


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
   import os

   import jax.numpy as jnp
   from jax import jit

   from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
   from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

   _A		       =						'0.12'  # assumed parallelism: 8


if is_torch_available():
   import torch

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Optional[int]=None				):
     if rng is None:
          __UpperCamelCase    							=random.Random()

     __UpperCamelCase    							=1
     for dim in shape:
          total_dims *= dim

     __UpperCamelCase    							=[]
     for _ in range(SCREAMING_SNAKE_CASE__				):
          values.append(rng.randint(0			,     vocab_size - 1				)				)

     __UpperCamelCase    							=np.array(SCREAMING_SNAKE_CASE__			,     dtype=jnp.intaa				).reshape(SCREAMING_SNAKE_CASE__				)

     return output

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]=None				):
     __UpperCamelCase    							=ids_tensor(SCREAMING_SNAKE_CASE__			,     vocab_size=2			,     rng=SCREAMING_SNAKE_CASE__				)
     # make sure that at least one token is attended to for each batch
     __UpperCamelCase    							=1
     return attn_mask






@require_flax
class     UpperCAmelCase__       :



    """simple docstring"""






    UpperCAmelCase__						:		str      =  None
    UpperCAmelCase__						:		Dict      =  ()






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()

         # cut to half length & take max batch_size 3
         __UpperCamelCase    							=2
         __UpperCamelCase    							=inputs['input_ids'].shape[-1] // 2
         __UpperCamelCase    							=inputs['input_ids'][:max_batch_size, :sequence_length]

         __UpperCamelCase    							=jnp.ones_like(A_    )
         __UpperCamelCase    							=attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         __UpperCamelCase    							=input_ids.shape[-1] + 5
         if config.eos_token_id is not None and config.pad_token_id is None:
              # hack to allow generate for models such as GPT2 as is done in `generate()`
              __UpperCamelCase    							=config.eos_token_id
         return config, input_ids, attention_mask, max_length






    @is_pt_flax_cross_test
    def _a					(			self    )  ->       Any:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=0

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model_class.__name__[4:]  # Skip the "Flax" at the beginning
              __UpperCamelCase    							=getattr(A_					,   A_    )
              __UpperCamelCase    							=pt_model_class(A_    ).eval()
              __UpperCamelCase    							=load_flax_weights_in_pytorch_model(A_					,   flax_model.params    )

              __UpperCamelCase    							=flax_model.generate(A_    ).sequences
              __UpperCamelCase    							=pt_model.generate(torch.tensor(A_					,   dtype=torch.long    )    )

              if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                   __UpperCamelCase    							=flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

              self.assertListEqual(pt_generation_outputs.numpy().tolist()					,   flax_generation_outputs.tolist()    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=True
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=2

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=2
         __UpperCamelCase    							=2

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[0]					,   input_ids.shape[0] * config.num_return_sequences    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=True
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=0.8
         __UpperCamelCase    							=10
         __UpperCamelCase    							=0.3
         __UpperCamelCase    							=1
         __UpperCamelCase    							=8
         __UpperCamelCase    							=9

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=1
         __UpperCamelCase    							=8
         __UpperCamelCase    							=9

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=2
         __UpperCamelCase    							=1
         __UpperCamelCase    							=8
         __UpperCamelCase    							=9

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()

         # pad attention mask on the left
         __UpperCamelCase    							=attention_mask.at[(0, 0)].set(0    )

         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_					,   attention_mask=A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_					,   attention_mask=A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()

         # pad attention mask on the left
         __UpperCamelCase    							=attention_mask.at[(0, 0)].set(0    )

         __UpperCamelCase    							=True
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_					,   attention_mask=A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_					,   attention_mask=A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()

         # pad attention mask on the left
         __UpperCamelCase    							=attention_mask.at[(0, 0)].set(0    )

         __UpperCamelCase    							=2
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_					,   attention_mask=A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_					,   attention_mask=A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






@require_flax
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'    )
         __UpperCamelCase    							=FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'    )

         __UpperCamelCase    							='Hello world'
         __UpperCamelCase    							=tokenizer(A_					,   return_tensors='np'    ).input_ids

         # typos are quickly detected (the correct argument is `do_sample`)
         with self.assertRaisesRegex(A_					,   'do_samples'    ):
              model.generate(A_					,   do_samples=A_    )

         # arbitrary arguments that will not be used anywhere are also not accepted
         with self.assertRaisesRegex(A_					,   'foo'    ):
              __UpperCamelCase    							={'foo': 'bar'}
              model.generate(A_					,   **A_    )










from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						requires_backends(self		,       "torch"		)

						if self.framework != "pt":
											raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''		)

						self.check_model_type(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}
						# preprocess args
						if "points_per_batch" in kwargs:
											a					     =   kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
											a					     =   kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
											a					     =   kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
											a					     =   kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
											a					     =   kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
											a					     =   kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
											a					     =   kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
											a					     =   kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
											a					     =   kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
											a					     =   kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
											a					     =   kwargs["output_rle_mask"]
						if "output_bboxes_mask" in kwargs:
											a					     =   kwargs["output_bboxes_mask"]
						return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       *lowerCamelCase_		,       num_workers=lowerCamelCase_		,       batch_size=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

						with self.device_placement():
											if self.framework == "pt":
																a					     =   self.get_inference_context()
																with inference_context():
																					a					     =   self._ensure_tensor_on_device(lowerCamelCase_		,       device=self.device		)
																					a					     =   self.model.get_image_embeddings(model_inputs.pop("pixel_values"		)		)
																					a					     =   image_embeddings

						a					     =   grid_points.shape[1]
						a					     =   points_per_batch if points_per_batch is not None else n_points

						if points_per_batch <= 0:
											raise ValueError(
											    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
											    "To return all points at once, set points_per_batch to None"		)

						for i in range(0		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   i == n_points - points_per_batch
											yield {
											    "input_points": batched_points,
											    "input_labels": labels,
											    "input_boxes": crop_boxes,
											    "is_last": is_last,
											    **model_inputs,
											}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   self.image_processor.filter_masks(
						    masks[0]		,       iou_scores[0]		,       original_sizes[0]		,       input_boxes[0]		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       )
						return {
						    "masks": masks,
						    "is_last": is_last,
						    "boxes": boxes,
						    "iou_scores": iou_scores,
						}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						for model_output in model_outputs:
											all_scores.append(model_output.pop("iou_scores"		)		)
											all_masks.extend(model_output.pop("masks"		)		)
											all_boxes.append(model_output.pop("boxes"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   defaultdict(lowerCamelCase_		)
						for output in model_outputs:
											for k, v in output.items():
																extra[k].append(lowerCamelCase_		)

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

						return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
