



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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast



@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  List[str]							=	tempfile.mkdtemp()

          UpperCamelCase    :  Optional[int]							=	BlipImageProcessor()
          UpperCamelCase    :  List[str]							=	BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'       )

          UpperCamelCase    :  Optional[Any]							=	BlipProcessor(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

          processor.save_pretrained(self.tmpdirname       )
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		Dict:
          return AutoProcessor.from_pretrained(self.tmpdirname,			**SCREAMING_SNAKE_CASE_       ).tokenizer
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		Tuple:
          return AutoProcessor.from_pretrained(self.tmpdirname,			**SCREAMING_SNAKE_CASE_       ).image_processor
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          shutil.rmtree(self.tmpdirname       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	[np.random.randint(255,			size=(3, 30, 400),			dtype=np.uinta       )]

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

          return image_inputs
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Tuple							=	BlipProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()       )
          processor.save_pretrained(self.tmpdirname       )

          UpperCamelCase    :  Union[str, Any]							=	self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)'       )
          UpperCamelCase    :  Tuple							=	self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_,			padding_value=1.0       )

          UpperCamelCase    :  Optional[int]							=	BlipProcessor.from_pretrained(
              self.tmpdirname,			bos_token='(BOS)',			eos_token='(EOS)',			do_normalize=SCREAMING_SNAKE_CASE_,			padding_value=1.0       )

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

          self.assertEqual(processor.image_processor.to_json_string(),			image_processor_add_kwargs.to_json_string()       )
          self.assertIsInstance(processor.image_processor,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  Optional[Any]							=	self.get_image_processor()
          UpperCamelCase    :  Union[str, Any]							=	self.get_tokenizer()

          UpperCamelCase    :  List[Any]							=	BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

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

          UpperCamelCase    :  List[Any]							=	image_processor(SCREAMING_SNAKE_CASE_,			return_tensors='np'       )
          UpperCamelCase    :  List[str]							=	processor(images=SCREAMING_SNAKE_CASE_,			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 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  str							=	self.get_image_processor()
          UpperCamelCase    :  Tuple							=	self.get_tokenizer()

          UpperCamelCase    :  Optional[int]							=	BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	'lower newer'

          UpperCamelCase    :  str							=	processor(text=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[str]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_token_type_ids=SCREAMING_SNAKE_CASE_       )

          for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key],			encoded_processor[key]       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[Any]							=	self.get_image_processor()
          UpperCamelCase    :  List[Any]							=	self.get_tokenizer()

          UpperCamelCase    :  Union[str, Any]							=	BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	'lower newer'
          UpperCamelCase    :  Tuple							=	self.prepare_image_inputs()

          UpperCamelCase    :  Union[str, Any]							=	processor(text=SCREAMING_SNAKE_CASE_,			images=SCREAMING_SNAKE_CASE_       )

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

          # test if it raises when no input is passed
          with pytest.raises(SCREAMING_SNAKE_CASE_       ):
             processor()
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  Optional[int]							=	self.get_image_processor()
          UpperCamelCase    :  Union[str, Any]							=	self.get_tokenizer()

          UpperCamelCase    :  Any							=	BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase    :  Any							=	processor.batch_decode(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       )

          self.assertListEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	self.get_image_processor()
          UpperCamelCase    :  Dict							=	self.get_tokenizer()

          UpperCamelCase    :  Optional[int]							=	BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	'lower newer'
          UpperCamelCase    :  str							=	self.prepare_image_inputs()

          UpperCamelCase    :  Union[str, Any]							=	processor(text=SCREAMING_SNAKE_CASE_,			images=SCREAMING_SNAKE_CASE_       )

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








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          =       logging.get_logger(__name__)


__UpperCAmelCase          =       {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  "codegen"
       UpperCAmelCase__				:					str        =  {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0400,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4096,			SCREAMING_SNAKE_CASE_=28,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="gelu_new",			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[int]							=	n_ctx
          UpperCamelCase    :  Optional[int]							=	n_positions
          UpperCamelCase    :  List[str]							=	n_embd
          UpperCamelCase    :  Dict							=	n_layer
          UpperCamelCase    :  int							=	n_head
          UpperCamelCase    :  Union[str, Any]							=	n_inner
          UpperCamelCase    :  int							=	rotary_dim
          UpperCamelCase    :  Optional[Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	resid_pdrop
          UpperCamelCase    :  Union[str, Any]							=	embd_pdrop
          UpperCamelCase    :  Optional[Any]							=	attn_pdrop
          UpperCamelCase    :  List[str]							=	layer_norm_epsilon
          UpperCamelCase    :  Union[str, Any]							=	initializer_range
          UpperCamelCase    :  str							=	use_cache

          UpperCamelCase    :  Dict							=	bos_token_id
          UpperCamelCase    :  Union[str, Any]							=	eos_token_id

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






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  str							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  Tuple							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Tuple							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Optional[int]							=	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    :  List[str]							=	common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                UpperCamelCase    :  List[Any]							=	seqlen + 2
                UpperCamelCase    :  List[str]							=	(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                UpperCamelCase    :  str							=	[
                    (torch.zeros(SCREAMING_SNAKE_CASE_       ), torch.zeros(SCREAMING_SNAKE_CASE_       )) for _ in range(self.num_layers       )
                ]

          UpperCamelCase    :  List[Any]							=	common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase    :  Optional[Any]							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  List[Any]							=	torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )],			dim=1       )

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



