any] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

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




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						torch.randn(1    ,  27    ,  38)
          _lowerCamelCase     :		List[Any] 						=						torch.randn(1    ,  27    ,  5_0257)
          _lowerCamelCase     :		List[str] 						=						torch.randn(1    ,  27    ,  3_0522)

          _lowerCamelCase     :		int 						=						processor.batch_decode([char_input, bpe_input, wp_input])

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




