
'''simple docstring'''

import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


UpperCAmelCase__   : Tuple					=		10_00






class    A     :





	def __init__(					self			:       Dict      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Tuple=1.2      ,							__magic_name__			:       Tuple="g"      ,							__magic_name__			:       int=0.5      ,							__magic_name__			:       List[str]="-"      ,							__magic_name__			:       Union[str, Any]="test_out.jpg"      ,							__magic_name__			:       Optional[int]=True      ,							__magic_name__			:       List[Any]=False      ,							__magic_name__			:       List[Any]=None      ,							__magic_name__			:       int=None      ,							__magic_name__			:       Union[str, Any]=0.7      ,							):


				"""simple docstring"""

				if isinstance(__magic_name__      ,							torch.Tensor							):
							lowerCAmelCase__								=							img.numpy().astype("np.uint8"							)
				if isinstance(__magic_name__      ,							__magic_name__							):
							lowerCAmelCase__								=							img_tensorize(__magic_name__							)
				assert isinstance(__magic_name__      ,							np.ndarray							)

				lowerCAmelCase__	,lowerCAmelCase__								=							img.shape[1], img.shape[0]
				lowerCAmelCase__								=							mplfigure.Figure(frameon=__magic_name__							)
				lowerCAmelCase__								=							fig.get_dpi()
				lowerCAmelCase__								=							(width * scale + 1E-2) / dpi
				lowerCAmelCase__								=							(height * scale + 1E-2) / dpi
				fig.set_size_inches(__magic_name__      ,							__magic_name__							)
				lowerCAmelCase__								=							fig.add_axes([0.0, 0.0, 1.0, 1.0]							)
				ax.axis("off"							)
				ax.set_xlim(0.0      ,							__magic_name__							)
				ax.set_ylim(__magic_name__							)

				lowerCAmelCase__								=							saveas
				lowerCAmelCase__								=							rgb
				lowerCAmelCase__								=							pynb
				lowerCAmelCase__								=							img
				lowerCAmelCase__								=							edgecolor
				lowerCAmelCase__								=							0.5
				lowerCAmelCase__								=							linestyle
				lowerCAmelCase__								=							int(np.sqrt(min(__magic_name__      ,							__magic_name__							)							) * scale // 3							)
				lowerCAmelCase__								=							width
				lowerCAmelCase__								=							height
				lowerCAmelCase__								=							scale
				lowerCAmelCase__								=							fig
				lowerCAmelCase__								=							ax
				lowerCAmelCase__								=							pad
				lowerCAmelCase__								=							idaobj
				lowerCAmelCase__								=							idaattr
				lowerCAmelCase__								=							FigureCanvasAgg(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       List[Any]=None							):


				"""simple docstring"""

				if color is None:
							lowerCAmelCase__								=							self.edgecolor
				((lowerCAmelCase__)	,(lowerCAmelCase__)	,(lowerCAmelCase__)	,(lowerCAmelCase__))								=							box
				lowerCAmelCase__								=							xa - xa
				lowerCAmelCase__								=							ya - ya
				self.ax.add_patch(
				    mpl.patches.Rectangle(
				        (xa, ya)      ,							__magic_name__      ,							__magic_name__      ,							fill=__magic_name__      ,							edgecolor=__magic_name__      ,							linewidth=self.font_size // 3      ,							alpha=self.alpha      ,							linestyle=self.linestyle      ,							)							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]      ,							__magic_name__			:       Any      ,							__magic_name__			:       int=None      ,							__magic_name__			:       Optional[int]=None      ,							__magic_name__			:       Union[str, Any]=None      ,							__magic_name__			:       Tuple=None							):


				"""simple docstring"""

				if len(boxes.shape							) > 2:
							lowerCAmelCase__								=							boxes[0]
				if len(obj_ids.shape							) > 1:
							lowerCAmelCase__								=							obj_ids[0]
				if len(obj_scores.shape							) > 1:
							lowerCAmelCase__								=							obj_scores[0]
				if len(attr_ids.shape							) > 1:
							lowerCAmelCase__								=							attr_ids[0]
				if len(attr_scores.shape							) > 1:
							lowerCAmelCase__								=							attr_scores[0]
				if isinstance(__magic_name__      ,							torch.Tensor							):
							lowerCAmelCase__								=							boxes.numpy()
				if isinstance(__magic_name__      ,							__magic_name__							):
							lowerCAmelCase__								=							np.array(__magic_name__							)
				assert isinstance(__magic_name__      ,							np.ndarray							)
				lowerCAmelCase__								=							np.prod(boxes[:, 2:] - boxes[:, :2]      ,							axis=1							)
				lowerCAmelCase__								=							np.argsort(-areas							).tolist()
				lowerCAmelCase__								=							boxes[sorted_idxs] if boxes is not None else None
				lowerCAmelCase__								=							obj_ids[sorted_idxs] if obj_ids is not None else None
				lowerCAmelCase__								=							obj_scores[sorted_idxs] if obj_scores is not None else None
				lowerCAmelCase__								=							attr_ids[sorted_idxs] if attr_ids is not None else None
				lowerCAmelCase__								=							attr_scores[sorted_idxs] if attr_scores is not None else None

				lowerCAmelCase__								=							[self._random_color(maximum=1							) for _ in range(len(__magic_name__							)							)]
				lowerCAmelCase__								=							[assigned_colors[idx] for idx in sorted_idxs]
				if obj_ids is not None:
							lowerCAmelCase__								=							self._create_text_labels_attr(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__							)
							for i in range(len(__magic_name__							)							):
										lowerCAmelCase__								=							assigned_colors[i]
										self.add_box(boxes[i]      ,							__magic_name__							)
										self.draw_labels(labels[i]      ,							boxes[i]      ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       str      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__								=							box
				lowerCAmelCase__								=							(xa, ya)
				lowerCAmelCase__								=							(ya - ya) * (xa - xa)
				lowerCAmelCase__								=							_SMALL_OBJ * self.scale
				if instance_area < small or ya - ya < 40 * self.scale:
							if ya >= self.height - 5:
										lowerCAmelCase__								=							(xa, ya)
							else:
										lowerCAmelCase__								=							(xa, ya)

				lowerCAmelCase__								=							(ya - ya) / np.sqrt(self.height * self.width							)
				lowerCAmelCase__								=							self._change_color_brightness(__magic_name__      ,							brightness_factor=0.7							)
				lowerCAmelCase__								=							np.clip((height_ratio - 0.02) / 0.08 + 1      ,							1.2      ,							2							)
				font_size *= 0.75 * self.font_size

				self.draw_text(
				    text=__magic_name__      ,							position=__magic_name__      ,							color=__magic_name__      ,							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       str="g"      ,							__magic_name__			:       Optional[int]="left"      ,							):


				"""simple docstring"""

				lowerCAmelCase__								=							0
				lowerCAmelCase__								=							self.font_size
				lowerCAmelCase__								=							np.maximum(list(mplc.to_rgb(__magic_name__							)							)      ,							0.2							)
				lowerCAmelCase__								=							max(0.8      ,							np.max(__magic_name__							)							)
				lowerCAmelCase__								=							{
				    "facecolor": "black",
				    "alpha": self.alpha,
				    "pad": self.pad,
				    "edgecolor": "none",
				}
				lowerCAmelCase__	,lowerCAmelCase__								=							position
				self.ax.text(
				    __magic_name__      ,							__magic_name__      ,							__magic_name__      ,							size=font_size * self.scale      ,							family="sans-serif"      ,							bbox=__magic_name__      ,							verticalalignment="top"      ,							horizontalalignment=__magic_name__      ,							color=__magic_name__      ,							zorder=10      ,							rotation=__magic_name__      ,							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       Tuple=None							):


				"""simple docstring"""

				if saveas is None:
							lowerCAmelCase__								=							self.saveas
				if saveas.lower().endswith(".jpg"							) or saveas.lower().endswith(".png"							):
							cva.imwrite(
							    __magic_name__      ,							self._get_buffer()[:, :, ::-1]      ,							)
				else:
							self.fig.savefig(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       Dict      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							[self.idaobj[i] for i in classes]
				lowerCAmelCase__								=							[self.idaattr[i] for i in attr_classes]
				lowerCAmelCase__								=							[
				    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
				    for label, score, attr, attr_score in zip(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__							)
				]
				return labels





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       Dict      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							[self.idaobj[i] for i in classes]
				if scores is not None:
							if labels is None:
										lowerCAmelCase__								=							["{:.0f}%".format(s * 100							) for s in scores]
							else:
										lowerCAmelCase__								=							["{} {:.0f}%".format(__magic_name__      ,							s * 100							) for li, s in zip(__magic_name__      ,							__magic_name__							)]
				return labels





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Optional[Any]=255							):


				"""simple docstring"""

				lowerCAmelCase__								=							np.random.randint(0      ,							len(_COLORS							)							)
				lowerCAmelCase__								=							_COLORS[idx] * maximum
				if not self.rgb:
							lowerCAmelCase__								=							ret[::-1]
				return ret





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				if not self.pynb:
							lowerCAmelCase__	,lowerCAmelCase__								=							self.canvas.print_to_buffer()
							if (width, height) != (self.width, self.height):
										lowerCAmelCase__								=							cva.resize(self.img      ,							(width, height)							)
							else:
										lowerCAmelCase__								=							self.img
				else:
							lowerCAmelCase__								=							io.BytesIO()  # works for cairo backend
							self.canvas.print_rgba(__magic_name__							)
							lowerCAmelCase__	,lowerCAmelCase__								=							self.width, self.height
							lowerCAmelCase__								=							buf.getvalue()
							lowerCAmelCase__								=							self.img

				lowerCAmelCase__								=							np.frombuffer(__magic_name__      ,							dtype="uint8"							)
				lowerCAmelCase__								=							buffer.reshape(__magic_name__      ,							__magic_name__      ,							4							)
				lowerCAmelCase__	,lowerCAmelCase__								=							np.split(__magic_name__      ,							[3]      ,							axis=2							)

				try:
							import numexpr as ne  # fuse them with numexpr

							lowerCAmelCase__								=							ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"							)
				except ImportError:
							lowerCAmelCase__								=							alpha.astype("float32"							) / 255.0
							lowerCAmelCase__								=							img * (1 - alpha) + rgb * alpha

				return visualized_image.astype("uint8"							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]      ,							__magic_name__			:       List[Any]      ,							__magic_name__			:       int							):


				"""simple docstring"""

				assert brightness_factor >= -1.0 and brightness_factor <= 1.0
				lowerCAmelCase__								=							mplc.to_rgb(__magic_name__							)
				lowerCAmelCase__								=							colorsys.rgb_to_hls(*mplc.to_rgb(__magic_name__							)							)
				lowerCAmelCase__								=							polygon_color[1] + (brightness_factor * polygon_color[1])
				lowerCAmelCase__								=							0.0 if modified_lightness < 0.0 else modified_lightness
				lowerCAmelCase__								=							1.0 if modified_lightness > 1.0 else modified_lightness
				lowerCAmelCase__								=							colorsys.hls_to_rgb(polygon_color[0]      ,							__magic_name__      ,							polygon_color[2]							)
				return modified_color

UpperCAmelCase__   : Dict					=		(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)







'''simple docstring'''

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TFSharedEmbeddings,
		)

		if _tf_gpu_memory_limit is not None:
				UpperCAmelCase__   : str					=		tf.config.list_physical_devices("GPU")
				for gpu in gpus:
						# Restrict TensorFlow to only allocate x GB of memory on the GPUs
						try:
								tf.config.set_logical_device_configuration(
								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
								)
								UpperCAmelCase__   : Optional[Any]					=		tf.config.list_logical_devices("GPU")
								print("Logical GPUs", logical_gpus)
						except RuntimeError as e:
								# Virtual devices must be set before GPUs have been initialized
								print(e)






@require_tf
class    A     :
	snake_case__      :Optional[int]        =     None
	snake_case__      :Optional[Any]        =     ()
	snake_case__      :Union[str, Any]        =     ()
	snake_case__      :Optional[int]        =     True
	snake_case__      :Any        =     True
	snake_case__      :Union[str, Any]        =     True
	snake_case__      :str        =     False





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       List[str]=False							):


				"""simple docstring"""

				lowerCAmelCase__								=							copy.deepcopy(__magic_name__							)

				if model_class in get_values(__magic_name__							):
							lowerCAmelCase__								=							{
							    k: tf.tile(tf.expand_dims(__magic_name__      ,							1							)      ,							(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
							    if isinstance(__magic_name__      ,							tf.Tensor							) and v.ndim > 0
							    else v
							    for k, v in inputs_dict.items()
							}

				if return_labels:
							if model_class in get_values(__magic_name__							):
										lowerCAmelCase__								=							tf.ones(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in get_values(__magic_name__							):
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in [
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							]:
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in get_values(__magic_name__							):
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in [
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							]:
										lowerCAmelCase__								=							tf.zeros(
										    (self.model_tester.batch_size, self.model_tester.seq_length)      ,							dtype=tf.intaa							)
				return inputs_dict





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
							lowerCAmelCase__								=							model_class(__magic_name__							)

							@tf.function
							def run_in_graph_mode():
										return model(__magic_name__							)

							lowerCAmelCase__								=							run_in_graph_mode()
							self.assertIsNotNone(__magic_name__							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
							lowerCAmelCase__								=							model_class(__magic_name__							)

							@tf.function(experimental_compile=__magic_name__							)
							def run_in_graph_mode():
										return model(__magic_name__							)

							lowerCAmelCase__								=							run_in_graph_mode()
							self.assertIsNotNone(__magic_name__							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							model_class(__magic_name__							)
							if getattr(__magic_name__      ,							"hf_compute_loss"      ,							__magic_name__							):
										# Test that model correctly compute the loss with kwargs
										lowerCAmelCase__								=							self._prepare_for_class(inputs_dict.copy()      ,							__magic_name__      ,							return_labels=__magic_name__							)
										# Is there a better way to remove these decoder inputs?
										lowerCAmelCase__								=							{
										    key: val
										    for key, val in prepared_for_class.items()
										    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
										}

										lowerCAmelCase__								=							{
										    "labels",
										    "label",
										    "label_ids",
										    "start_positions",
										    "start_position",
										    "end_positions",
										    "end_position",
										    "next_sentence_label",
										}
										lowerCAmelCase__								=							possible_label_cols.intersection(set(__magic_name__							)							)
										self.assertGreater(len(__magic_name__							)      ,							0      ,							msg="No matching label names found!"							)
										lowerCAmelCase__								=							{key: val for key, val in prepared_for_class.items() if key in label_names}
										lowerCAmelCase__								=							{key: val for key, val in prepared_for_class.items() if key not in label_names}
										self.assertGreater(len(__magic_name__							)      ,							0							)

										# Make sure it works with XLA!
										model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)      ,							jit_compile=__magic_name__							)
										# Make sure the model fits without crashing regardless of where we pass the labels
										lowerCAmelCase__								=							model.fit(
										    __magic_name__      ,							validation_data=__magic_name__      ,							steps_per_epoch=1      ,							validation_steps=1      ,							shuffle=__magic_name__      ,							verbose=0      ,							)
										lowerCAmelCase__								=							history.history["loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)
										lowerCAmelCase__								=							history.history["val_loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)

										# Now test it with separate labels, to make sure that path works in XLA too.
										lowerCAmelCase__								=							model_class(__magic_name__							)
										model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)      ,							jit_compile=__magic_name__							)
										lowerCAmelCase__								=							model.fit(
										    __magic_name__      ,							__magic_name__      ,							validation_data=(inputs_minus_labels, labels)      ,							steps_per_epoch=1      ,							validation_steps=1      ,							shuffle=__magic_name__      ,							verbose=0      ,							)

										lowerCAmelCase__								=							history.history["loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)
										lowerCAmelCase__								=							history.history["val_loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__								=							True
				lowerCAmelCase__								=							True

				if hasattr(__magic_name__      ,							"use_cache"							):
							lowerCAmelCase__								=							True

				lowerCAmelCase__								=							getattr(self.model_tester      ,							"encoder_seq_length"      ,							self.model_tester.seq_length							)
				lowerCAmelCase__								=							getattr(self.model_tester      ,							"key_length"      ,							__magic_name__							)

				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
							lowerCAmelCase__								=							model_class(__magic_name__							)
							model.build()
							lowerCAmelCase__								=							len(model(__magic_name__							)							)

							for key in list(class_inputs_dict.keys()							):
										# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
										if key not in model.input_signature:
													del class_inputs_dict[key]
										# Check it's a tensor, in case the inputs dict has some bools in it too
										elif isinstance(class_inputs_dict[key]      ,							tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
													lowerCAmelCase__								=							tf.cast(class_inputs_dict[key]      ,							tf.intaa							)

							if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
										continue  # Some models have inputs that the preparation functions don't create, we skip those

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__magic_name__      ,							saved_model=__magic_name__							)
										lowerCAmelCase__								=							os.path.join(__magic_name__      ,							"saved_model"      ,							"1"							)
										lowerCAmelCase__								=							tf.keras.models.load_model(__magic_name__							)
										lowerCAmelCase__								=							model(__magic_name__							)

										if self.is_encoder_decoder:
													lowerCAmelCase__								=							outputs["encoder_hidden_states"]
													lowerCAmelCase__								=							outputs["encoder_attentions"]
										else:
													lowerCAmelCase__								=							outputs["hidden_states"]
													lowerCAmelCase__								=							outputs["attentions"]

										self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)

										lowerCAmelCase__								=							getattr(
										    self.model_tester      ,							"expected_num_hidden_layers"      ,							self.model_tester.num_hidden_layers + 1							)

										self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)
										self.assertListEqual(
										    list(output_hidden_states[0].shape[-2:]							)      ,							[self.model_tester.seq_length, self.model_tester.hidden_size]      ,							)

										self.assertEqual(len(__magic_name__							)      ,							self.model_tester.num_hidden_layers							)
										self.assertListEqual(
										    list(output_attentions[0].shape[-3:]							)      ,							[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				tf.keras.mixed_precision.set_global_policy("mixed_float16"							)

				# try/finally block to ensure subsequent tests run in float32
				try:
							lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
										lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
										lowerCAmelCase__								=							model_class(__magic_name__							)
										lowerCAmelCase__								=							model(__magic_name__							)

										self.assertIsNotNone(__magic_name__							)
				finally:
							tf.keras.mixed_precision.set_global_policy("float32"							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				# head_mask and decoder_head_mask has different shapes than other input args
				if "head_mask" in inputs_dict:
							del inputs_dict["head_mask"]
				if "decoder_head_mask" in inputs_dict:
							del inputs_dict["decoder_head_mask"]
				if "cross_attn_head_mask" in inputs_dict:
							del inputs_dict["cross_attn_head_mask"]
				lowerCAmelCase__								=							{
				    module_member
				    for model_class in self.all_model_classes
				    for module in (import_module(model_class.__module__							),)
				    for module_member_name in dir(__magic_name__							)
				    if module_member_name.endswith("MainLayer"							)
				    for module_member in (getattr(__magic_name__      ,							__magic_name__							),)
				    if isinstance(__magic_name__      ,							__magic_name__							)
				    and tf.keras.layers.Layer in module_member.__bases__
				    and getattr(__magic_name__      ,							"_keras_serializable"      ,							__magic_name__							)
				}

				for main_layer_class in tf_main_layer_classes:
							# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
							if "T5" in main_layer_class.__name__:
										# Take the same values than in TFT5ModelTester for this shared layer
										lowerCAmelCase__								=							TFSharedEmbeddings(self.model_tester.vocab_size      ,							self.model_tester.hidden_size      ,							name="shared"							)
										lowerCAmelCase__								=							False
										lowerCAmelCase__								=							main_layer_class(__magic_name__      ,							embed_tokens=__magic_name__							)
							else:
										lowerCAmelCase__								=							main_layer_class(__magic_name__							)

							lowerCAmelCase__								=							{
							    name: tf.keras.Input(tensor.shape[1:]      ,							dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
							}

							if hasattr(self.model_tester      ,							"num_labels"							):
										lowerCAmelCase__								=							self.model_tester.num_labels
							else:
										lowerCAmelCase__								=							2

							lowerCAmelCase__								=							tf.data.Dataset.from_tensor_slices(
							    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

							lowerCAmelCase__								=							main_layer(__magic_name__							)[0]
							lowerCAmelCase__								=							tf.keras.layers.Dense(__magic_name__      ,							activation="softmax"      ,							name="outputs"							)(__magic_name__							)
							lowerCAmelCase__								=							tf.keras.models.Model(inputs=__magic_name__      ,							outputs=[outputs]							)

							model.compile(loss="binary_crossentropy"      ,							optimizer="adam"      ,							metrics=["binary_accuracy"]							)
							model.fit(__magic_name__      ,							epochs=1							)

							with tempfile.TemporaryDirectory() as tmpdirname:
										lowerCAmelCase__								=							os.path.join(__magic_name__      ,							"keras_model.h5"							)
										model.save(__magic_name__							)
										if "T5" in main_layer_class.__name__:
													lowerCAmelCase__								=							tf.keras.models.load_model(
													    __magic_name__      ,							custom_objects={
													        main_layer_class.__name__: main_layer_class,
													        "TFSharedEmbeddings": TFSharedEmbeddings,
													    }      ,							)
										else:
													lowerCAmelCase__								=							tf.keras.models.load_model(
													    __magic_name__      ,							custom_objects={main_layer_class.__name__: main_layer_class}							)
										assert isinstance(__magic_name__      ,							tf.keras.Model							)
										model(__magic_name__							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							model_class(__magic_name__							)

							lowerCAmelCase__								=							copy.deepcopy(__magic_name__							)

							if not self.is_encoder_decoder:
										lowerCAmelCase__								=							inputs["input_ids"]
										del inputs["input_ids"]
							else:
										lowerCAmelCase__								=							inputs["input_ids"]
										lowerCAmelCase__								=							inputs.get("decoder_input_ids"      ,							__magic_name__							)
										del inputs["input_ids"]
										inputs.pop("decoder_input_ids"      ,							__magic_name__							)

							if not self.is_encoder_decoder:
										lowerCAmelCase__								=							model.get_input_embeddings()(__magic_name__							)
							else:
										lowerCAmelCase__								=							model.get_input_embeddings()(__magic_name__							)
										lowerCAmelCase__								=							model.get_input_embeddings()(__magic_name__							)

							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)

							@tf.function
							def run_in_graph_mode():
										return model(__magic_name__							)

							lowerCAmelCase__								=							run_in_graph_mode()
							self.assertIsNotNone(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       int      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							[]
				if model.config.bos_token_id is not None:
							special_tokens.append(model.config.bos_token_id							)
				if model.config.pad_token_id is not None:
							special_tokens.append(model.config.pad_token_id							)
				if model.config.eos_token_id is not None:
							special_tokens.append(model.config.eos_token_id							)

				# create random bad tokens that are not special tokens
				lowerCAmelCase__								=							[]
				while len(__magic_name__							) < num_bad_tokens:
							lowerCAmelCase__								=							tf.squeeze(ids_tensor((1, 1)      ,							self.model_tester.vocab_size							)      ,							0							).numpy()[0]
							if token not in special_tokens:
										bad_tokens.append(__magic_name__							)
				return bad_tokens





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

				for token_id in output_ids[0].numpy().tolist():
							self.assertGreaterEqual(__magic_name__      ,							0							)
							self.assertLess(__magic_name__      ,							self.model_tester.vocab_size							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       str      ,							__magic_name__			:       Dict							):


				"""simple docstring"""

				for bad_word_ids in bad_words_ids:
							# for all slices in batch
							for generated_ids_slice in generated_ids:
										# for all word idx
										for i in range(len(__magic_name__							)      ,							len(__magic_name__							)							):
													# if tokens match
													if generated_ids_slice[i - len(__magic_name__							) : i] == bad_word_ids:
																return True
				return False





