
"""simple docstring"""

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= tempfile.mkdtemp()

      __UpperCamelCase       						= BlipImageProcessor()
      __UpperCamelCase       						= GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'   )

      __UpperCamelCase       						= BlipaProcessor(__UpperCAmelCase			,  __UpperCAmelCase   )

      processor.save_pretrained(self.tmpdirname   )





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return AutoProcessor.from_pretrained(self.tmpdirname			,  **__UpperCAmelCase   ).tokenizer





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return AutoProcessor.from_pretrained(self.tmpdirname			,  **__UpperCAmelCase   ).image_processor





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      shutil.rmtree(self.tmpdirname   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= [np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta   )]

      __UpperCamelCase       						= [Image.fromarray(np.moveaxis(__UpperCAmelCase			,  0			,  -1   )   ) for x in image_inputs]

      return image_inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BlipaProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()   )
      processor.save_pretrained(self.tmpdirname   )

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

      __UpperCamelCase       						= BlipaProcessor.from_pretrained(
          self.tmpdirname			,  bos_token='(BOS)'			,  eos_token='(EOS)'			,  do_normalize=__UpperCAmelCase			,  padding_value=1.0   )

      self.assertEqual(processor.tokenizer.get_vocab()			,  tokenizer_add_kwargs.get_vocab()   )
      self.assertIsInstance(processor.tokenizer			,  __UpperCAmelCase   )

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= self.prepare_image_inputs()

      __UpperCamelCase       						= image_processor(__UpperCAmelCase			,  return_tensors='np'   )
      __UpperCamelCase       						= processor(images=__UpperCAmelCase			,  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   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= 'lower newer'

      __UpperCamelCase       						= processor(text=__UpperCAmelCase   )

      __UpperCamelCase       						= tokenizer(__UpperCAmelCase			,  return_token_type_ids=__UpperCAmelCase   )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,  encoded_processor[key]   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= 'lower newer'
      __UpperCamelCase       						= self.prepare_image_inputs()

      __UpperCamelCase       						= processor(text=__UpperCAmelCase			,  images=__UpperCAmelCase   )

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

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

      __UpperCamelCase       						= processor.batch_decode(__UpperCAmelCase   )
      __UpperCamelCase       						= tokenizer.batch_decode(__UpperCAmelCase   )

      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= 'lower newer'
      __UpperCamelCase       						= self.prepare_image_inputs()

      __UpperCamelCase       						= processor(text=__UpperCAmelCase			,  images=__UpperCAmelCase   )

      # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
      self.assertListEqual(list(inputs.keys()   )			,  ['pixel_values', 'input_ids', 'attention_mask']   )





"""simple docstring"""

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


UpperCamelCase			:       Optional[int]									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"gptj"
     lowercase		     =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(			self			,  __UpperCAmelCase=5_0400			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=4096			,  __UpperCAmelCase=28			,  __UpperCAmelCase=16			,  __UpperCAmelCase=64			,  __UpperCAmelCase=None			,  __UpperCAmelCase="gelu_new"			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=True			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= n_positions
      __UpperCamelCase       						= n_embd
      __UpperCamelCase       						= n_layer
      __UpperCamelCase       						= n_head
      __UpperCamelCase       						= n_inner
      __UpperCamelCase       						= rotary_dim
      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= resid_pdrop
      __UpperCamelCase       						= embd_pdrop
      __UpperCamelCase       						= attn_pdrop
      __UpperCamelCase       						= layer_norm_epsilon
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= use_cache

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id

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


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "default"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase			,  task=__UpperCAmelCase			,  patching_specs=__UpperCAmelCase			,  use_past=__UpperCAmelCase   )
      if not getattr(self._config			,  'pad_token_id'			,  __UpperCAmelCase   ):
       # TODO: how to do that better?
       __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
      if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase			,  direction='inputs'   )
       __UpperCamelCase       						= {0: 'batch', 1: 'past_sequence + sequence'}
      else:
       __UpperCamelCase       						= {0: 'batch', 1: 'sequence'}

      return common_inputs





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_head





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = False			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= super(__UpperCAmelCase			,  self   ).generate_dummy_inputs(
          __UpperCAmelCase			,  batch_size=__UpperCAmelCase			,  seq_length=__UpperCAmelCase			,  is_pair=__UpperCAmelCase			,  framework=__UpperCAmelCase   )

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase       						= 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

        __UpperCamelCase						,			__UpperCamelCase       						= common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __UpperCamelCase       						= seqlen + 2
        __UpperCamelCase       						= (
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __UpperCamelCase       						= [
            (torch.zeros(__UpperCAmelCase   ), torch.zeros(__UpperCAmelCase   )) for _ in range(self.num_layers   )
        ]

      __UpperCamelCase       						= common_inputs['attention_mask']
      if self.use_past:
       __UpperCamelCase       						= ordered_inputs['attention_mask'].dtype
       __UpperCamelCase       						= torch.cat(
           [ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase			,  __UpperCAmelCase			,  dtype=__UpperCAmelCase   )]			,  dim=1   )

      return ordered_inputs







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 13



