





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	     =					{
    '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 			__lowercase     (     __SCREAMING_SNAKE_CASE							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''codegen'''
					__lowerCAmelCase					=					{
					    '''max_position_embeddings''': '''n_positions''',
					    '''hidden_size''': '''n_embd''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}







					def __init__(  self		,     _UpperCAmelCase=50400		,     _UpperCAmelCase=2048		,     _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=50256		,     _UpperCAmelCase=50256		,     _UpperCAmelCase=False		,     **_UpperCAmelCase		,     ):
									__a	:  List[Any]         =					vocab_size
									__a	:  Any         =					n_ctx
									__a	:  Tuple         =					n_positions
									__a	:  Dict         =					n_embd
									__a	:  Optional[Any]         =					n_layer
									__a	:  Tuple         =					n_head
									__a	:  List[str]         =					n_inner
									__a	:  Union[str, Any]         =					rotary_dim
									__a	:  Optional[Any]         =					activation_function
									__a	:  str         =					resid_pdrop
									__a	:  List[Any]         =					embd_pdrop
									__a	:  Any         =					attn_pdrop
									__a	:  Optional[int]         =					layer_norm_epsilon
									__a	:  List[str]         =					initializer_range
									__a	:  List[Any]         =					use_cache

									__a	:  List[Any]         =					bos_token_id
									__a	:  Optional[int]         =					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 			__lowercase     (     __SCREAMING_SNAKE_CASE							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = "default"		,     _UpperCAmelCase = None		,     _UpperCAmelCase = False		,     ):
									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?
													__a	:  List[str]         =					0







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
									if self.use_past:
													self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE		,     direction='''inputs'''       )
													__a	:  Dict         =					{0: "batch", 1: "past_sequence + sequence"}
									else:
													__a	:  Tuple         =					{0: "batch", 1: "sequence"}

									return common_inputs







					@property
					def 	_lowerCamelCase						(  self       ):
									return self._config.n_layer







					@property
					def 	_lowerCamelCase						(  self       ):
									return self._config.n_head







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = -1		,     _UpperCAmelCase = -1		,     _UpperCAmelCase = False		,     _UpperCAmelCase = None		,     ):
									__a	:  List[Any]         =					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()
									__a	:  Optional[Any]         =					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

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

									__a	:  Any         =					common_inputs["attention_mask"]
									if self.use_past:
													__a	:  Union[str, Any]         =					ordered_inputs["attention_mask"].dtype
													__a	:  Tuple         =					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 	_lowerCamelCase						(  self       ):
									return 13






"""simple docstring"""






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
		from PIL import Image
else:




		class 			__lowercase     :







							'''simple docstring'''




							@staticmethod
							def 	_lowerCamelCase						(  *_UpperCAmelCase		,     **_UpperCAmelCase       ):
											pass

def       __A (     a_  :Image)						->						str:
				__a	:  List[str]         =					hashlib.mda(image.tobytes())
				return m.hexdigest()[:10]

def       __A (     a_  :Image)						->						Dict:
				__a	:  Any         =					np.array(a_)
				__a	:  Tuple         =					npimg.shape
				return {"hash": hashimage(a_), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
					__lowerCAmelCase					=					dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[str]         =					MaskGenerationPipeline(model=_UpperCAmelCase		,     image_processor=_UpperCAmelCase       )
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									pass







					@require_tf
					@unittest.skip('''Image segmentation not implemented in TF'''       )
					def 	_lowerCamelCase						(  self       ):
									pass







					@slow
					@require_torch
					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					pipeline('''mask-generation'''		,     model='''facebook/sam-vit-huge'''       )

									__a	:  Optional[Any]         =					image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     points_per_batch=256       )

									# Shortening by hashing
									__a	:  Optional[int]         =					[]
									for i, o in enumerate(outputs['''masks''']       ):
													new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase       ), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase		,     decimals=4       )		,     [
									        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
									        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1},
									        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
									        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
									        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
									        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_9_6_7},
									        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.9_9_3},
									        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_9_0_9},
									        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_8_7_9},
									        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_8_3_4},
									        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_7_1_6},
									        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_6_1_2},
									        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_5_9_9},
									        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_5_5_2},
									        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_5_3_2},
									        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_5_1_6},
									        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_4_9_9},
									        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_4_8_3},
									        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_4_6_4},
									        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
									        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
									        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9_4_0_8},
									        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_3_3_5},
									        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_3_2_6},
									        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_2_6_2},
									        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_9_9_9},
									        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_6},
									        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_4},
									        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_3},
									        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_1}
									    ]		,     )
									# fmt: on







					@require_torch
					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					'''facebook/sam-vit-huge'''
									__a	:  Tuple         =					pipeline('''mask-generation'''		,     model=_UpperCAmelCase       )

									__a	:  List[Any]         =					image_segmenter(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     pred_iou_thresh=1		,     points_per_batch=256       )

									# Shortening by hashing
									__a	:  Optional[int]         =					[]
									for i, o in enumerate(outputs['''masks''']       ):
													new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase       ), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(_UpperCAmelCase		,     decimals=4       )		,     [
									        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
									        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1_0},
									        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
									        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
									        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
									    ]		,     )