import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

				if is_vision_available():
								from transformers import OneFormerImageProcessor
								from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
								from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
				from PIL import Image
def  snake_case						(__lowercase ,      __lowercase="shi-labs/oneformer_demo"						)	->	List[str]:

						'''simple docstring'''



						with open(hf_hub_download(__lowercase ,      __lowercase ,      repo_type="dataset"						) ,      "r"						) as f:
												_snake_case						:					Optional[int]    =   json.load(__lowercase						)
						_snake_case						:					Optional[Any]    =   {}
						_snake_case						:					int    =   []
						_snake_case						:					Dict    =   []
						for key, info in class_info.items():
												_snake_case						:					Optional[Any]    =   info["name"]
												class_names.append(info["name"]						)
												if info["isthing"]:
																		thing_ids.append(int(__lowercase						)						)
						_snake_case						:					str    =   thing_ids
						_snake_case						:					Optional[int]    =   class_names
						return metadata



class        lowercase_   (					unittest.TestCase		):



							def __init__(						self				,    lowercase_				,    lowercase_=7				,    lowercase_=3				,    lowercase_=30				,    lowercase_=400				,    lowercase_=None				,    lowercase_=True				,    lowercase_=True				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=10				,    lowercase_=False				,    lowercase_=255				,    lowercase_="shi-labs/oneformer_demo"				,    lowercase_="ade20k_panoptic.json"				,    lowercase_=10				,    ):
													_snake_case						:					Dict    =   parent
													_snake_case						:					Any    =   batch_size
													_snake_case						:					List[Any]    =   num_channels
													_snake_case						:					Optional[int]    =   min_resolution
													_snake_case						:					List[str]    =   max_resolution
													_snake_case						:					Optional[Any]    =   do_resize
													_snake_case						:					str    =   {"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
													_snake_case						:					Tuple    =   do_normalize
													_snake_case						:					List[Any]    =   image_mean
													_snake_case						:					Any    =   image_std
													_snake_case						:					Tuple    =   class_info_file
													_snake_case						:					Optional[int]    =   prepare_metadata(lowercase_				,    lowercase_    )
													_snake_case						:					Optional[int]    =   num_text
													_snake_case						:					int    =   repo_path

													# for the post_process_functions
													_snake_case						:					int    =   2
													_snake_case						:					Any    =   10
													_snake_case						:					Any    =   10
													_snake_case						:					Union[str, Any]    =   3
													_snake_case						:					List[str]    =   4
													_snake_case						:					Union[str, Any]    =   num_labels
													_snake_case						:					Any    =   do_reduce_labels
													_snake_case						:					Union[str, Any]    =   ignore_index



							def 	UpperCamelCase	(						self    ):
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "num_labels": self.num_labels,
													    "do_reduce_labels": self.do_reduce_labels,
													    "ignore_index": self.ignore_index,
													    "class_info_file": self.class_info_file,
													    "metadata": self.metadata,
													    "num_text": self.num_text,
													}



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_=False    ):
													if not batched:
																			_snake_case						:					Dict    =   image_inputs[0]
																			if isinstance(lowercase_				,    Image.Image    ):
																									_snake_case    ,_snake_case						:					str    =   image.size
																			else:
																									_snake_case    ,_snake_case						:					Optional[int]    =   image.shape[1], image.shape[2]
																			if w < h:
																									_snake_case						:					Optional[Any]    =   int(self.size["shortest_edge"] * h / w    )
																									_snake_case						:					str    =   self.size["shortest_edge"]
																			elif w > h:
																									_snake_case						:					List[str]    =   self.size["shortest_edge"]
																									_snake_case						:					List[Any]    =   int(self.size["shortest_edge"] * w / h    )
																			else:
																									_snake_case						:					Tuple    =   self.size["shortest_edge"]
																									_snake_case						:					List[Any]    =   self.size["shortest_edge"]

													else:
																			_snake_case						:					Union[str, Any]    =   []
																			for image in image_inputs:
																									_snake_case    ,_snake_case						:					List[Any]    =   self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			_snake_case						:					Dict    =   max(lowercase_				,    key=lambda lowercase_    : item[0]    )[0]
																			_snake_case						:					List[Any]    =   max(lowercase_				,    key=lambda lowercase_    : item[1]    )[1]

													return expected_height, expected_width



							def 	UpperCamelCase	(						self    ):
													return OneFormerForUniversalSegmentationOutput(
													    # +1 for null class
													    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)    )				,    masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)    )				,    )




@require_torch
@require_vision
class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
							# only for test_image_processing_common.test_image_proc_to_json_string
							_lowerCamelCase						=		image_processing_class



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   OneFormerImageProcessorTester(self    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self.image_processing_tester.prepare_image_processor_dict()



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(lowercase_				,    "image_mean"    )    )
													self.assertTrue(hasattr(lowercase_				,    "image_std"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_normalize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_resize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "size"    )    )
													self.assertTrue(hasattr(lowercase_				,    "ignore_index"    )    )
													self.assertTrue(hasattr(lowercase_				,    "class_info_file"    )    )
													self.assertTrue(hasattr(lowercase_				,    "num_text"    )    )
													self.assertTrue(hasattr(lowercase_				,    "repo_path"    )    )
													self.assertTrue(hasattr(lowercase_				,    "metadata"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_reduce_labels"    )    )



							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processor
													_snake_case						:					Optional[int]    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													_snake_case						:					List[str]    =   prepare_image_inputs(self.image_processing_tester				,    equal_resolution=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    Image.Image    )

													# Test not batched input
													_snake_case						:					List[str]    =   image_processor(image_inputs[0]				,    ["semantic"]				,    return_tensors="pt"    ).pixel_values

													_snake_case    ,_snake_case						:					str    =   self.image_processing_tester.get_expected_values(lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processing_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													_snake_case    ,_snake_case						:					Optional[int]    =   self.image_processing_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													_snake_case						:					Any    =   image_processor(
													    lowercase_				,    ["semantic"] * len(lowercase_    )				,    return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processing_tester.batch_size,
													        self.image_processing_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processor
													_snake_case						:					int    =   self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													_snake_case						:					Union[str, Any]    =   prepare_image_inputs(self.image_processing_tester				,    equal_resolution=lowercase_				,    numpify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    np.ndarray    )

													# Test not batched input
													_snake_case						:					Optional[Any]    =   image_processor(image_inputs[0]				,    ["semantic"]				,    return_tensors="pt"    ).pixel_values

													_snake_case    ,_snake_case						:					List[str]    =   self.image_processing_tester.get_expected_values(lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processing_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													_snake_case    ,_snake_case						:					int    =   self.image_processing_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													_snake_case						:					Optional[Any]    =   image_processor(
													    lowercase_				,    ["semantic"] * len(lowercase_    )				,    return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processing_tester.batch_size,
													        self.image_processing_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processor
													_snake_case						:					str    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													_snake_case						:					Any    =   prepare_image_inputs(self.image_processing_tester				,    equal_resolution=lowercase_				,    torchify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    torch.Tensor    )

													# Test not batched input
													_snake_case						:					Dict    =   image_processor(image_inputs[0]				,    ["semantic"]				,    return_tensors="pt"    ).pixel_values

													_snake_case    ,_snake_case						:					List[str]    =   self.image_processing_tester.get_expected_values(lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processing_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													_snake_case    ,_snake_case						:					List[str]    =   self.image_processing_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													_snake_case						:					Optional[int]    =   image_processor(
													    lowercase_				,    ["semantic"] * len(lowercase_    )				,    return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processing_tester.batch_size,
													        self.image_processing_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )



							def 	UpperCamelCase	(						self				,    lowercase_=False				,    lowercase_=False				,    lowercase_="np"    ):
													_snake_case						:					Optional[Any]    =   self.image_processing_class(**self.image_processor_dict    )
													# prepare image and target
													_snake_case						:					Any    =   self.image_processing_tester.num_labels
													_snake_case						:					Any    =   None
													_snake_case						:					Union[str, Any]    =   None
													_snake_case						:					Optional[Any]    =   prepare_image_inputs(self.image_processing_tester				,    equal_resolution=lowercase_    )
													if with_segmentation_maps:
																			_snake_case						:					List[Any]    =   num_labels
																			if is_instance_map:
																									_snake_case						:					Optional[int]    =   list(range(lowercase_    )    ) * 2
																									_snake_case						:					Tuple    =   dict(enumerate(lowercase_    )    )
																			_snake_case						:					str    =   [
																			    np.random.randint(0				,    high * 2				,    (img.size[1], img.size[0])    ).astype(np.uinta    ) for img in image_inputs
																			]
																			if segmentation_type == "pil":
																									_snake_case						:					Optional[int]    =   [Image.fromarray(lowercase_    ) for annotation in annotations]

													_snake_case						:					int    =   image_processor(
													    lowercase_				,    ["semantic"] * len(lowercase_    )				,    lowercase_				,    return_tensors="pt"				,    instance_id_to_semantic_id=lowercase_				,    pad_and_return_pixel_mask=lowercase_				,    )

													return inputs



							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													def common(lowercase_=False				,    lowercase_=None    ):
																			_snake_case						:					int    =   self.comm_get_image_processor_inputs(
																			    with_segmentation_maps=lowercase_				,    is_instance_map=lowercase_				,    segmentation_type=lowercase_    )

																			_snake_case						:					List[str]    =   inputs["mask_labels"]
																			_snake_case						:					str    =   inputs["class_labels"]
																			_snake_case						:					Union[str, Any]    =   inputs["pixel_values"]
																			_snake_case						:					List[str]    =   inputs["text_inputs"]

																			# check the batch_size
																			for mask_label, class_label, text_input in zip(lowercase_				,    lowercase_				,    lowercase_    ):
																									self.assertEqual(mask_label.shape[0]				,    class_label.shape[0]    )
																									# this ensure padding has happened
																									self.assertEqual(mask_label.shape[1:]				,    pixel_values.shape[2:]    )
																									self.assertEqual(len(lowercase_    )				,    self.image_processing_tester.num_text    )

													common()
													common(is_instance_map=lowercase_    )
													common(is_instance_map=lowercase_				,    segmentation_type="pil"    )
													common(is_instance_map=lowercase_				,    segmentation_type="pil"    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   np.zeros((20, 50)    )
													_snake_case						:					Tuple    =   1
													_snake_case						:					List[Any]    =   1
													_snake_case						:					Union[str, Any]    =   1

													_snake_case						:					List[str]    =   binary_mask_to_rle(lowercase_    )
													self.assertEqual(len(lowercase_    )				,    4    )
													self.assertEqual(rle[0]				,    21    )
													self.assertEqual(rle[1]				,    45    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.image_processing_class(
													    num_labels=self.image_processing_tester.num_classes				,    max_seq_length=77				,    task_seq_length=77				,    class_info_file="ade20k_panoptic.json"				,    num_text=self.image_processing_tester.num_text				,    repo_path="shi-labs/oneformer_demo"				,    )
													_snake_case						:					Tuple    =   self.image_processing_tester.get_fake_oneformer_outputs()

													_snake_case						:					Optional[int]    =   fature_extractor.post_process_semantic_segmentation(lowercase_    )

													self.assertEqual(len(lowercase_    )				,    self.image_processing_tester.batch_size    )
													self.assertEqual(
													    segmentation[0].shape				,    (
													        self.image_processing_tester.height,
													        self.image_processing_tester.width,
													    )				,    )

													_snake_case						:					str    =   [(1, 4) for i in range(self.image_processing_tester.batch_size    )]
													_snake_case						:					Dict    =   fature_extractor.post_process_semantic_segmentation(lowercase_				,    target_sizes=lowercase_    )

													self.assertEqual(segmentation[0].shape				,    target_sizes[0]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.image_processing_class(
													    num_labels=self.image_processing_tester.num_classes				,    max_seq_length=77				,    task_seq_length=77				,    class_info_file="ade20k_panoptic.json"				,    num_text=self.image_processing_tester.num_text				,    repo_path="shi-labs/oneformer_demo"				,    )
													_snake_case						:					Tuple    =   self.image_processing_tester.get_fake_oneformer_outputs()
													_snake_case						:					Optional[int]    =   image_processor.post_process_instance_segmentation(lowercase_				,    threshold=0    )

													self.assertTrue(len(lowercase_    ) == self.image_processing_tester.batch_size    )
													for el in segmentation:
																			self.assertTrue("segmentation" in el    )
																			self.assertTrue("segments_info" in el    )
																			self.assertEqual(type(el["segments_info"]    )				,    lowercase_    )
																			self.assertEqual(
																			    el["segmentation"].shape				,    (self.image_processing_tester.height, self.image_processing_tester.width)    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.image_processing_class(
													    num_labels=self.image_processing_tester.num_classes				,    max_seq_length=77				,    task_seq_length=77				,    class_info_file="ade20k_panoptic.json"				,    num_text=self.image_processing_tester.num_text				,    repo_path="shi-labs/oneformer_demo"				,    )
													_snake_case						:					List[Any]    =   self.image_processing_tester.get_fake_oneformer_outputs()
													_snake_case						:					Any    =   image_processor.post_process_panoptic_segmentation(lowercase_				,    threshold=0    )

													self.assertTrue(len(lowercase_    ) == self.image_processing_tester.batch_size    )
													for el in segmentation:
																			self.assertTrue("segmentation" in el    )
																			self.assertTrue("segments_info" in el    )
																			self.assertEqual(type(el["segments_info"]    )				,    lowercase_    )
																			self.assertEqual(
																			    el["segmentation"].shape				,    (self.image_processing_tester.height, self.image_processing_tester.width)    )
from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase=None ,      __lowercase=None						)	->	Tuple:

						'''simple docstring'''



						if attention_mask is None:
												_snake_case						:					List[Any]    =   tf.cast(tf.math.not_equal(__lowercase ,      config.pad_token_id						) ,      tf.inta						)
						return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class        lowercase_   :
							_lowerCamelCase						=		OPTConfig
							_lowerCamelCase						=		{}
							_lowerCamelCase						=		'gelu'



							def __init__(						self				,    lowercase_				,    lowercase_=13				,    lowercase_=7				,    lowercase_=True				,    lowercase_=False				,    lowercase_=99				,    lowercase_=16				,    lowercase_=2				,    lowercase_=4				,    lowercase_=4				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=20				,    lowercase_=2				,    lowercase_=1				,    lowercase_=0				,    lowercase_=16				,    lowercase_=16				,    ):
													_snake_case						:					Dict    =   parent
													_snake_case						:					List[str]    =   batch_size
													_snake_case						:					Optional[Any]    =   seq_length
													_snake_case						:					Dict    =   is_training
													_snake_case						:					List[Any]    =   use_labels
													_snake_case						:					Dict    =   vocab_size
													_snake_case						:					Tuple    =   hidden_size
													_snake_case						:					Optional[int]    =   num_hidden_layers
													_snake_case						:					List[str]    =   num_attention_heads
													_snake_case						:					Tuple    =   intermediate_size
													_snake_case						:					Dict    =   hidden_act
													_snake_case						:					Any    =   hidden_dropout_prob
													_snake_case						:					Optional[int]    =   attention_probs_dropout_prob
													_snake_case						:					Tuple    =   max_position_embeddings
													_snake_case						:					List[Any]    =   eos_token_id
													_snake_case						:					Optional[int]    =   pad_token_id
													_snake_case						:					Dict    =   bos_token_id
													_snake_case						:					List[Any]    =   embed_dim
													_snake_case						:					Optional[int]    =   word_embed_proj_dim
													_snake_case						:					Union[str, Any]    =   False



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   ids_tensor([self.batch_size, self.seq_length - 1]				,    self.vocab_size    )
													_snake_case						:					str    =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )				,    1    )
													_snake_case						:					int    =   tf.concat([input_ids, eos_tensor]				,    axis=1    )

													_snake_case						:					Optional[Any]    =   self.config_cls(
													    vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    ffn_dim=self.intermediate_size				,    dropout=self.hidden_dropout_prob				,    attention_dropout=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    eos_token_id=self.eos_token_id				,    bos_token_id=self.bos_token_id				,    pad_token_id=self.pad_token_id				,    embed_dim=self.embed_dim				,    word_embed_proj_dim=self.word_embed_proj_dim				,    is_encoder_decoder=lowercase_				,    **self.config_updates				,    )
													_snake_case						:					Any    =   prepare_opt_inputs_dict(lowercase_				,    lowercase_    )
													return config, inputs_dict



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Any    =   TFOPTModel(config=lowercase_    )
													_snake_case						:					int    =   inputs_dict["input_ids"]

													_snake_case						:					Optional[int]    =   input_ids[:1, :]
													_snake_case						:					Any    =   inputs_dict["attention_mask"][:1, :]
													_snake_case						:					List[str]    =   1

													# first forward pass
													_snake_case						:					Union[str, Any]    =   model(lowercase_				,    attention_mask=lowercase_				,    use_cache=lowercase_    )

													_snake_case    ,_snake_case						:					str    =   outputs.to_tuple()

													# create hypothetical next token and extent to next_input_ids
													_snake_case						:					Dict    =   ids_tensor((self.batch_size, 3)				,    config.vocab_size    )
													_snake_case						:					Optional[Any]    =   tf.cast(ids_tensor((self.batch_size, 3)				,    2    )				,    tf.inta    )

													# append to next input_ids and
													_snake_case						:					int    =   tf.concat([input_ids, next_tokens]				,    axis=-1    )
													_snake_case						:					int    =   tf.concat([attention_mask, next_attn_mask]				,    axis=-1    )

													_snake_case						:					Optional[Any]    =   model(lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Optional[int]    =   model(lowercase_				,    attention_mask=lowercase_				,    past_key_values=lowercase_    )[0]

													self.parent.assertEqual(next_tokens.shape[1]				,    output_from_past.shape[1]    )

													# select random slice
													_snake_case						:					int    =   int(ids_tensor((1,)				,    output_from_past.shape[-1]    )    )
													_snake_case						:					List[str]    =   output_from_no_past[:, -3:, random_slice_idx]
													_snake_case						:					List[Any]    =   output_from_past[:, :, random_slice_idx]

													# test that outputs are equal for slice
													tf.debugging.assert_near(lowercase_				,    lowercase_				,    rtol=1e-3    )




@require_tf
class        lowercase_   (					__snake_case						,			__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
							_lowerCamelCase						=		(TFOPTForCausalLM,) if is_tf_available() else ()
							_lowerCamelCase						=		(
							    {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
							)
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		10



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   TFOPTModelTester(self    )
													_snake_case						:					Optional[int]    =   ConfigTester(self				,    config_class=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self.config_tester.run_common_tests()



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_decoder_model_past_large_inputs(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					str    =   self.model_tester.prepare_config_and_inputs_for_common()

													def _get_word_embedding_weight(lowercase_				,    lowercase_    ):
																			if hasattr(lowercase_				,    "weight"    ):
																									return embedding_layer.weight
																			else:
																									# Here we build the word embeddings weights if not exists.
																									# And then we retry to get the attribute once built.
																									model.build()
																									if hasattr(lowercase_				,    "weight"    ):
																															return embedding_layer.weight
																									else:
																															return None

													for model_class in self.all_model_classes:
																			for size in [config.vocab_size - 10, config.vocab_size + 10]:
																									# build the embeddings
																									_snake_case						:					Dict    =   model_class(config=lowercase_    )
																									_snake_case						:					Union[str, Any]    =   _get_word_embedding_weight(lowercase_				,    model.get_input_embeddings()    )
																									_snake_case						:					Optional[Any]    =   _get_word_embedding_weight(lowercase_				,    model.get_output_embeddings()    )

																									# reshape the embeddings
																									model.resize_token_embeddings(lowercase_    )
																									_snake_case						:					int    =   _get_word_embedding_weight(lowercase_				,    model.get_input_embeddings()    )
																									_snake_case						:					Tuple    =   _get_word_embedding_weight(lowercase_				,    model.get_output_embeddings()    )

																									# check that the resized embeddings size matches the desired size.
																									_snake_case						:					Any    =   size if size is not None else config.vocab_size

																									self.assertEqual(new_input_embeddings.shape[0]				,    lowercase_    )

																									# check that weights remain the same after resizing
																									_snake_case						:					Dict    =   True
																									for pa, pa in zip(old_input_embeddings.value()				,    new_input_embeddings.value()    ):
																															if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																																					_snake_case						:					Optional[int]    =   False
																									self.assertTrue(lowercase_    )

																									if old_output_embeddings is not None and new_output_embeddings is not None:
																															self.assertEqual(new_output_embeddings.shape[0]				,    lowercase_    )

																															_snake_case						:					Optional[int]    =   True
																															for pa, pa in zip(old_output_embeddings.value()				,    new_output_embeddings.value()    ):
																																					if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																																											_snake_case						:					str    =   False
																															self.assertTrue(lowercase_    )




def  snake_case						(__lowercase						)	->	Dict:

						'''simple docstring'''



						return tf.constant(__lowercase ,      dtype=tf.intaa						)



@require_tf
class        lowercase_   (					unittest.TestCase		):
							_lowerCamelCase						=		99



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   tf.ones((4, 1)				,    dtype=tf.intaa    ) * 2
													_snake_case						:					Optional[int]    =   tf.concat([ids_tensor((4, 6)				,    self.vocab_size - 3    ) + 3, eos_column_vector]				,    axis=1    )
													_snake_case						:					List[Any]    =   input_ids.shape[0]
													_snake_case						:					List[str]    =   OPTConfig(
													    vocab_size=self.vocab_size				,    hidden_size=24				,    num_hidden_layers=2				,    num_attention_heads=2				,    ffn_dim=32				,    max_position_embeddings=48				,    eos_token_id=2				,    pad_token_id=1				,    bos_token_id=0				,    )
													return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class        lowercase_   (					unittest.TestCase		):



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   TFOPTModel.from_pretrained("facebook/opt-350m"    )
													_snake_case						:					Optional[Any]    =   _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    )
													_snake_case						:					Optional[int]    =   tf.not_equal(lowercase_				,    model.config.pad_token_id    )
													with tf.GradientTape():
																			_snake_case						:					List[Any]    =   model(input_ids=lowercase_				,    attention_mask=lowercase_    ).last_hidden_state
													_snake_case						:					List[str]    =   (1, 11, 512)
													self.assertEqual(output.shape				,    lowercase_    )
													_snake_case						:					Optional[int]    =   tf.constant(
													    [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]    )
													self.assertTrue(np.allclose(output[:, :3, :3]				,    lowercase_				,    atol=4e-3    )    )

													_snake_case						:					List[Any]    =   tf.function(lowercase_				,    jit_compile=lowercase_    )
													_snake_case						:					Tuple    =   xla_generate(lowercase_				,    lowercase_    )[0]
													self.assertTrue(np.allclose(output[:, :3, :3]				,    lowercase_				,    atol=4e-2    )    )




@require_tf
@slow
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													super().setUp()
													_snake_case						:					Optional[int]    =   "facebook/opt-350m"



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   TFOPTForCausalLM.from_pretrained(self.path_model    )
													_snake_case						:					Optional[Any]    =   GPTaTokenizer.from_pretrained(self.path_model    )

													_snake_case						:					List[str]    =   [
													    "Today is a beautiful day and I want to",
													    "In the city of",
													    "Paris is the capital of France and",
													    "Computers and mobile phones have taken",
													]
													# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
													_snake_case						:					List[Any]    =   tokenizer(lowercase_				,    return_tensors="tf"				,    padding=lowercase_				,    add_special_tokens=lowercase_    )
													_snake_case						:					List[str]    =   tf.math.reduce_mean(model(inputs.input_ids				,    attention_mask=inputs.attention_mask    )[0]				,    axis=-1    )
													_snake_case						:					Tuple    =   tf.constant(
													    [
													        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
													        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
													        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
													        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
													    ]    )
													self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-4    )    )

													_snake_case						:					List[Any]    =   tf.function(lowercase_				,    jit_compile=lowercase_    )
													_snake_case						:					Tuple    =   tf.math.reduce_mean(xla_generate(inputs.input_ids				,    attention_mask=inputs.attention_mask    )[0]				,    axis=-1    )
													self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-4    )    )




@require_tf
@slow
class        lowercase_   (					unittest.TestCase		):



							@property
							def 	UpperCamelCase	(						self    ):
													return [
													    "Today is a beautiful day and I want",
													    "In the city of",
													    "Paris is the capital of France and",
													    "Computers and mobile phones have taken",
													]



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   "facebook/opt-125m"

													_snake_case						:					int    =   [
													    "Today is a beautiful day and I want to",
													    "In the city of New York, the city",
													    "Paris is the capital of France and the capital",
													    "Computers and mobile phones have taken over the",
													]

													_snake_case						:					str    =   []
													_snake_case						:					Any    =   GPTaTokenizer.from_pretrained(lowercase_    )
													_snake_case						:					List[Any]    =   TFOPTForCausalLM.from_pretrained(lowercase_    )

													for prompt in self.prompts:
																			_snake_case						:					str    =   tokenizer(lowercase_				,    return_tensors="tf"    ).input_ids

																			_snake_case						:					Any    =   model.generate(lowercase_				,    max_length=10    )

																			_snake_case						:					List[str]    =   tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_    )
																			predicted_outputs += generated_string

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   "facebook/opt-350m"

													_snake_case						:					Dict    =   GPTaTokenizer.from_pretrained(lowercase_    )
													_snake_case						:					Dict    =   TFOPTForCausalLM.from_pretrained(lowercase_    )

													_snake_case						:					int    =   "left"

													# use different length sentences to test batching
													_snake_case						:					Union[str, Any]    =   [
													    "Hello, my dog is a little",
													    "Today, I",
													]

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_				,    return_tensors="tf"				,    padding=lowercase_    )
													_snake_case						:					List[Any]    =   inputs["input_ids"]

													_snake_case						:					Union[str, Any]    =   model.generate(input_ids=lowercase_				,    attention_mask=inputs["attention_mask"]    )

													_snake_case						:					int    =   tokenizer(sentences[0]				,    return_tensors="tf"    ).input_ids
													_snake_case						:					List[str]    =   model.generate(input_ids=lowercase_    )

													_snake_case						:					Dict    =   inputs_non_padded.shape[-1] - tf.math.reduce_sum(
													    tf.cast(inputs["attention_mask"][-1]				,    tf.intaa    )    )
													_snake_case						:					int    =   tokenizer(sentences[1]				,    return_tensors="tf"    ).input_ids
													_snake_case						:					int    =   model.generate(input_ids=lowercase_				,    max_length=model.config.max_length - num_paddings    )

													_snake_case						:					Tuple    =   tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_    )
													_snake_case						:					Dict    =   tokenizer.decode(output_non_padded[0]				,    skip_special_tokens=lowercase_    )
													_snake_case						:					Any    =   tokenizer.decode(output_padded[0]				,    skip_special_tokens=lowercase_    )

													_snake_case						:					Optional[Any]    =   [
													    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
													    "Today, I was in the middle of a conversation with a friend about the",
													]
													self.assertListEqual(lowercase_				,    lowercase_    )
													self.assertListEqual(lowercase_				,    [non_padded_sentence, padded_sentence]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   "facebook/opt-350m"

													_snake_case						:					Optional[int]    =   [
													    "Today is a beautiful day and I want to",
													    "In the city of San Francisco, the city",
													    "Paris is the capital of France and the capital",
													    "Computers and mobile phones have taken over the",
													]

													_snake_case						:					str    =   []
													_snake_case						:					str    =   GPTaTokenizer.from_pretrained(lowercase_    )
													_snake_case						:					List[str]    =   TFOPTForCausalLM.from_pretrained(lowercase_    )

													for prompt in self.prompts:
																			_snake_case						:					Dict    =   tokenizer(lowercase_				,    return_tensors="tf"    ).input_ids

																			_snake_case						:					Any    =   model.generate(lowercase_				,    max_length=10    )

																			_snake_case						:					Tuple    =   tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_    )
																			predicted_outputs += generated_string

													self.assertListEqual(lowercase_				,    lowercase_    )