

'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       tempfile.mkdtemp()

				# fmt: off
				SCREAMING_SNAKE_CASE   :      str				     =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
				# fmt: on
				SCREAMING_SNAKE_CASE   :      int				     =       os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]	)
				with open(self.vocab_file      ,      "w"      ,      encoding="utf-8"	) as vocab_writer:
					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       {
				    "do_resize": True,
				    "size": {"height": 18, "width": 18},
				    "do_normalize": True,
				    "image_mean": [0.5, 0.5, 0.5],
				    "image_std": [0.5, 0.5, 0.5],
				}
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       os.path.join(self.tmpdirname      ,      a	)
				with open(self.image_processor_file      ,      "w"      ,      encoding="utf-8"	) as fp:
					json.dump(a      ,      a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      **a							: Optional[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				return BertTokenizer.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      **a							: List[str]	)							->       Tuple:

				"""simple docstring"""

				return ViTImageProcessor.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Tuple:

				"""simple docstring"""

				shutil.rmtree(self.tmpdirname	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta	)]

				SCREAMING_SNAKE_CASE   :      Tuple				     =       [Image.fromarray(np.moveaxis(a      ,      0      ,      -1	)	) for x in image_inputs]

				return image_inputs
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_tokenizer()
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.get_image_processor()

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       VisionTextDualEncoderProcessor(tokenizer=a      ,      image_processor=a	)

				processor.save_pretrained(self.tmpdirname	)
				SCREAMING_SNAKE_CASE   :      str				     =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer.get_vocab()	)
				self.assertIsInstance(processor.tokenizer      ,      (BertTokenizer, BertTokenizerFast)	)

				self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor.to_json_string()	)
				self.assertIsInstance(processor.image_processor      ,      a	)
			def 							__UpperCamelCase (			self							: str	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       VisionTextDualEncoderProcessor(
				    tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.get_tokenizer(bos_token="(BOS)"      ,      eos_token="(EOS)"	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.get_image_processor(do_normalize=a      ,      padding_value=1.0	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       VisionTextDualEncoderProcessor.from_pretrained(
				    self.tmpdirname      ,      bos_token="(BOS)"      ,      eos_token="(EOS)"      ,      do_normalize=a      ,      padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer      ,      (BertTokenizer, BertTokenizerFast)	)

				self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.image_processor      ,      a	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      str				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      List[str]				     =       VisionTextDualEncoderProcessor(tokenizer=a      ,      image_processor=a	)

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

				SCREAMING_SNAKE_CASE   :      int				     =       image_processor(a      ,      return_tensors="np"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       processor(images=a      ,      return_tensors="np"	)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1e-2	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       Dict:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       VisionTextDualEncoderProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       "lower newer"

				SCREAMING_SNAKE_CASE   :      List[str]				     =       processor(text=a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tokenizer(a	)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       VisionTextDualEncoderProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      int				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       processor(text=a      ,      images=a	)

				self.assertListEqual(list(inputs.keys()	)      ,      ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

				# test if it raises when no input is passed
				with self.assertRaises(a	):
					processor()
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      int				     =       VisionTextDualEncoderProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       processor.batch_decode(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tokenizer.batch_decode(a	)

				self.assertListEqual(a      ,      a	)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       VisionTextDualEncoderProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      int				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       processor(text=a      ,      images=a	)

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


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






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







			lowerCamelCase__           ='gptj'
			lowerCamelCase__           ={
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}
			def __init__(			self							: Dict      ,      a							: Optional[Any]=5_0400      ,      a							: List[str]=2048      ,      a							: List[Any]=4096      ,      a							: int=28      ,      a							: Union[str, Any]=16      ,      a							: List[Any]=64      ,      a							: int=None      ,      a							: Optional[int]="gelu_new"      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.0      ,      a							: Union[str, Any]=0.0      ,      a							: Union[str, Any]=1e-5      ,      a							: Any=0.02      ,      a							: Optional[int]=True      ,      a							: Tuple=5_0256      ,      a							: Union[str, Any]=5_0256      ,      a							: List[Any]=False      ,      **a							: str      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       n_positions
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_embd
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_layer
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       n_head
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_inner
				SCREAMING_SNAKE_CASE   :      Any				     =       rotary_dim
				SCREAMING_SNAKE_CASE   :      str				     =       activation_function
				SCREAMING_SNAKE_CASE   :      int				     =       resid_pdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       embd_pdrop
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attn_pdrop
				SCREAMING_SNAKE_CASE   :      List[str]				     =       layer_norm_epsilon
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       use_cache

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       bos_token_id
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       eos_token_id

				super().__init__(
				    bos_token_id=a      ,      eos_token_id=a      ,      tie_word_embeddings=a      ,      **a	)






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







			def __init__(			self							: Optional[int]      ,      a							: PretrainedConfig      ,      a							: str = "default"      ,      a							: List[PatchingSpec] = None      ,      a							: bool = False      ,      )							->       Any:

				"""simple docstring"""

				super().__init__(a      ,      task=a      ,      patching_specs=a      ,      use_past=a	)
				if not getattr(self._config      ,      "pad_token_id"      ,      a	):
					# TODO: how to do that better?
					SCREAMING_SNAKE_CASE   :      Dict				     =       0
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
				if self.use_past:
					self.fill_with_past_key_values_(a      ,      direction="inputs"	)
					SCREAMING_SNAKE_CASE   :      int				     =       {0: "batch", 1: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       {0: "batch", 1: "sequence"}

				return common_inputs
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return self._config.n_layer
			@property
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				return self._config.n_head
			def 							__UpperCamelCase (			self							: str      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       super(a      ,      self	).generate_dummy_inputs(
				    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)

				# We need to order the input in the way they appears in the forward()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       OrderedDict({"input_ids": common_inputs["input_ids"]}	)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
					else:
						import torch

						SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       common_inputs["input_ids"].shape
						# Not using the same length for past_key_values
						SCREAMING_SNAKE_CASE   :      Any				     =       seqlen + 2
						SCREAMING_SNAKE_CASE   :      Dict				     =       (
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						SCREAMING_SNAKE_CASE   :      str				     =       [
						    (torch.zeros(a	), torch.zeros(a	)) for _ in range(self.num_layers	)
						]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       common_inputs["attention_mask"]
				if self.use_past:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       ordered_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE   :      Any				     =       torch.cat(
					    [ordered_inputs["attention_mask"], torch.ones(a      ,      a      ,      dtype=a	)]      ,      dim=1	)

				return ordered_inputs






			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				return 13